CN103501193B - Method for compensating for MU-MAS and dynamically adapting to MU-MAS - Google Patents

Abstract

A method for compensating for MU-MAS and dynamically adapting to MU-MAS is described. The method for compensating for the MU-MAS comprises: transmitting a training signal from each antenna of a base station to one or each of a plurality of wireless client devices, one or each of the client devices analyzing each training signal to generate frequency offset compensation data, and receiving the frequency offset compensation data at the base station; computing MU-MAS precoder weights based on the frequency offset compensation data to pre-cancel the frequency offset at the transmitter; transmitting the training signal and analyzing the training signal on a link between each antenna and one or each of the plurality of wireless client devices to obtain channel characterization data at the base station; and receiving the channel characterization data at the base station; computing a plurality of MU-MAS precoder weights based on the channel characterization data to pre-cancel frequency and phase offset and/or inter-user interference; precoding data using the MU-MAS precoder weights to generate precoded data signals; and transmitting the precoded data signals through the antenna to the each respective client device.

Description

Method for compensating MU-MAS communication and dynamically adapting MU-MAS

The application is a divisional application of an invention patent application with an application date of 20/08/2008, an application number of 200880102933.4, entitled "system and method for distributed input and distributed output wireless communication".

Priority requirement

This application is a continuation of application No.10/902,978 filed on 7/30 of 2004.

Technical Field

The present invention relates generally to the field of communication systems. In particular, the present invention relates to systems and methods for distributed input distributed output wireless communications using space-time coding techniques.

Background

Space-time coding of communication signals

Spatial multiplexing and space-time coding are known to be relatively recent developments in wireless technology. One particular type of space-time coding is known as "multiple-input multiple-output" (MIMO) since several antennas are used at each terminal. By using multiple antennas for transmission and reception, multiple independent radio waves can be transmitted simultaneously within the same frequency range. The following article provides an overview of MIMO.

IEEE member David Gesbert, IEEE member Mansor Shafi, IEEE member Da-shan Shiu, IEEE member Peter J.Smith and IEEE advanced member Ayman Naguib IEEE JOURNAL ON SELECTEDAREAS IN COMMUNICATIONS, VOL.21, NO.3, APRIL 2003: "From the perspective to Practice: overview of MIMO Space-Time Coded Wireless Systems ".

IEEE TRANSCTIONS ON COMMUNICATIONS, VOL.50, NO.12, DECEMBER2000, IEEE Member David Gesbert, IEEE Member Helmut Bolskei, Dhananijay A.Gore, and IEEE Member Arogyaswami J.Paulraj: "Outdoor MIMO Wireless Channels: models and Performance prediction ".

Basically, MIMO technology is based on the application of spatially distributed antennas for generating parallel spatial data streams within a common frequency band. The radio waves propagate in such a way that the individual signals can be separated and demodulated at the receiver, even if they are transmitted within the same frequency band, which can result in a plurality of statistically independent (i.e., effectively separated) communication channels.Thus, compared to standard wireless communication systems that strive to suppress multipath signals (i.e., multiple time-delayed signals at the same frequency with modifications in amplitude and phase), MIMO can rely on uncorrelated or weakly correlated multipath signals to achieve higher throughput and improved signal-to-noise ratio in a given frequency band. Examples show that MIMO technology achieves higher throughput (throughput) at comparable power to signal-to-noise ratio (SNR), whereas conventional non-MIMO systems can only achieve lower throughput. High-traffic company (high-traffic is the largest wireless technology provider) websitehttp:／／www.cdmatech.com／products／what_mimo_ delivers.jsp：This functionality is described on the page entitled "at MIMO releases" above: "MIMO is the multiple antenna technology of multiple antenna transmission of multiple antenna performance of multiple antenna or multiple time of the peak data rate of multiple antenna MHz of multiple antennaapplications QUALCOMM′s fourth generation MIMO technology delivers speedsof315Mbps in36MHz of spectrum or8.8Mbps／MHz.Compare this to the peak capacityof802.11a／g(even with beam-forming or diversity techniques)which deliversonly54Mbps in17MHz of spectrum or3.18Mbps／MHz”。

In general, MIMO systems face the practical limit of less than 10 antennas per device (and hence improvements in the network are less than 10 x throughput) for several reasons:

1. physical limitations: there must be sufficient spacing between the MIMO antennas on a given device so that each receives statistically independent signals. Although improvements in MIMO throughput can still be seen even at fractional-wavelength antenna spacings, efficiency deteriorates rapidly as antennas come closer together, resulting in a lower MIMO throughput multiplier.

See, for example, the following references:

[1]D.-S.Shiu，G.J.Foschini，M.J.Gans，and J.M.Kahn，“Fading correlationand its effect on the eapacity of multielement antenna systems,”IEEETrans.Comm.，vol.48，no.3，pp.502-513，Mar.2000.

[2]V.Pohl，V.Jungnickel，T.Haustein，and C.von Helmolt，“Antenna spacingin MIMO indoor ehannels，”Proc.IEEE Veh.Technol.Conf.，vol.2，pp.749-753，May2002.

[3]M.Stoytchev，H.Safar，A.L.Moustakas，and S.Simon，“Compact antennaarrays for MIMO applications,”Proc.IEEE Antennas and Prop.Symp.，vol.3，pp.708-711，July2001.

[4]A.Forenza and R.W.Heath Jr.，“Impact of antenna geometry on MIMOcommunication in indoor clustered channels，”Proc.IEEE Antennas andProp.Symp.，vol.2，pp.1700-1703，June2004.

furthermore, for small antenna spacings, the coupling effects between each other may degrade the performance of the MIMO system.

See, for example, the following references:

[5]M.J.Fakhereddin and K.R.Dandekar，“Combined effect of polarizationdiversity and mutual coupling on MIMO capacity，”Proc.IEEE Antennas andProp.Symp.，vol.2，pp.495-498，June2003.

[7]P.N.Fletcher，M.Dean，and A.R.Nix，“Mutual coupling in multi-elementarray antennas and its influence on MIMO channel capacity,”IEEE ElectronicsLetters，vol.39，pp.342-344，Feb.2003.

[8]V.Jungnickel，V.Pohl，and C.Von Helmolt，“Capaeity of MIMO systemswith closely spaced antennas,”IEEE Comm.Lett.，vol.7，pp.361-363，Aug.2003.

[10]J.W.Wallace and M.A.Jensen，“Termination-dependent diversityperformance of coupled antennas：Network theory analysis,”IEEE Trans.AntennasPropagat.，vol.52,pp.98-105，Jan.2004.

[13]C.Waldschmidt，S.Schulteis，and W.Wiesbeck，“Complete RF systemmodel for analysis of compact MIMO arrays，”IEEE Trans.on Veh.Technol.，vol.53，pp.579-586，May2004.

[14]M.L.Morris and M.A.Jensen,“Network model for MIMO systems withcoupled antennas and noisy amplifiers,”IEEE Trans.Antennas Propagat.，vol.53,pp.545-552，Jan.2005.

also, when the antennas are crowded together, the antennas must typically be made smaller, which can also affect antenna efficiency.

See, for example, the following references:

[15]H.A.Wheeler，“Small antennas,”IEEE Trans.Antennas Propagat.，vol.AP-23，n.4，pp.462-469，July1975.

[16]J.S.McLean，“Are-examination of the fundamental limits on theradiation Q of electrically small antennas,”IEEE Trans.Antennas Propagat.，vol.44，n.5,pp.672-676，May1996.

finally, with lower frequencies and longer wavelengths, the physical size of the MIMO device becomes unwieldy. An extreme example is in the HF band where the MIMO device antennas must be separated from each other by a distance of 10 meters or more.

2. And (4) noise limitation. Each MIMO receiver/transmitter subsystem generates a certain level of noise. As more and more such subsystems are placed in close proximity to each other, the background noise rises. Meanwhile, when more different signals need to be identified from a multi-antenna MIMO system, lower background noise is required.

3. Cost and power limitations. Although cost and power consumption are not a focus in some MIMO applications, in a typical wireless product, both cost and power consumption are critical constraints when developing a successful product. For each MIMO antenna, a separate RF subsystem is required, including separate analog-to-digital (a/D) and digital-to-analog (D/a) converters. Unlike many aspects of digital systems scaled by moore's law (empirical observations made by gordon's co-founder of intel, the number of transistors on the integrated circuit of a microdevice doubles approximately every 24 months; source: http:// www.intel.com/technology/moore /), such dense analog subsystems typically have physical structure size and power requirements whose size scales linearly with cost and power. Therefore, a multi-antenna MIMO device will become extremely expensive and have a prohibitively high power consumption compared to a single-antenna device.

As a result of the above, most MIMO systems expected today are on the order of 2 to 4 antennas, resulting in a2 to 4 times increase in throughput and some increase in SNR (signal-to-noise ratio) due to the diversity benefits of multi-antenna systems. MIMO systems with 10 antennas have been contemplated (particularly at higher microwave frequencies due to shorter wavelengths and closer antenna spacing), but beyond 10 antennas are highly impractical except for some special and cost-insensitive applications.

Virtual antenna array

One particular application of MIMO type technology is virtual antenna arrays. Such a system is proposed in research documents proposed by the research collaboration group in the field of european science and technology, EURO, Barcelona, Spain, 1 month 15-17 days 2003: center for telecommunications Research, King's College London, UK: "a step towards MIMO: virtual Antenna Arrays ", Mischa Dohler & Hamid Aghvami.

As described in the document, virtual antenna arrays are cooperative wireless device systems (e.g., cellular telephones) that communicate with each other over separate communication channels (provided they are sufficiently close to each other) rather than their base stations over their primary communication channels so as to operate cooperatively (e.g., if they are GSM cellular telephones in the UHF band, this may be the 5GHz Industrial Scientific Medical (ISM) wireless band). Single antenna devices potentially achieve MIMO-like throughput improvement by forwarding information between several devices within mutual relay range (except within the range of the base station) as if they were one device physically having multiple antennas.

In practice, however, such systems are extremely difficult to implement and have limited usefulness. First, it is necessary to keep each device now having a minimum of two different communication paths to achieve throughput improvement, and the availability of its second relay link is often uncertain. Moreover, because they have at a minimum a second communication subsystem and greater computational requirements, the devices are more expensive, physically larger, and consume more power. Furthermore, the system relies on a very complex real-time collaboration of all systems, potentially over multiple communication links. Finally, as the concurrent channel utilization increases (e.g., concurrent telephone call transmissions using MIMO technology), the computational burden on each device also increases (typically exponentially increasing with the linear increase in channel utilization), which is highly impractical for portable devices with tight power and size constraints.

Disclosure of Invention

A system and method for compensating for frequency and phase offsets in a Multiple Antenna System (MAS) with Multiuser (MU) transmissions ("MU-MAS") is described. For example, a method according to one embodiment of the invention includes: transmitting training signals from each antenna of the base station to one or each of a plurality of wireless client devices, the one or each of the client devices analyzing each training signal to generate frequency offset compensation data and receiving the frequency offset compensation data at the base station; calculating MU-MAS precoder weights based on the frequency offset compensation data to pre-cancel frequency offset at a transmitter; precoding a training signal using the MU-MAS precoder weights to generate a precoded training signal for each antenna of a base station; transmitting precoded training signals from each antenna of the base station to each of the plurality of wireless client devices, each client device analyzing each training signal to generate channel characterization data and receiving the channel characterization data at the base station; calculating a plurality of MU-MAS precoding weights based on the channel characteristic data, the MU-MAS precoder weights being calculated for pre-cancelling frequency and phase offsets and/or interference between users; precoding data using the MU-MAS precoder weights to generate precoded data signals for each antenna of the base station; and transmitting the precoded data signals to each client device thereof through each antenna of the base station.

Drawings

A better understanding of the present invention can be obtained from the following detailed description in conjunction with the following drawings, in which:

fig. 1 shows a prior art MIMO system.

Fig. 2 shows an N-antenna base station in communication with a plurality of single-antenna client devices.

Fig. 3 shows a three antenna base station communicating with three single antenna client devices.

Fig. 4 illustrates a training signal technique used in one embodiment of the present invention.

Fig. 5 illustrates channel characteristic data transmitted from a client device to a base station according to one embodiment of the present invention.

FIG. 6 illustrates multiple input distributed output ("MIDO") downstream transmission according to one embodiment of the invention.

Fig. 7 illustrates multiple-input multiple-output ("MIMO") uplink transmission according to one embodiment of the invention.

Fig. 8 shows a base station cycling through different customer groups to allocate throughput in accordance with one embodiment of the present invention.

Fig. 9 illustrates proximity-based customer grouping according to one embodiment of the invention.

Fig. 10 shows an embodiment of the present invention used in an NVIS system.

Fig. 11 shows an embodiment of a DIDO transmitter with an I/Q compensation function.

Fig. 12 shows a DIDO receiver with an I/Q compensation function.

FIG. 13 shows one embodiment of a DIDO-OFDM system with I/Q compensation.

FIG. 14 shows one embodiment of DIDO2 × 2 performance (performance) with and without I/Q compensation.

Fig. 15 shows one embodiment of DIDO2 x 2 performance with and without I/Q compensation.

Fig. 16 shows an embodiment of SER (symbol error rate) for different QAM constellations with and without I/Q compensation.

Fig. 17 shows one embodiment of DIDO2 x 2 performance with and without I/Q compensation at different user equipment locations.

Fig. 18 shows an embodiment of the SER with and without I/Q compensation in the ideal (i.i.d. (independent and co-distributed)) channel.

Fig. 19 shows one embodiment of a transmitter architecture for an adaptive DIDO system.

Fig. 20 shows one embodiment of a receiver architecture for an adaptive DIDO system.

Fig. 21 shows an embodiment of a method of adaptive DIDO-OFDM.

Fig. 22 shows an embodiment of an antenna layout for DIDO measurements.

Fig. 23 shows an embodiment of an array configuration for a different level (order) DIDO system.

Fig. 24 shows the performance of different levels of DIDO systems.

Fig. 25 shows an embodiment of an antenna array for DIDO measurements.

Fig. 26 shows one embodiment of DIDO2 x 2 performance as a function of user equipment location for 4-QAM and 1/2FEC rates.

Fig. 27 shows an embodiment of an antenna layout for DIDO measurements.

Fig. 28 shows how DIDO8 x 8 produces a larger SE than DIDO2 x 2 with low TX power requirements in one embodiment.

Fig. 29 shows one embodiment of DIDO2 x 2 performance with antenna selection.

Fig. 30 shows the average Bit Error Rate (BER) performance in i.i.d. channels for different DIDO precoding schemes.

Fig. 31 shows the signal-to-noise ratio gain of an ASel as a function of the number of additional transmit antennas in the i.i.d. channel.

Fig. 32 shows the SNR threshold as a function of the number of users (M) for Block Diagonalization (BD) and ASel with 1 and 2 outer antennas in the i.i.d. channel.

Fig. 33 shows BER and average SNR per user for two users located in the same angular direction and having different Angular Spread (AS) values.

FIG. 34 shows similar results to FIG. 33, but with a higher angular separation between users.

Fig. 35 plots the AS a function of the SNR threshold for different values of the average angle of arrival (AOA) for a user.

Fig. 36 shows SNR thresholds for an exemplary case of 5 users.

Fig. 37 provides a comparison of the SNR threshold BD with ASel for the 2 user case with 1 and 2 additional antennas.

Fig. 38 shows similar results to fig. 37, but for the case of 5 users.

Fig. 39 shows SNR thresholds for BD schemes with different AS values.

Fig. 40 shows the SNR threshold in a spatially correlated channel with AS =0.1 ° for BD and ASel with 1 and 2 additional antennas.

Fig. 41 shows the calculation of SNR threshold for the other two channel cases of AS =5 °.

Fig. 42 shows the calculation of SNR threshold for the other two channel cases of AS =10 °.

Fig. 43-44 show the SNR threshold AS a function of the number of users (M) and the Angular Spread (AS) of the BD and ASel schemes for 1 and 2 additional antennas, respectively.

Fig. 45 shows a receiver equipped with a frequency offset estimator/compensator;

FIG. 46 shows a DIDO2 × 2 system model, according to one embodiment of the invention.

FIG. 47 illustrates a method according to an embodiment of the invention.

Fig. 48 shows SER results for DIDO2 x 2 systems with and without frequency offset.

Fig. 49 compares SNR threshold performance of different DIDO schemes.

Fig. 50 compares the amount of overhead required for different method embodiments.

FIG. 51 shows that at f_maxSimulation with small frequency offset of =2Hz and no integer offset correction.

Fig. 52 shows the result when the integer offset estimator is turned off.

Detailed Description

In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced without some of these specific details. In addition, well-known structures and devices are shown in block diagram form in order to avoid obscuring the underlying principles of the present invention.

Fig. 1 shows a prior art MIMO system with transmit antennas 104 and receive antennas 105. The throughput rate of such a system may achieve 3 times the throughput rate typically achieved in available channels. There are many different ways to implement the details of such a MIMO system, which are described in published literature on the subject, and the following explanation will describe one such way.

The channel is "characterized" before data is transmitted in the MIMO system of fig. 1. This is achieved by initially transmitting a "training signal" from each transmit antenna 104 to each receiver 105. The training signal is generated by the coding and modulation subsystem 102 and converted to an analog signal by a D/a converter (not shown) and then converted from a baseband signal to an RF signal by each transmitter 103. Each receive antenna 105 coupled to its RF receiver 106 receives and converts each training signal to a baseband signal. The baseband signal is converted to a digital signal by a D/a converter (not shown) and the training signal is then characterized by the signal processing subsystem 107. The characteristics of each signal may include many factors including, for example, phase and amplitude relative to a reference signal internal to the receiver, an absolute reference signal, a relative reference signal, characteristic noise, or other factors. Each signal is typically characterized as a vector of phase and amplitude variations that represent several aspects of the signal as it travels through the channel. For example, in a quadrature amplitude modulated ("QAM") modulated signal, the characteristic may be a vector of phase and amplitude offsets of several multipath images of the signal. As another example, in an orthogonal frequency division multiplexing ("OFDM") modulated signal, it may be a vector of phase and amplitude offsets of several or all individual component signals (sub-signals) in the OFDM spectrum.

After all three transmit antennas 104 have completed their training signal transmissions, the signal processing subsystem 107 will have stored three channel characteristics for each of the three receive antennas 105, which forms a matrix 108 of 3 × 3, denoted as a channel characteristic matrix "H", for each individual matrix element H_i，jIs the channel characteristic of the training signal transmission of transmit antenna 104i received by receive antenna 105 j.

In this regard, the signal processing subsystem 107 inverts the matrix H108 to produce H^-1And awaits transmission of actual data from transmit antenna 104. Note that a variety of existing MIMO techniques described in the available literature may be used to ensure that the H matrix 108 is reversible.

In implementation, the content (payload) of the data to be transmitted is fed to the data input subsystem 100. It is then split into three parts by a splitter (splitter)101 before being sent to the coding and modulation subsystem 102. For example, if the content is ASCII bits of "abcdef," it can be split by the distributor into three sub-contents "ad," be, "and" cf. Each sub-content is then sent to the coding and modulation subsystem 102 separately.

Each sub-content is encoded separately by using a coding system adapted to the statistical independence and error correction capability of each signal. These include, but are not limited to, Reed-Solomon coding, Viterbi coding (Viterbi coding), and enhanced coding (Turbo Codes). Finally, each of the three encoded sub-contents is modulated using a modulation method appropriate to the channel. Exemplary modulation methods are differential phase shift keying modulation ("DPSK"), 64-QAM modulation, and OFDM. It should be noted here that the diversity gain provided by MIMO allows for a higher order modulation constellation that is also feasible in SISO (single input single output) systems using the same channel. Each coded and modulated signal is then transmitted via its own antenna 104, following D/a conversion by a D/a conversion unit (not shown) and RF generation by each transmitter 103.

Each receive antenna 105 will receive a different combination of the three transmitted signals from the antennas 104, assuming that sufficient spatial diversity exists between the transmit and receive antennas. Each RF receiver 106 receives each signal and converts them to a baseband signal, which is then digitized by an a/D converter (not shown). If y is_nIs the signal received by the nth receiving antenna 105, x_nIs the signal transmitted by the nth transmit antenna 104 and N is noise, this can be described by the following equation.

y₁=x₁H₁₁+x₂H₁₂+x₃H₁₃+N

y₂=x₁H₁₂+x₂H₂₂+x₃H₂₃+N

y₃=x₁H₁₃+x₂H₃₂+x₃H₃₃+N

Assuming that this is a system of three equations with three unknowns, then this is the signal processing subsystem 107 deducing x₁、x₂And x₃Linear algebra of (assuming N is at a sufficiently low level to allow decoding of the signal):

x₁=y₁H^-1 ₁₁+y₂H^-1 ₁₂+y₃H^-1 ₁₃

x₂=y₁H^-1 ₂₁+y₂H^-1 ₂₂+y₃H^-1 ₂₃

x₃=y₁H^-1 ₃₁+y₂H^-1 ₃₂+y₃H^-1 ₃₃

once the three transmitted signals x have been derived_nThey are demodulated, decoded and error corrected by the signal processing subsystem 107 to recover the three bit streams originally separated by the distributor 101. These bit streams are combined in a combiner unit 108 and output as a single data stream from a data output 109. Assuming that the robustness of the system can overcome the noise impairments, the data output 109 will produce the same bit stream as the bit stream introduced into the data input 100.

Although the described prior art systems are generally effective up to four antennas, perhaps as many as 10 antennas, it becomes very impractical to have a large number of antennas (e.g., 25, 100 or 1000) for the reasons described in the background section of this disclosure.

Typically, such prior art systems are bi-directional, with the return path being implemented in exactly the same way, but in turn with transmit and receive subsystems on each side of the communication channel.

Fig. 2 shows an embodiment of the invention in which a Base Station (BS)200 is configured with a Wide Area Network (WAN) interface 201 (e.g., through T1 or other high speed connection) and is provided with a number (N) of antennas 202. We use the term "base station" for the moment to refer to any wireless station that communicates wirelessly with a group of customers at fixed locations. Examples of a base station may be an access point in a Wireless Local Area Network (WLAN), or a WAN antenna or antenna array. There are several client devices 203-207, each with a single antenna, which are served by the base station 200 over the air. Although for the purposes of this example it is very easy to think of a base station located in an office environment where it serves user devices 203 and 207 equipped with personal computers of a wireless network, this architecture will be applicable in a large number of application scenarios, both indoors and outdoors where the base station serves wireless customers. For example, the base station may be located on a cell phone tower or on a television broadcast tower. In one embodiment, the base station 200 is placed on the ground for upstream transmission of HF frequencies (e.g., 24MHz frequencies) to reflect signals back from the ionosphere, as described in co-pending application No. 10/817,731 entitled SYSTEM AND METHOD for frequency adjustment NEAR VERTICAL INCIDENTCE SKYWAVE ("NVIS") COMMUNICATION SPACE-TIME CODING, filed 4/20/2004, which is assigned to the assignee of the present application and is incorporated herein by reference.

Certain details associated with the base station 200 and the client devices illustrated are for illustrative purposes only and are not required in accordance with the underlying principles of the invention. For example, the base station may be connected via the WAN interface 201 to a plurality of different types of wide area networks, including private wide area networks, such as those used for digital video transmission. Similarly, the client device may be any kind of wireless data processing and/or communication device including, but not limited to, cellular telephones, personal digital assistants ("PDAs"), receivers, and wireless cameras.

In one embodiment, the n antennas 202 of the base station are spatially separated so that each transmits and receives non-spatially correlated signals as if the base station were a transceiver of prior art MIMO. As described in the background, experiments have been made in which antennas are placed at λ/6 (i.e. 1/6 wavelengths) intervals, which successfully achieve throughput improvement from MIMO, but in general, the further apart these base station antennas are placed, the better the performance of the system, λ/2 being a satisfactory minimum distance. Of course, the underlying principles of the invention are not limited to any particular separation between antennas.

Note that a single base station 200 may well place its antennas at large distances. For example, in the HF spectrum, the antenna may be 10 meters or more (e.g., in the NVIS implementation mentioned above). If 100 such antennas are used, the antenna array of the base station may occupy an area of several square kilometers.

In addition to spatial diversity techniques, one embodiment of the present invention polarizes the signal in order to improve the effective throughput of the system. Increasing channel capacity through polarization is a well-known technique that has been used by satellite television providers for many years. Using polarization techniques, multiple (e.g., three) base station or user antennas can be brought into close proximity to each other and still be non-spatially correlated. While conventional RF systems typically benefit only from two-dimensional (e.g., x and y) diversity of polarization, the architecture described herein may further benefit from three-dimensional (x, y, and z) diversity of polarization.

In addition to spatial and polarization diversity, one embodiment of the present invention employs nearly orthogonal radiation patterns (patterns) to improve link performance via pattern diversity. Pattern diversity can improve the capacity and error rate performance of MIMO systems, and its advantages over other antenna diversity techniques can be found in the following articles:

[17]L.Dong，H.Ling，and R.W.Heath Jr.，“Multiple-input multiple-output

wireless communication systems using antenna pattern diversity,”Proc.IEEE Glob.Telecom.Conf.，vol.1，pp.997-1001，Nov.2002.

[18]R.Vaughan，“Switched parasitic elements for antenna diversity,”IEEE

Trans.Antennas Propagat.，vol.47，pp.399-405，Feb.1999.

[19]P.Mattheijssen，M.H.A.J.Herben，G.Dolmans，and L.Leyten，“Antenna-pattern diversity versus space diversity for use at handhelds，”IEEE Trans.onVeh.Technol.，vol.53，pp.1035-1042，July2004.

[20]C.B.Dietrich Jr，K.Dietze，J.R.Nealy，and W.L.Stutzman，“Spatial，polarization，and pattern diversity for wireless handheld terminals,”Proc.IEEEAntennas and Prop.Symp.，vol.49，pp.1271-1281，Sep.2001.

[21]A.Forenza and R.w.Heath，Jr.，“Benefit of Pattern Diversity Via2-element Array of Circular Patch Antennas in Indoor Clustered MIMO Channels”，IEEE Trans.on Communications，vol.54，no.5，pp.943-954，May2006.

by using directional pattern diversity, multiple base stations or user antennas can be brought into close proximity to each other and nevertheless not spatially correlated.

Figure 3 provides additional details of one embodiment of the base station 200 and client devices 203 and 207 shown in figure 2. For simplicity, the base station 300 is shown with only three antennas 305 and three client devices 306-308. It should be noted, however, that the embodiments of the invention described herein may be implemented with an almost unlimited number of antennas 305 (i.e., limited only by the available space and noise) and client devices 306 and 308.

Fig. 3 is similar to the prior art MIMO architecture shown in fig. 1, where both have three antennas at each end of the communication channel. The significant difference is that in prior art MIMO systems, the three antennas 105 on the right side of fig. 1 are at a fixed distance from each other (e.g., integrated in a single device), and the signals received from each antenna 105 are processed together in the signal processing subsystem 107. In contrast, in FIG. 3, the three antennas 309 on the right side of the figure are each coupled to a different client device 306-308, each of which may be distributed anywhere within the range of the base station 305. In this way, the signal received by each client device may be processed independently of the other two received signals in its encoding, modulation, signal processing subsystem 311. Thus, in contrast to a "MIMO" system with multiple inputs (i.e., antenna 105) and multiple outputs (i.e., antenna 104), fig. 3 shows a multiple input (i.e., antenna 305) distributed output (i.e., antenna 305) system, referred to hereinafter as a "mid" system.

Note that this application uses different terminology than the previous application to better conform to academic and industrial conventions. In the previously referenced co-pending application No. 10/817,731 filed on 20/4/2004 and entitled "SYSTEM AND METHOD FOR ENHANCING NEARVERTICAL INCIDENCE SKYWAVE (" NVIS ") communiation use SPACE-TIME CODING" and application No.10/902,978 filed on 30/7/2004 (which application is a continuation of this application), the meaning of "input" and "output" (in the context of SIMO, MISO, DIMO, and MIDO) is contrary to the meaning of this term in this application. In the previous application, "input" refers to a wireless signal input to a receiving antenna (e.g., antenna 309 in fig. 3), and "output" refers to a wireless signal output by a transmitting antenna (e.g., antenna 305). In academia and the wireless industry, antisense to "input" and "output" are commonly used, where "input" refers to wireless signals input to a channel (i.e., wireless signals transmitted from antenna 305) and "output" refers to wireless signals output from a channel (i.e., wireless signals received by antenna 309). This application uses this term in a reverse of the usage in the application referenced earlier in this paragraph. Accordingly, the following describes the terminology used between several applications in a form that is equivalent to:

the MIDO architecture shown in FIG. 3 achieves a similar capacity boost over SISO systems as MIMO for a given number of transmit antennas. One difference between MIMO and the particular mio embodiment shown in fig. 3, however, is that to achieve the capacity boost provided by multiple base station antennas, each of the MIDO client devices 306 and 308 requires only a single receive antenna, while for MIMO each client device requires at least as many receive antennas as the multiple of the capacity desired to be achieved. Given that there is often a practical limit to how many antennas can be placed at the client device (as explained in the background), this typically limits the MIMO system to between 4 and 10 antennas (4 to 10 times capacity). Since the base station 300 typically serves many customer devices from a fixed and powered location, it is practical to extend it well beyond 10 antennas, and separate the antennas with appropriate distances to achieve spatial diversity. As depicted, each antenna is equipped with a transceiver 304 and a portion of the processing power of the coding, modulation, and signal processing components 303. It is noted that in this embodiment, no matter how much the base station 300 is expanded, each client device 306 and 308 will only require one antenna 309, so the cost for the single-user client device 306 and 308 will be low, and the cost of the base station 300 can be shared among users with large base numbers.

In fig. 4-6, examples of how the MIDO transfer from the base station 300 to the client device 306 and 308 is accomplished are shown.

In one embodiment of the invention, the channel is characterized before MIDO transmission begins. For a MIMO system, each antenna 405 transmits training signals one after another. Fig. 4 shows only the transmission of the first training signal, but for three antennas 405 there are three separate transmissions. Each training signal is generated by the coding, modulation and signal processing subsystem 403, converted to analog by a D/a converter, and transmitted as an RF signal through each RF transceiver 404. Various Coding, modulation, and signal processing techniques may be used including, but not limited to, those described above (e.g., Reed Solomon, Viterbi Coding; QAM, DPSK, QPSK modulation, etc.).

Each client device 406-408 receives the training signal through its antenna 409 and converts the training signal to a baseband signal through transceiver 410. An a/D converter (not shown) converts the signal into a digital signal where it is processed by the encoding, modulation and signal processing subsystem 411. Signal characterization logic 320 then identifies the characteristics of the resulting signal (e.g., identifies the phase and amplitude distortions described above) and stores the characteristics in memory. This feature processing is similar to that of prior art MIMO systems, with a significant difference that each client device calculates only the feature vectors for its one antenna, not n antennas. For example, the training signal in a known pattern initializes the coding, modulation, and signal processing subsystems 420 of the client devices 406 (by transmitting at the time of generation)Receive it from the message of (a), or by other initialization processes). When the antenna 405 transmits the training signal in a known pattern, the coding, modulation, and signal processing subsystem 420 uses correlation to find the strongest training signal reception pattern, which saves the phase and amplitude offsets, which it then subtracts from the received signal. Next, it finds a second strong received pattern associated with the training signal, saves the phase and amplitude offsets, and then it subtracts the second strong pattern from the received signal. This process continues until some fixed number of phase and amplitude offsets (e.g., 8) are preserved or the detectable training signal pattern falls below a given background noise. The vector of the phase/amplitude offset becomes element H of vector 413₁₁. At the same time, the encoding, modulation, and signal processing subsystems of client devices 407 and 408 perform the same processing, producing their vector elements H₂₁And H₃₁。

The memory in which the channel characteristics are stored may be non-volatile memory, such as flash memory, or a hard disk, and/or volatile memory, such as random access memory (e.g., SDRAM, RDAM). In addition, different user devices may use different types of memory to store feature information at the same time (e.g., a PDA may use flash memory, while a laptop may use a hard disk). The underlying principles of the invention are not limited to any particular type of storage mechanism on various client devices or base stations.

As described above, according to the scheme used, since there is only one antenna per client device 406- > 408, only the 1 x 3 rows 413- > 415 of the H matrix are stored per one. Fig. 4 shows the stage after the transmission of the first training signal, where the first column of the 1 x 3 rows 413 and 415 stores the channel characteristic information of the first antenna of the three base station antennas 405. The remaining two columns store the channel characteristics of the next two training signal transmissions from the remaining two base station antennas. Note that for purposes of illustration, the three training signal patterns are transmitted at separate times. If three training signal patterns are selected so as to be uncorrelated with each other, they can be transmitted simultaneously, thus reducing training time.

As shown in fig. 5, after all three pilot transmissions are completed, each client device 506-. For simplicity, only one client device 506 is shown in fig. 5 transmitting its characteristic information. Suitable modulation methods (e.g., DPSK, 64QAM, OFDM) may be used in conjunction with appropriate error correction coding (e.g., Reed Solomon, viterbi coding, and/or Turbo Codes) to ensure that the base station 500 accurately receives the data in rows 513 and 515.

In fig. 5, although all three antennas 505 are shown receiving signals, a single antenna and a single transceiver of the base station 500 is sufficient for receiving each 1 x 3 row 513 and 515 transmissions. However, under certain conditions, using many or all of the antennas 505 and transceivers 504 to receive each transmission (i.e., using prior art single input multiple output ("SIMO") processing techniques in the coding, modulation, and signal processing subsystem 503) may achieve a better signal-to-noise ratio (SNR) than a single antenna 505 and transceiver 504.

When the coding, modulation and signal processing subsystem 503 of the base station 500 receives the 1 x 3 rows 513 and 515 from each client device 507-508, it stores the 1 x 3 rows 513 and 515 into the 3 x 3H matrix 516. For client devices, the base station may use many different storage technologies to store the matrix 516, including, but not limited to, non-volatile mass storage (e.g., hard disk) and/or volatile memory (e.g., SDRAM). Fig. 5 shows the stage where the base station has received and stored a1 x 3 row 513 from the client device 509. When the 1 x 3 rows 514 and 515 are transmitted from the remaining client devices, they may be transmitted and stored in the H matrix 516 until the entire H matrix 516 is stored.

Referring to fig. 6, an embodiment of a MIDO transfer from a base station 600 to a client device 606-608 will now be described. Because each client device 606-608 is a separate device, each device receives a different data transmission. Thus, embodiments of the base station 600 include a WAN interface 601 and coding, modulation, and signalingA router 602 communicatively coupled between the number processing subsystems 603 and receiving a plurality of data streams (formatted as bit streams) from the WAN interface 601, the data streams being divided into separate data streams u corresponding to each of the client devices 606 and 608, respectively₁-u₃And (5) sending. To this end, the router 602 may use various known routing techniques.

As shown in FIG. 6, the three bit streams, u₁-u₃Routed into the coding, modulation and signal processing subsystem 603, coded into statistically independent error correction streams (e.g., using Reed Solomon, viterbi or turbo coding), and modulated with a modulation method appropriate to the channel (e.g., DPSK, 64QAM or OFDM). In addition, the embodiment shown in fig. 6 includes signal precoding logic 630, which signal precoding logic 630 is used to uniquely encode the signals transmitted from each antenna 605 based on the signal characteristics matrix 616. In particular, in this embodiment, precoding logic 630 operates to precode three bit streams u of FIG. 6₁-u₃Multiplied by the inverse of H matrix 616 to generate three new bit streams u'₁-u′₃Rather than routing each coded and modulated bit stream to a separate antenna (as is done in fig. 1). The three precoded bit streams are then converted to analog signals by a D/a converter (not shown) and transmitted as RF signals by transceiver 604 and antenna 605.

The operations performed by the pre-encoding module 630 will be described before explaining how the client device 606-. Similar to the MIMO example of fig. 1 above, the coded and modulated signal for each of the three original bit streams will be denoted as u_n. In the embodiment shown in FIG. 6, each u_iContaining data from three bitstreams routed by the router 602, each such bitstream would become one of three user devices 606 and 608.

However, unlike the MIMO example of FIG. 1, where each x_iWith each antenna 104 transmitting, in the embodiment of the invention shown in FIG. 6, at each customer's installationAntenna 609 receives each u_i(plus any noise N in the channel). To achieve such a result, the output of each of the three antennas 605 (which we denote as v)_i) Is u_iAnd a function that characterizes the H matrix for each client device. In an embodiment, precoding logic 630 in the coding, modulation, and signal processing subsystems calculates each v by performing the following equation_i：

v₁＝u₁H^-1 ₁₁+u₂H^-1 ₁₂+u₃H^-1 ₁₃

v₂＝u₁H^-1 ₂₁+u₂H^-1 ₂₂+u₃H^-1 ₂₃

v₃＝u₁H^-1 ₃₁+u₂H^-1 ₃₂+u₃H^-1 ₃₃

Thus, unlike MIMO, where the channel transforms the signal and then computes each x at the receiver_iWhereas the embodiments of the invention described herein solve for each v at the transmitter before the channel transforms the signal_i. Each antenna 609 receives u that has been received from other antennas 609_n-1U separated from bit stream_i. Each transceiver 610 converts the respective received signal to a baseband signal, which is digitized by an a/D converter (not shown), and x of which is digitized by a respective coding, modulation, and signal processing subsystem 611_iThe bit stream is demodulated and decoded and sent to a data interface 612 used by the client device (e.g., an application on the client device).

The embodiments of the invention described herein may be implemented using a variety of different coding and modulation methods. For example, in an OFDM implementation in which the frequency spectrum is divided into multiple sub-bands, the techniques described herein may be used to characterize each individual sub-band. However, as described above, the underlying principles of the invention are not limited to any particular modulation method.

If the client device 606-608 is a portable data processing device such as a PDA, laptop computer and/or wireless telephone, the channel characteristics may change frequently as the client device may move from one location to another. Thus, in one embodiment of the invention, the channel characteristic matrix 616 of the base station is continuously updated. In one embodiment, the base station 600 periodically (every 250 milliseconds) sends out new training signals to each client device, and each client device continuously sends its channel feature vector back to the base station 600 to ensure that the channel features remain accurate (e.g., if the environment changes or the client device moves to affect the channel). In one embodiment, the training signal is interleaved in the actual data signal sent to each client device. Typically, the throughput of the training signal is much lower than the throughput of the data signal, so this will have little impact on the overall throughput of the system. Accordingly, in this embodiment, channel characterization matrix 616 may be continually updated as the base station actively communicates with each client device, thereby maintaining accurate channel characterization as client devices move from one location to the next, or as the environment changes, thereby affecting the channel.

One embodiment of the present invention shown in fig. 7 uses MIMO technology to improve the uplink communication channel (i.e., the channel from the client device 706-708 to the base station 700). In this embodiment, the uplink channel characterization logic 741 in the base station continually analyzes and characterizes the channel from each client device. In particular, each client device 706-708 transmits a training signal to the base station 700 where the channel characterization logic 741 analyzes to produce an N M channel characterization matrix 741, where N is the number of client devices and M is the number of antennas used by the base station. The embodiment shown in fig. 7 uses three antennas 705 and three client devices 706 and 708 at the base station, which results in a 3 x 3 channel characterization matrix 741 residing at the base station 700. The client device may use the MIMO uplink transmission shown in fig. 7 for transmitting data back to the base station 700 and for transmitting channel characteristic vectors back to the base station 700, as shown in fig. 5. Unlike the embodiment shown in fig. 5, however, in fig. 5, the channel characterization vectors for each client device are transmitted at separate times, whereas the method shown in fig. 7 allows the channel characterization vectors to be transmitted from multiple client devices back to the base station 700 at the same time, thereby greatly reducing the impact of the channel characterization vectors on the backhaul channel throughput.

As described above, the characteristics of each signal may include many factors, including, for example, phase and amplitude relative to a reference signal internal to the receiver, an absolute reference signal, a relative reference signal, characteristic noise, or other factors. For example, in a quadrature amplitude modulation modulated signal, the features may be phase and amplitude offset vectors for several multipath images of the signal. As another example, in an orthogonal frequency division multiplexed modulated signal, the features may be phase and amplitude offset vectors for several or all of the individual component signals in the OFDM spectrum. The training signal may be generated by the coding and modulation subsystem 711 of each client device, converted to an analog signal by a D/a converter (not shown), and then converted from a baseband signal to an RF signal by the transmitter 709 of each client device. In one embodiment, to ensure synchronization of the training signals, the client device transmits the training signals only at the time of the request by the base station (e.g., in the case of round robin). Further, the training signal may be interleaved in the actual data signal transmitted from each client apparatus, or may be transmitted together with the actual data signal. Thus, even if client devices 706-708 are mobile, the upstream channel characterization logic 741 may continuously transmit and analyze the training signal, thereby ensuring that the channel characterization matrix 741 remains updated.

The total channel capacity supported by the foregoing embodiments of the present invention may be defined as min (N, M), where M is the number of client devices and N is the number of base station antennas. That is, the capacity is defined by the number of antennas on the base station side or the client side. As such, one embodiment of the present invention uses a synchronization technique to ensure that no more than min (N, M) antennas are transmitting/receiving at a given time.

In a typical scenario, the number of antennas 705 of the base station 700 will be less than the number of client devices 706 and 708. Fig. 8 shows an exemplary scenario that allows 5 client devices 804-808 to communicate with a base station having three antennas 802. In this embodiment, having determined the total number of client devices 804 and 808 and detected the necessary channel characteristics information (e.g., as described above), the base station 800 selects a first group of three clients 810 with which to communicate (three clients in this example because min (N, M) ═ 3). After communicating with the first group of clients 810 for a specified time, the base station selects another group of three clients 811 with which to communicate. To evenly distribute the communication channels, the base station 800 selects two client devices 807, 808 not included in the first group. In addition, base station 800 selects additional client devices 806 included in the first group since additional antennas are available. In one embodiment, the base station 800 cycles through the customer population in this manner, thereby effectively allocating each customer the same amount of throughput in time. For example, to evenly distribute throughput, the base station may then select any combination of three client devices other than client device 806 (i.e., since client device 806 is used to communicate with the base station in the first two cycles).

In one embodiment, the base station may use the aforementioned techniques to transmit training signals to and receive training signals and signal characteristic data from each client device in addition to standard data communications.

In one embodiment, certain client devices or groups of client devices may be assigned different levels of throughput, e.g., client devices may be prioritized so that it may be ensured that a relatively higher priority client device has more communication cycles (i.e., more throughput) than a lower priority client device. The "priority" of the customer may be selected based on a number of variables including, for example, the customer's subscription fee for wireless bandwidth (e.g., for willing to pay more for extra throughput), and/or the type of data communicated to/from the customer device (e.g., real-time communications, such as telephone voice and video, get a higher priority than non-real-time communications, such as email).

In embodiments where the base station dynamically allocates throughput based on the current load required by each client device. For example, if a client device 804 is live streaming video and the other devices 805 and 808 are performing non-real-time functions such as email, the base station 800 may allocate relatively more throughput to the client 804. It should be noted, however, that the underlying principles of the invention are not limited to any particular throughput allocation technique.

As shown in fig. 9, the two client devices 907,908 may be in close proximity so that the channel characteristics of the clients are virtually the same. As a result, the base station will receive and store virtually equal channel feature vectors for both client devices 907, 908, and therefore this will not produce a spatially distributed signal unique to each client. Accordingly, in one embodiment, the base station will ensure that any two or more client devices that are in close proximity to each other are assigned to different clusters. For example, in fig. 9, the base station 900 communicates first with a first group 910 of client devices 904, 905, and 908 and then with a second group 911 of client devices 905, 906, 907, which ensures that the client devices 907 and 908 are in different groups.

Alternatively, in one embodiment, the base station 900 communicates with the client devices 907 and 908 simultaneously, but multiplexes the communication channels using known channel multiplexing techniques. For example, the base station may use time division multiplexing ("TDM"), frequency division multiplexing ("FDM"), or code division multiple access ("CDMA") techniques to separate the individual, spatially-correlated signals between client devices 907 and 908.

Although each client device described above is equipped with a single antenna, the underlying principles of the invention may be implemented to improve throughput by using client devices with multiple antennas. For example, when used on the wireless system described above, a customer with 2 antennas will achieve a 2-fold throughput boost, a customer with 3 antennas will achieve a 3-fold throughput boost, and so on (i.e., assuming that the spatial and angular separation between the antennas is sufficient). The same general rules apply to the base station when cycling through client devices with multiple antennas. For example, it may treat each antenna as a separate client and distribute the throughput to that "client" as if it were any other client (e.g., ensuring that each client is provided with sufficient or comparable communication cycles).

As described above, one embodiment of the present invention improves signal-to-noise ratio and throughput in near normal incidence sky wave ("NVIS") using the MIDO and/or MIMO signal transmission techniques described above. Referring to fig. 10, in one embodiment of the invention, a first NVIS base station 1001 equipped with a matrix of N antennas 1002 is used to communicate with M client devices 1004. The NVIS antenna 1002 and the antennas 1004 of the various user devices transmit signals upstream at an angle within about 15 degrees of vertical to achieve the desired NVIS and minimize the effects of ground wave interference. In one embodiment, the antenna 1002 and client device 1004 support multiple independent data streams 1006 at specified frequencies in the NVIS spectrum (e.g., at carrier frequencies or frequencies below 23MHz, but typically below 10 MHz) using the various mio and MIMO techniques described above, thereby significantly improving throughput at the specified frequencies (i.e., in proportion to the number of statistically independent data streams).

The NVIS antennas serving a given base station may be at a significant physical distance from each other. Given long wavelengths below 10MHz and the long distance of signal propagation (round trip distance of 300 miles), physical separation of antennas of hundreds of codes, and even miles, can provide benefits in diversity. Under such conditions, the individual antenna signals may be retrieved to a central location, which may be processed using conventional wired or wireless communication systems. Alternatively, each antenna may have local equipment to process its signal and then transmit the data back to the central location using a conventional wired or wireless communication system. In one embodiment of the invention, the NVIS base station 1001 has a broadband link 1015 to the internet 1010 (or other wide area network) to provide remote, high speed, wireless network access to the client device 1003.

In one embodiment, a base station and/or user may utilize polarization/pattern diversity (pattern diversity) techniques to reduce array size and/or user distance while providing diversity and increasing throughput. For example, in a DIMO system with HF transmission, users may be co-located and their signals not correlated due to polarization/pattern diversity. In particular, by using directional pattern diversity, one user may communicate with the base station via ground waves, while other users may communicate with the base station via NVIS.

Additional embodiments of the invention

I、DIDO-OFDM precoding with I/Q imbalance

One embodiment of the present invention employs a system and method for compensating for in-phase-quadrature (I/Q) imbalance in a Distributed Input Distributed Output (DIDO) system with Orthogonal Frequency Division Multiplexing (OFDM). Briefly, according to the present embodiment, the ue estimates the channel and feeds back the information to the base station; a base station calculates a precoding matrix to eliminate interference between carriers and between users caused by I/Q imbalance; and the parallel data streams are sent to a plurality of user equipments via DIDO pre-coding; the user equipment demodulates the data via a Zero Forcing (ZF), Minimum Mean Square Error (MMSE), or Maximum Likelihood (ML) receiver to suppress the residual interference.

As detailed below, some of the salient features of this embodiment of the invention include, but are not limited to:

precoding for cancelling inter-carrier interference (ICI) from mirror tones (mirrortones) in OFDM systems (due to I/Q mismatch);

precoding for canceling inter-user interference and ICI (due to I/Q mismatch) in DIDO-OFDM systems;

techniques for cancelling ICI (due to I/Q mismatch) via a ZF receiver in DIDO-OFDM systems employing Block Diagonalization (BD);

techniques for canceling inter-user interference and ICI (due to I/Q mismatch) via precoding (at the transmitter) and ZF or MMSE filters (at the receiver) in DIDO-OFDM systems;

techniques for canceling inter-user interference and ICI (due to I/Q mismatch) via precoding (at the transmitter) and a non-linear detector (at the receiver) similar to a Maximum Likelihood (ML) detector in a DIDO-OFDM system;

using pre-coding based on channel condition information for cancelling inter-carrier interference (ICI) from image tones in OFDM systems (due to I/Q mismatch);

using pre-coding based on channel condition information for cancelling inter-carrier interference (ICI) from image tones (due to I/Q mismatch) in a DIDO-OFDM system;

using an I/Q mismatch known DIDO precoder (I/Q mismatch aware DIDO precoder) at the base station and an I/Q known DIDO receiver at the user terminal;

using an I/Q mismatch known DIDO precoder (I/Q mismatch aware DIDO precoder) at the base station, an I/Q known DIDO receiver at the user terminal, and an I/Q known channel estimator;

using an I/Q mismatch known DIDO precoder at the base station, an I/Q known DIDO receiver at the user terminal, and an I/Q known channel estimator and an I/Q known DIDO feedback generator (which transmits channel condition information from the user terminal to the station);

using an I/Q mismatch known DIDO precoder at the base station, and using an I/Q known DIDO configurator (which uses I/Q channel information to perform various functions including user selection, adaptive coding and modulation, space-time-frequency mapping, or precoder selection);

using an I/Q-known DIDO receiver that cancels ICI (due to I/Q mismatch) via a ZF receiver in a DIDO-OFDM system employing a Block Diagonalization (BD) precoder;

using an I/Q-known DIDO receiver (which cancels inter-user interference and ICI (due to I/Q mismatch) via precoding in a DIDO-OFDM system (at the transmitter) and a non-linear detector similar to a Maximum Likelihood (ML) detector (at the receiver)); and

an I/Q-known DIDO receiver is used (which cancels ICI (due to I/Q mismatch) via ZF or MMSE filters in DIDO-OFDM systems).

a、Background

The transmit and receive signals of a typical wireless communication system contain in-phase-quadrature (I/Q) components. In practical systems, the in-phase and quadrature components may be distorted due to imperfections in mixing and baseband operation. These distortions (distortions) manifest as I/Q phase, gain and delay mismatches. Phase imbalance is caused by sine and cosine (cosine) in the modulator/demodulator not being correctly orthogonal. Gain imbalance is caused by the different amplification between the in-phase and quadrature components. Due to the difference in delay between the I and Q tracks (rail) in the analog circuit, there may also be additional distortion, referred to as delay imbalance.

In Orthogonal Frequency Division Multiplexing (OFDM) systems, I/Q imbalance can result in inter-carrier imbalance (ICI) from the transmit tones. This effect has been studied in some documents, and methods for compensating for I/Q mismatch in single-input single-output SISO-OFDM systems have been proposed in the following documents: benedetto and P.Mandarii, "Analysis of the effect of the I/Q base and filter mismatch in an OFDMmodem," Wireless personal communications, pp.175-186, 2000; schuchert and r.hasholzner, "a novel I/Q interference compensation scheme for the reception of ofdm signals," IEEE transmission on connector Electronics, aug.2001; m.valkama, m.renfors and v.koivunen, "Advanced methods for I/Q immalance compensation communication receivers," IEEE trans.sig.proc, oct.2001; R.Rao and B.Daneshrad, "Analysis of I/Q mismatch and a cancellation scheme for OFDM systems," ISTmobile Communication Summit, June 2004; tarighat, R.Baghei and A.H.Sayed, "Compensation schemes and Performance analysis of IQ immbalancein OFDMreceivers," Signal Processing, IEEE TransactihS on [ see also Acoustics, Speech, and Signal Processing, IEEE TransactihS on ], vol.53, pp.3257-3268, Aug.2005.

The extension of this work to a multiple-input multiple-output MIMO-OFDM system is shown in the following materials: r.rao and b.daneshrad, "I/Q mismatch cancellation for MIMO OFDM systems," in Personal, inotor and Mobile Radio communications, 2004; PIMRC2004.15th IEEE International symposium on, vol.4, 2004, pp.2710-2714. For Spatial Multiplexing (SM), please see r.m.rao, w.zhu, s.lang, c.oberli, d.brown, j.bhatia, j.f.frigon, j.wang, P; gupta, h.lee, d.n.liu, s.g. Wong, m.fitz, b.daneshrad, and o.takeshita, "Multiantenna tests for research and administration in wireless communications," IEEE communications major, vol.42, No.12, pp.72-81, dec.2004; lang, m.r.rao and b.daneshrad, "design and definition OF a5.25ghz software defined wireless OF DM communication platform," IEEE Communications major, vol.42, No.6, pp.6-12, June 2004; for orthogonal space-time block codes (OSTBC), please see a.tarighat and a.h.sayed, "MIMO OFDM receivers for systems with IQ immbalances," IEEE trans.sig.proc, vol.53, pp.3583-3596, sep.2005.

Unfortunately, no information exists at present that describes how to correct for I/Q gain and phase imbalance errors in Distributed Input Distributed Output (DIDO) communication systems. The embodiments of the invention described below provide a solution to these problems.

The DIDO system includes a base station with distributed antennas that transmits parallel data streams (precoded) to multiple users using the same radio resources (i.e., same slot duration and frequency band) as the conventional SIO system to enhance downlink throughput. Application No.10/902,978 entitled "System and method for Distributed Input-Distributed Wireless Communications" filed by s.g. Perlman and t.cotter at 2004, month 7 and 30 ("prior application"), which is assigned to the assignee of the present application and which is incorporated herein by reference, gives a detailed description of the DIDO System.

There are a number of ways to implement a DIDO precoder. One solution is Block Diagonalization (BD) as described in: speech, a.l. swindlehurst and m.haardt, "Zero for methods for downlink spatial multiplexing in multiplexers MIMO channels," ieee trans.sig.proc, vol.52, pp.461-471, feb.2004; k.k.wong, r.d.mutch, and k.b.letaief, "Ajoint channel differentiation for multiuser MIMO antenna systems," ieee trans.wireless comm., vol.2, pp.773-786, JuI 2003; choi and r.d.mutch, "a transmission processing technique for a multi-user MIMO systems using a composition approach," IEEE trans.wireless comm., vol.3, pp.20-24, Jan 2004; z.shen, j.g.andrews, r.w.heath and b.l.evans, "Low complex user selection algorithm for multiuser MIMO systems with block diagonalization," accepted as published in ieee trans.sig.proc, sep.2005; z.shen, r.chen, j.g.andrews, r.w.heath and b.l.evans, "summary of multi-user MIMO channels with block diagonalization," filed in IEEE trans.wireless comm., oct.2005; R.Chen, R.W. Heath, and J.G. Andrews, "Transmit selection direction for unity encoded multiuser spatial multiplexing systems with linear receivers," was accepted into IEEE Trans, on Signal processing, 2005. The methods for I/Q compensation given in these materials contemplate BD precoders, and the precoders can be extended to any type of DIDO precoding.

In DIDO-OFDM systems, I/Q mismatch can cause two effects: ICI and inter-user interference. The former is due to interference from image tones, similar to that in the SISO-OFDM system. The latter is due to the fact that I/Q mismatch can disrupt the orthogonality of the DIDO precoder, thereby creating interference between users. Both types of interference can be cancelled at the transmitter and receiver by the methods described herein. Three methods for I/Q compensation in DIDO-OFDM systems are described herein and their performance is compared for with and without I/Q mismatch. Results are shown based on simulations and actual measurements performed with DIDO-OFDM prototypes.

This embodiment is an extension of the prior application. In particular, these embodiments relate to the following features of the earlier application:

the system described in the prior application, in which the I/Q orbits are subject to gain and phase imbalances;

calculating, at a transmitter, a DIDO precoder with I/Q compensation using a training signal employed for channel estimation; and

the signal characteristic data takes into account the distortion due to I/Q imbalance and is used at the transmitter to calculate the DIDO precoder according to the method proposed by the present material.

b、Modes for carrying out the invention

First, the mathematical model and architecture of the present invention will be described.

Before presenting the present solution, it is useful to explain the core mathematical concepts. We explain this by assuming I/Q gain and phase imbalance (phase delay is not included in this description, but will be handled automatically in the DIDO-OFDM form of the algorithm). To explain the basic idea, assume that we want to combine two complex numbers s = s_I+js_QAnd h = h_I+jh_QMultiply and let x = h ＊ s.

X_I=S_Ih_I-S_Qh_Q

And

x_Q=S_Ih_Q+S_Qh_I

its matrix form can be rewritten as:

the normalized transformation is marked by the channel matrix (H). Now assume s is the transmitted symbol and h is the channel. The presence of I/Q gain and phase imbalances can be modeled by creating the following non-normalized transforms:

the effect of this skill is to confirm that:

now, rewrite (A):

we make the following definitions:

and

these two matrices have a normalized structure and can therefore be represented in complex form:

h_e=h₁₁+h₂₂+j(h₂₁-h₁₂)

and

h_c=h₁₁-h₂₂+j(h₂₁+h₁₂)

using all of this knowledge, we can derive the effective equation back to have two channels (equivalent channel h)_eAnd a conjugated channel h_c) In scalar form. Therefore, the effective transformation in (5) becomes:

x=h_es+h_cs^*

we refer to the first channel as the equivalent channel and the second channel as the conjugate channel. If there is no I/Q gain and phase imbalance, then the equivalent channel is the channel we want to observe.

Using similar arguments, the input-output relationships of discrete-time MIMON × M systems with I/Q gain and phase imbalance can be shown (by using scalar equivalents to establish their matrix counterparts):

wherein t is a discrete time index, h_e，h_c∈C^M×N，s＝[s₁，...，s_N]，x＝[x₁，...，x_M]And L is the number of channel taps.

In the DIDO-OFDM system, the received signal in the frequency domain is represented. Recall from the signal and system if the following equation is satisfied:

FFT_K{s[t]}＝S[k]FFT_K{s^*[t]}＝S^*[(-k)]＝S^*[K-k]for k＝0，1，...，K-1

With OFDM, for subcarrier k, the equivalent input-output relationship of the MIMO-OFDM system is:

where K is 0, 1.., K-1 is an OFDM subcarrier index, H_eAnd H_cRepresenting the equivalent and conjugate channel matrices, respectively, as defined below:

and

(1) the second contribution in (1) is interference from the image tone. It can be processed by constructing the following iterative (stacked) matrix system (please pay close attention to the conjugate values):

whereinAndvectors in the frequency domain for the transmitted and received symbols, respectively.

By using this method, an effective matrix can be built for DIDO operation. For example, with the DIDO2 × 2 input-output relationship (assuming each user has a single receive antenna), the first user equipment may consider the following equation (in the absence of noise):

and the second user notices the following equation:

wherein,respectively represent a matrix H_eAnd H_cAnd w ∈ C^4x4Is a DIDO precoding matrix. According to (2) and (3), the symbols received by user m can be noticedTwo sources of interference caused by I/Q imbalance (i.e., intercarrier interference from image tones (i.e.,and inter-user interference (i.e.,andp ≠ m)). (3) The DIDO precoding matrix W in (a) is designed to cancel these two interference terms.

There are a number of different implementations of the DIDO precoder that can be used here, depending on the joint detection applied at the receiver. In one embodiment, channel diversity based synthesis may be employed(rather than) Calculated Block Diagonalization (BD) (see, e.g., q.h.spencer, a.l.swindlehurst, and m.haardt, "Zeroforcingmethods for downlink spatial multiplexing in multiuser MIMO channels," ieee trans.sig.proc, vol.52, pp.461-471, feb.2004.k.k; wong, r.d.mutch, and k.b.letaief, "Ajoint channel diagonalization for multiuser MIMO antipna systems," ieee trans.wireless comm., vol.2, pp.773-786, JuI 2003; l, u.choi and r.d.mutch, "algorithm preprocessing technique for multiuser MIMO systems using adaptive approach," IEEE trans.wireless comm., vol.3, pp.20-24, Jan 2004; z.shen, j.g. Andrews, r.w. Heath, and B.L Evans, "Low complex user selection algorithm for multiuser MIMO systems with block segmentation," accepted as published in IEEE trans.sig.proc, sep.2005; z.shen, r.chen, j.g. Andrews, r.w. Heath, and b.levans, "Sum capacity of multi-user MIMO channels with blocking diagnosis," submitted to IEEE trans.wireless comm., oct.2005). Thus, current DIDO systems select precoders such that:

wherein, α_i，jIs constant, andthis approach is very beneficial because by using the precoder, I/Q gain and phase are completely eliminated at the transmitterThe effect of bit imbalance may leave other aspects of the DIDO precoder intact.

The DIDO precoder may also be designed to pre-cancel inter-user interference without pre-canceling ICI due to IQ imbalance. With this approach, the receiver (rather than the transmitter) can compensate for IQ imbalance by employing one of the receive filters described below. Therefore, the precoding design criteria in (4) can be modified to:

and

wherein for the m-th transmitted signal,and is A symbol vector received for user m.

On the receiving side, to transmit symbol vectorsThe estimation is done, user m employs a ZF filter, and the estimated symbol vector is given as:

although ZF filters are most readily understood, the receiver may also apply any number of other filters known to those skilled in the art. One popular choice is an MMSE filter, wherein:

and ρ is the signal-to-noise ratio. Alternatively, the user may perform maximum likelihood symbol detection (or sphere decoder, iterative variation). For example, the first user may use an ML receiver and solve the following optimizations:

where S is the set of all possible vectors S and depends on the constellation size. The ML receiver gives better performance but requires higher complexity at the receiver. A similar set of equations may be applied to the second user.

Note that in (6) and (7)Andis assumed to have zero entries. This assumption is valid only if the transmit precoder is able to completely cancel the inter-user interference for the criteria in (4). In a similar manner, the first and second substrates are,anddiagonal only if the transmit precoder is capable of completely canceling intercarrier interference (i.e., from mirror tones)And (4) matrix.

Fig. 13 shows an embodiment of the architecture of a DIDO-OFDM system with I/Q compensation, which includes an IQ-DIDO precoder 1302 located in a Base Station (BS), a transmission channel 1304, channel estimation logic 1306 located in a user equipment, and a ZF, MMSE, or ML receiver 1308. The channel estimation logic 1306 pairs the channel via training signalsAndestimates are made and fed back to the precoder within the AP. The BS calculates DIDO precoder weights (matrix W) to cancel in advance interference due to I/Q gain and phase imbalance and user interference, and transmits data to the user through the wireless channel 1304. User device m employs ZF, MMSE, or ML receiver 1308 to cancel the residual interference and demodulate the data by utilizing the channel estimates provided by unit 1304.

The following three embodiments may be employed to implement this I/Q compensation algorithm.

Method 1-TX compensation: in this embodiment, the transmitter calculates the precoding matrix according to the criteria in (4). At the receiver, the user equipment employs a "simplified" ZF receiver, whereinAndis assumed to be a diagonal matrix. Therefore, equation (8) reduces to:

method 2-RX compensation: in this embodiment, the transmitter calculates the precoding matrix based on the conventional BD method described in r.chen, r.w.heath, and j.g.andrews, "Transmit selection direction for unity coded multi-user mapping systems with linear receivers," accepted to IEEE Trans, on signal processing, 2005, and does not cancel inter-carrier and inter-user interference for the criteria in (4). With this method, the precoding matrices in (2) and (3) are simplified to:

at the receiver, the user equipment employs the ZF filter as in (8). Note that this method does not cancel interference in advance at the transmitter as in method 1 above. Thus, it cancels the intercarrier interference at the receiver, but not the inter-user interference. Furthermore, feedback is required compared to method 1Andin method 2, the user only needs to feed back the vector for the transmitterTo calculate the DIDO precoder. Therefore, method 2 is particularly suited for DIDO systems with low rate feedback channels. On the other hand, method 2 requires a slightly higher computational complexity at the user equipment to compute the ZF receiver in (8) instead of (11).

Method 3-TX-RX compensation: in one embodiment, the two methods described above are combined. The transmitter calculates the precoding matrix as in (4), and the receiver estimates the transmitted symbols according to (8).

I/Q imbalance (whether phase imbalance, gain imbalance, or delay imbalance) can cause detrimental degradation to signal quality in a wireless communication system. For this reason, conventional circuits are designed to have low imbalance. However, as described above, this problem can be corrected by using digital signal processing in the form of transmit precoding and/or a specific receiver. One embodiment of the present invention comprises a system having a plurality of new functional units, each of which is important for implementing I/Q correction in an OFDM communication system or a DIDO-OFDM communication system.

One embodiment of the present invention uses precoding based on channel condition information to eliminate inter-carrier interference (ICI) from image tones (due to I/Q mismatch) in OFDM systems. As shown in fig. 11, the DIDO transmitter according to this embodiment includes a user selector unit 1102, a plurality of code modulation units 1104, a plurality of mapping units 1106 corresponding thereto, a DIDO IQ-known precoding unit 1108, a plurality of RF transmitter units 1114, a user feedback unit 1112, and a DIDO configurator unit 1110.

The user selector unit 1102 selects a plurality of users U based on the feedback information obtained by the feedback unit 1112₁-U_MAssociated data and provides the information to each of the plurality of code modulation units 1104. Each code modulation unit 1104 codes and demodulates information bits of each user and transmits them to a mapping unit 1106. The mapping unit 1106 maps the input bits to complex symbols and sends the result to the DIDO IQ-aware precoding unit 1108. The DIDO IQ-aware precoding section 1108 calculates DIDO IQ-aware precoding weights using channel condition information acquired from the user by the feedback section 1112, and precodes input symbols acquired from the mapping section 1106. Each precoded data stream is sent by DIDO IQ-aware precoding unit 1108 to OFDM unit 1115, which OFDM unit 1115 calculates the IFFT and adds a cyclic prefix. This information is sent to the D/a unit 1116, which D/a unit 1116 performs digital-to-analog conversion and sends it to the RF unit 1114. The RF unit 1114 upconverts the baseband signal to an intermediate/radio frequency and transmits it to a transmission antenna.

The precoder operates on the conventional and mirror tones together to compensate for I/Q imbalance. Any number of precoder design criteria may be used, including ZF, MMSE, or weighted MMSE design. In a preferred embodiment, the precoder may completely remove ICI due to I/Q mismatch, so that the receiver does not need to perform any additional compensation.

In one embodiment, the precoder uses a block diagonalization criterion to completely cancel inter-user interference without completely canceling each user's I/Q effects (which requires additional receiver processing). In another embodiment, the precoder uses a zero forcing criterion to completely cancel both inter-user interference due to I/Q imbalance and ICI interference. This embodiment may use a conventional DIDO-OFDM processor at the receiver.

One embodiment of the present invention uses precoding based on channel condition information to cancel inter-carrier interference (ICI) from image tones in a DIDO-OFDM system (due to I/Q mismatch), and each user employs an IQ-aware DIDO receiver. As shown in fig. 12, in one embodiment of the invention, the system (including receiver 1202) includes a plurality of RF units 1208, a corresponding plurality of a/D units 1210, an IQ-known channel estimator unit 1204, and a DIDO feedback generator unit 1206.

The RF unit 1208 receives a signal transmitted from the DIDO transmitter unit 1114, down-converts the signal to baseband, and provides the down-converted signal to the a/D unit 1210. The a/D unit 1210 then performs analog-to-digital conversion on the signal and sends it to an OFDM unit 1213. The OFDM unit 1213 removes the cyclic prefix and performs FFT to report the signal to the frequency domain. During the training period, the OFDM unit 1213 sends the output to the IQ-known channel estimation unit 1204, which IQ-known channel estimation unit 1204 calculates the channel estimates in the frequency domain. Alternatively, the channel estimate may be calculated in the time domain. During data periods, OFDM unit 1213 sends an output to IQ-aware receiver unit 1202. The IQ-aware receiver unit computes the IQ receiver and demodulates/decodes the signal to obtain data 1214. The IQ-aware channel estimation unit 1204 sends the channel estimates to a DIDO feedback generator unit 1206, and the feedback generator unit 1204 may quantize the channel estimates and send them back to the transmitter via a feedback control channel 1112.

Receiver 1202 shown in fig. 12 may operate under any number of criteria known to those skilled in the art, including ZF, MMSE, maximum likelihood, or MAP receivers. In a preferred embodiment, the receiver uses an MMSE filter to cancel ICI due to IQ imbalance on mirror tones. In another preferred embodiment, the receiver jointly detects the symbols on the mirror tones using a non-linear detector similar to a maximum likelihood search. This approach has good performance but higher complexity.

In one embodiment, the receiver coefficients are determined using an IQ-known channel estimator 1204 to remove ICI. Therefore, we claim the benefits of a DIDO-OFDM system (using precoding based on channel condition information to cancel inter-carrier interference (ICI) from image tones (due to I/Q mismatch)), an IQ-known DIDO receiver, and an IQ-known channel estimator. The channel estimator may use a conventional training signal or may use a specially constructed training signal transmitted on the in-phase and quadrature signals. Any number of estimation algorithms may be implemented, including least squares, MMSE, or maximum likelihood. The IQ-known channel estimator provides an input to an IQ-known receiver.

Channel condition information may be provided to stations through channel reciprocity or through a feedback channel. One embodiment of the present invention comprises a DIDO-OFDM system having an I/Q-known precoder and an I/Q-known feedback channel for transmitting channel condition information from user terminals to stations. The feedback channel may be a physical or logical control channel. Which may be dedicated or shared in the random access channel. The feedback information may be generated by using a DIDO feedback generator at the user terminal (which we also claim for the benefit of this user terminal). The DIDO feedback generator takes as input the output of the I/Q-aware channel estimator. Which may quantize the channel coefficients or may use any number of finite feedback algorithms known in the art.

The allocation, modulation and coding rate of users, mapping to space-time-frequency coded slots may vary depending on the results of the DIDO feedback generator. Accordingly, an embodiment includes an IQ-aware DIDO configurator that configures a DIDO IQ-aware precoder using IQ-known channel estimates from one or more users, selecting modulation rates, coding rates, subsets of users allowed to transmit, and their mapping to space-time-frequency coded slots.

To evaluate the performance of the proposed compensation method, three DIDO2 × 2 systems will be compared:

1. with I/Q mismatch: transmit through all tones (except DC and edge tones) and not compensate for I/Q mismatch;

2. with I/Q compensation: transmit with all tones and compensate for I/Q mismatch by using "method 1" above;

3. ideally: transmission is made only through an odd number of tones to avoid inter-user interference and inter-carrier (i.e., from mirrored tones) interference due to I/Q mismatch.

After this, the results obtained with measurements using DIDO-OFDM prototype in real propagation scenarios are shown. Fig. 14 shows the 64-QAM constellation obtained from the three systems described above. These constellations are obtained at the same user location and with a fixed average signal-to-noise ratio (-45 dB). The first constellation 1401 is very noisy (interference from image tones due to I/Q imbalance). The second constellation 1402 shows some improvement (due to I/Q compensation). Note that second constellation 1402 is not as clean as the ideal case shown in constellation 1403 (due to the presence of phase noise that may generate inter-carrier interference (ICI)).

FIG. 15 shows the average SER (symbol error rate) 1501 and the actual throughput per user (goodput)1502 for a DIDO2 × 2 system with 64-QAM and 3/4 code rates with and without I/Q mismatch, OFDM bandwidth of 250KHZ with 64 tonesAnd a cyclic prefix length L_cpAnd = 4. Since ideally we only send data through a subset of the tones, the SER and actual throughput performance is evaluated based on the average per tone transmit power (rather than the total transmit power) to ensure a fair comparison between the different cases. Furthermore, in the following results, we use normalized values of the transmit power (in decibels) since our goal here is to compare the relative (rather than absolute) performance of the different schemes. FIG. 15 shows that in the presence of I/Q imbalance, the SER saturates and does not reach the target SER (10)^-2) This is consistent with the results reported in a. taghat anda. h. sayed, "MIMO OFDM receivers for systems with IQ immbalances," ieee trans. sig. proc, vol.53, pp.3583-3596, sep.2005. This saturation effect is due to the fact that the signal power and the interference power (from the image tones) increase with increasing TX power. However, with the proposed I/Q compensation method, interference can be eliminated and better SER performance is obtained. Note that since 64-QAM modulation requires a larger transmit power, the SER may have a slight increase at high SNR due to amplitude saturation effects in the DAC.

Furthermore, it can be observed that the SER performance is very close to the ideal case in the presence of I/Q compensation. Between these two cases, the 2dB gap in TX power is due to phase noise (which may create additional interference between adjacent OFDM tones). Finally, the actual throughput curve 1502 shows that when the I/Q method is applied, it can send twice as much data as the ideal case because we use all data tones instead of only odd tones (for the ideal case).

Fig. 16 illustrates SER performance for different QAM constellations with or without I/Q compensation. We can observe that in this embodiment the proposed method is particularly advantageous for 64-QAM constellations. For 4-QAM and 16-QAM, the I/Q compensation method may yield worse performance than with I/Q mismatch, possibly because the proposed method requires more power for data transmission and interference cancellation from image tones. Furthermore, 4-QAM and 16-QAM are not as affected by I/Q mismatch as 64-QAM due to the large minimum distance between constellation points. See A.Tarighat, R.Baghher, and A.H.Sayed, "Compensation schemes and performance analysis of IQ imbalance in OFDM receivers," Signal Processing, IEEETransaction on [ see also Acoustics, Speech, and Signal Processing, IEEETransaction on vol ], U.S. Pat. No. 53, pp.3257-3268, Aug.2005. Figure 16 can also be observed and concluded by comparing the I/Q mismatch with the ideal case for 4-QAM and 16-QAM. Thus, the additional power required by the DIDO precoder with interference cancellation (from mirror modulation) cannot be guaranteed for the small benefit of I/Q compensation for the case of 4-QAM and 16-QAM. Note that this problem can be solved by adopting the above-described I/Q compensation methods 2 and 3.

Finally, the relative SER performance of the three methods described above was measured under different propagation conditions. SER performance in the presence of I/Q mismatch is also described for reference. Fig. 17 shows the measured SER at two different user locations for a 64-QAM DIDO2 x 2 system with a carrier frequency of 450.5MHZ and a bandwidth of 250 KHz. In location 1, the user is 6 λ away from the BS, which is in a different room and in NLOS (No line of sight) state. In location 2, the user is a distance λ from the BS with LOS (line of sight).

Fig. 17 shows that all three compensation methods perform more strongly than without compensation. It should be noted, however, that method 3 outperforms the other two compensation methods in any channel situation. The relative performance of methods 1 and 2 depends on the propagation conditions. By actually measuring the activity, it can be derived that method 1 outperforms method 2 in general, since it eliminates in advance (at the transmitter) the inter-user interference caused by the I/O imbalance. When the inter-user interference is small, as shown in graph 1702 of fig. 17, method 2 may outperform method 1 because it does not suffer from power loss due to the I/Q compensated precoder.

Up to now, different approaches have been compared by considering only a limited set of propagation scenarios (as shown in fig. 17). After this, the relative performance of these methods was measured in the ideal i.i.d. (independent and equally distributed) channel. The DIDO-OFDM system is simulated with I/Q phase and gain imbalance on the transmit and receive sides. Fig. 18 shows the performance of the proposed method in case of gain balancing only on the transmitter side (i.e. gain 0.8 on the I track of the first transmit chain and gain 1 on the other tracks). It can be seen that method 3 outperforms all other methods. Furthermore, method 1 may perform better than method 2 in the i.i.d. channel than the results obtained at position 2 in graph 1702 of fig. 17.

Therefore, three new methods are presented to compensate for the I/Q imbalance in the DIDO-OFDM system described above, and method 3 outperforms the other proposed compensation methods. In systems with low rate feedback channels, method 2 may be used to reduce the amount of feedback required for DIDO precoding, but may result in poor SER performance.

II、Adaptive DIDO transmission scheme

Another embodiment of a system and method for enhancing performance of a Distributed Input Distributed Output (DIDO) system will be described. The method dynamically allocates radio resources to different user equipments by tracking changing channel conditions to increase throughput while meeting certain target error rates. The user equipment estimates the channel quality and feeds the channel quality back to a Base Station (BS); the base station processes channel quality obtained from the user equipment to select an optimal user equipment set, a DIDO scheme, a modulation/coding scheme (MCS), and an array configuration for a next transmission; the base station transmits parallel data to a plurality of user equipments via precoding, and the signal is demodulated at a receiver.

A system for efficiently allocating resources for DIDO wireless links is also described. The system includes a DIDO base station having a DIDO configurator, the base station processing feedback received from users to select an optimal user set, DIDO scheme, modulation/coding scheme (MCS), and array configuration for next transmission; a receiver in a DIDO system that measures channels and other related parameters to generate a DIDO feedback signal; and a DIDO feedback control channel for transmitting feedback information from the user to the base station.

As detailed below, some salient features of this embodiment of the invention may include, but are not limited to:

techniques for adaptively selecting the number of users, DIDO transmission scheme (i.e., antenna selection or multiplexing), modulation/coding scheme (MCS), and array configuration to minimize SER, or maximize spectral efficiency per user or downlink spectral efficiency based on channel quality information;

a technique for defining a plurality of sets of DIDO transmission modes as a combination of a DIDO scheme and MCS;

techniques for assigning different DIDO modes to different slot, OFDM, and DIDO substreams according to channel conditions;

techniques for dynamically assigning different DIDO modes to different users based on their channel qualities;

a criterion for activating an adaptive DIDO handoff based on link quality metrics computed in the time, frequency and spatial domains;

criteria for activating an adaptive DIDO handover based on a look-up table.

A DIDO system having a DIDO configurator at a base station as shown in fig. 19, which can adaptively select the number of users, DIDO transmission scheme (i.e., antenna selection or multiplexing), modulation/coding scheme (MCS), and array configuration based on channel quality information to minimize SER or maximize spectral efficiency per user or downlink spectral efficiency;

as shown in fig. 20, a DIDO system having a DIDO configurator at a base station and a DIDO feedback generator at each user equipment uses estimated channel conditions and/or other parameters (similar to estimated SNR) at a receiver to generate a feedback message input to the DIDO configurator.

A DIDO system having a DIDO configurator (at the base station), a DIDO feedback generator, and a DIDO feedback control channel (the DIDO feedback channel is used to transmit DIDO-specific configuration information from the user to the base station).

a、Background

In a Multiple Input Multiple Output (MIMO) system, Diversity schemes (e.g., Orthogonal Space Time Block Codes (OSTBC) (see v. tarokh, h. jafarkhani, and a. r. calderbank, "space block codes from transmission signals," IEEE trans. info.th., vol.45, pp.1456-467, jui.1999) or Antenna selection (see r. w. heat jr., s.sandhu, and a. j. paulj, "Antenna selection for transmitting systems with linear applications," IEEE trans. com., wo. 5, pp.142-144, ap. 2001) may be conceived to prevent channel fading, improve link reliability (which may translate into better coverage), on the other hand, Spatial Multiplexing (SM) may be derived from multiple parallel data transmission systems, see "parallel transmission systems, such as coverage, map, 2. 1. and parallel transmission systems, see" network.1. fig. 1. and "parallel transmission systems, such as" network. The theoretical diversity/multiplexing trade-offs of "IEEE trans. info.th", vol.49, No.5, pp.1073-1096, May be achieved in MIMO systems at the same time. A practical implementation is to adaptively switch between diversity and multiplexing transmission schemes by tracking changing channel conditions.

A number of adaptive MIMO transmission techniques have been proposed. The diversity/multiplexing switching method in r.w.heat and a.j.paulraj, "switching between diversity and multiplexing in MIMO systems," IEEE trans.comm., vol.53, No.6, pp.962-968, jun.2005 is designed to improve BER (bit error rate) for fixed rate transmission based on instantaneous channel quality information. Alternatively, statistical channel information may be employed to activate adaptation as in s.catheux, v.erceg, d.gesbert, and r.w.heat.jr., "Adaptive modulation and MIMO coding for branched wireless data networks," IEEE com.mag., vol.2, pp.108-115, June2002 ("catheux"), thereby reducing feedback overhead and the number of control messages. The adaptive transmission algorithm in Catreux is designed to enhance spectral efficiency for a predetermined target error rate in an Orthogonal Frequency Division Multiplexing (OFDM) system based on a channel time/frequency selection indicator. Also for narrowband systems, a similar low feedback adaptive approach is proposed, which exploits channel spatial selectivity to switch between diversity schemes and spatial multiplexing. See, e.g., a.forenza, m.r.mckay, a.pandhariptane, r.w.heat.jr., and i.b.collings, "Adaptive mimo transmission for amplifying the capacity of the spatial correlated channels," accepted to the IEEE Trans, on veh.tech., mar.2007; m.r.mckay, i.b.collins, a.forenza, and r.w.heat.jr., "Multiplexing/beamforming switching for coded mimo in spatial coordinated Rayleigh channels," accepted into IEEE Trans, onveh.tech., dec.2007; forenza, m.r.mckay, r.w.heat.jr., and i.b.collins, "Switching between osten OSTBC and spatial multiplexing with linear receivers across multiplexed MIMO channels," proc.ieee veh.technol.conf., vol.3, pp.1387-1391, May 2006; m.r.mckay, i.b.collins, a.forenza, and r.w.heat jr., "athrosoughput-based adaptive MIMO BICM adaptive for spatial coherent channels," occurs in proc.ieee ICC, June 2006.

In this document, we extend the range of operation presented in the various previous disclosures to DIDO-OFDM systems. See, e.g., r.w.heat and a.j.paulraj, "Switching between diversity and multiplexing in mimo systems," IEEE trans.comm., vol.53, No.6, pp.962-968, jun.2005; s.casteux, v.erceg, d.gesbert, and r.w.heat jr., "Adaptive modulation and MIMO coding for wideband wireless data networks," IEEE comm.mag., vol.2, pp.108-115, June 2002; forenza, m.r.mckay, a.pandhariptane, r.w.heat jr., and i.b.collins, "adaptive mimo transmission for expanding the capacity of coating corrogated channels," IEEE Trans, on veh.tech., vol.56, n.2, pp.619-630, mar.2007; m.r.mckay, i.b.collins, a.forenza, and r.w.heat jr., "Multiplexing/beamforming switching for coded MIMO in spatial coordinated Rayleigh channels," accepted into IEEE Trans, on veh.tech., dec.2007; forenza, m.r.mckay, r.w.heath jr, and i.b.collins, "Switching between ostcon ostco and spatial multiplexing with linear receivers across applied coherent MIMO channels," proc.ieee veh.technol.conf., vol.3, pp.1387-1391, May 2006; m.r.mckay, i.b.collins, a.forenza, and r.w.heat jr., "athrosoughput-based adaptive MIMO BICM adaptive for spatial coherent channels," occurs in proc.ieee ICC, June 2006.

A new adaptive DIDO transmission strategy is described that improves system performance by switching between different numbers of users, different numbers of transmit antennas, and transmission schemes as a means based on channel quality information. Note that m.sharf and b.hastib, "On the capacity of MIMO branched channel with partial information," IEEE Trans. info.th., vol.51, p.506522, feb.2005 and w.choice, a.forza, j.g.andrews, and r.w.heat jr., "orthogonal space division multiple access with beam selection," present in IEEE ns, On Communications, have proposed schemes for adaptively selecting users in multiuser MIMO systems. However, Opportunistic (OSDMA) schemes in these publications are designed to maximize the total capacity by exploiting multi-user diversity, and they can only achieve part of the theoretical capacity of dirty paper (dirty paper) codes, since the interference is not completely cancelled in advance at the transmitter. In the DIDO transmission algorithm described herein, block diagonalization is used to eliminate the inter-user interference in advance. However, the proposed adaptive transmission strategy can be applied to any DIDO system regardless of the type of precoding technique.

This patent application describes extensions to the embodiments of the invention described above and of the prior applications, including but not limited to the following additional features:

1. the training symbols used for channel estimation in the prior application may be employed by the wireless client device to evaluate link quality metrics in the adaptive DIDO scheme.

2. As described in the prior application, the base station receives signal characteristic data from the client device. In the current embodiment, the signal characteristic data is defined as a link quality metric for activating adaptation.

3. The prior application describes a mechanism for selecting the number of antennas and users and defines the throughput allocation. Furthermore, different levels of throughput may be dynamically assigned to different clients as in the prior application. The current embodiment of the invention defines a novel criterion relating to this selection and throughput distribution.

b、Modes for carrying out the invention

The proposed adaptive DIDO technique aims to enhance the spectral efficiency per user or downlink spectral efficiency by dynamically allocating radio resources in time, frequency and space to different users in the system. The overall adaptive criteria is used to improve throughput while meeting a target bit error rate. The adaptive algorithm may also be used to improve the user's link quality (or coverage) via a diversity scheme, depending on the propagation state. Fig. 21 shows a flow chart describing the steps of the adaptive DIDO scheme.

At 2102, a Base Station (BS) collects channel condition information from all users. At 2104, based on the received CSI, the base station computes a link quality metric in time/frequency/space domain. These link quality metrics are used to select users to be served in the next transmission, and the transmission mode for each user, at 2106. Note that the transmission modes include different combinations of modulation/coding and DIDO schemes. Finally, at 2108, the BS transmits the data to the user via DIDO precoding.

At 2102, the base station selects channel condition information (CSI) from all user devices. At 2104, the base station uses the CSI to determine instantaneous or statistical channel quality for all user devices. In a DIDO-OFDM system, channel quality (or link quality metric) may be estimated in the time, frequency, and spatial domains. The base station then uses the link quality metrics to determine the best subset of users and the transmission mode for the current propagation state at 2106. The DIDO transmission mode sets are combined into a combination of a DIDO scheme (i.e., antenna selection or multiplexing), a modulation/coding scheme (MCS), and an array configuration. At 2108, data is transmitted to the user devices using the selected number of users and the transmission mode.

Mode selection may be performed by a look-up table (LUT) that is pre-computed based on bit error rate performance in different propagation environments of the DIDO system. These LUTs map channel quality information to bit error rate performance. To construct the LUT, the error rate performance of the DIDO system in different propagation scenarios can be evaluated based on SNR. From the bit error rate curve, the minimum SNR required to achieve a certain predetermined target bit error rate can be calculated. We define this SNR requirement as the SNR threshold. The SNR threshold is then evaluated at different propagation scenarios and for different DIDO transmission modes and stored in the LUT. For example, the SER results in fig. 24 and 26 may be used to construct a LUT. Thereafter, based on the LUT, the base station can select a transmission mode for the active user that can improve throughput while meeting a predetermined target error rate. Finally, the base station transmits the data to the selected user via DIDO precoding. Note that different DIDO modes may be assigned to different slots, OFDM tones, and DIDO substreams so that adaptation may be performed in the time, frequency, and spatial domains.

Fig. 19-20 illustrate one embodiment of a system employing DIDO adaptation. Several new functional units are introduced to implement the proposed DIDO adaptation algorithm. Specifically, in one embodiment, DIDO configurator 1910 may perform a variety of functions including selecting a number of users, DIDO transmission scheme (i.e., antenna selection and multiplexing), modulation/coding scheme (MCS), and array configuration based on channel quality information 1912 provided by the user equipment.

User selector element 1902 selects a plurality of users U based on feedback information obtained by DIDO configurator 1910₁-U_MAssociated data and provides this information to each of the plurality of coded modulation units 1904. Each code modulation unit 1904 codes and modulates information bits of each user, and sends them to a mapping unit 1906. The mapping unit 1906 maps the input bits to complex symbols and sends them to the precoding unit 1908. Code modulation unit 1904 and mapping unit 1906 both use the information obtained from DIDO configurator unit 1910 to select the type of modulation/coding scheme to be employed for each user. The information may be calculated by configurator unit 1910 by using the channel quality information for each user provided by feedback unit 1912. DIDO precoding section 1908 calculates DIDO precoding weights using the information acquired by DIDO configurator section 1910, and precodes the input symbols acquired from mapping section 1906. Each precoded data stream is sent by DIDO precoding section 1906 to OFDM section 1915, and OFDM section 1915 calculates IFFT and adds a cyclic prefix. This information is sent to D/a unit 1916, D/a unit 1916 performs digital-to-analog conversion, and the resulting analog signal is sent to RF unit 1914. The RF unit 1914 up-converts the baseband signal to an intermediate/radio frequency and transmits it to a transmitting antenna.

RF unit 2008 of each client device receives the signal transmitted from DIDO transmitter unit 1914, down-converts the signal to baseband, and provides the down-converted signal to a/D unit 2010. The a/D unit 2010 then converts the signal from analog to digital and sends it to the OFDM unit 2013. The OFDM unit 2013 removes the cyclic prefix and performs FFT to report the signal to the frequency domain. In the training period, OFDM unit 2013 sends an output to channel estimation unit 2004, and channel estimation unit 2004 calculates a channel estimate in the frequency domain. Alternatively, the channel estimate may be calculated in the time domain. During the data period, OFDM unit 2013 sends an output to receiver unit 2002, which receiver unit 2002 demodulates/decodes the signal to obtain data 2014. The channel estimation unit 2004 sends the channel estimates to the DIDO feedback generator unit 2006, which DIDO feedback generator unit 2006 may quantize the channel estimates and send them back to the transmitter via feedback control channel 1912.

The DIDO configurator 1910 may use information obtained at the base station or, in a preferred embodiment, additionally use the output of the DIDO feedback generator 2006 (see fig. 20) operating at each user equipment. The DIDO feedback generator 2006 uses the estimated channel conditions 2004 and/or other parameters at the receiver similar to the estimated SNR to generate a feedback message to be input to the DIDO configurator 1910. The DIDO feedback generator 2006 may compress, quantize, and/or use some limited feedback strategy known in the art at the receiver.

The DIDO configurator 1910 may use information recovered from DIDO feedback control channel 1912. The DIDO feedback control channel is a logical or physical control channel that can be used to transmit the output of the DIDO feedback generator 2006 from the user to the base station. Control channel 1912 may be implemented in any number of ways known in the art and may be a logical or physical control channel. As a physical channel, it may comprise dedicated time/frequency slots assigned to users. It may also be a random access channel shared by all users. The control channels may be pre-assigned or may be created by stealing bits in a predetermined manner in existing control channels.

In the following discussion, the results obtained by making measurements with the DIDO-OFDM prototype will be described in a real propagation environment. These results demonstrate the feasibility of potential gain in an adaptive DIDO system. First, the performance of different levels of DIDO systems is demonstrated, indicating that the number of antennas/users can be increased to achieve greater downlink throughput. Thereafter, DIDO performance is described in relation to the location of the user equipment, indicating that varying channel conditions need to be tracked. Finally, the performance of the DIDO system employing the diversity technique is described.

i. Performance of different level DIDO systems

The performance of different DIDO systems is evaluated with more and more transmit antennas (N ═ M, where M is the number of users). The performance of the following systems was compared: SISO, DIDO2 × 2, DIDO4 × 4, DIDO6 × 6, and DIDO8 × 8. DIDON × M refers to DIDO with N transmit antennas and M users at the BS.

Fig. 22 shows a transmit/receive antenna layout. The transmit antennas 2201 are arranged in a square array configuration with users located around the transmit array. In fig. 22, T denotes a "transmitting" antenna, and U denotes a "user equipment" 2202.

Different subsets of antennas in the 8-element transmit array are active, depending on the value of N chosen for different measurements. For each DIDO level (N), a subset of antennas is selected that can cover the largest real estate for which the fixed size constraint of the 8-element array is directed. This criterion is expected to enhance spatial diversity for a given value of N.

Fig. 23 shows array configurations for different DIDO levels that fit into the available real estate (i.e., dashed lines). The square dotted box has a size of 24 "x 24", corresponding to λ x λ at a carrier frequency of 450 MHz.

Based on the comments associated with FIG. 23 and with reference to FIG. 22, the performance of each of the following systems will now be defined and compared:

SISO with T1 and U1 (2301)

DIDO 2X 2(2302) with T1, 2 and U1, 2

DIDO 4X 4(2303) with T1, 2, 3, 4 and U1, 2, 3, 4)

DIDO 6X 6(2304) with T1, 2, 3, 4, 5, 6 and U1, 2, 3, 4, 5, 6

DIDO 8X 8(2305) with T1, 2, 3, 4, 5, 6, 7, 8 and U1, 2, 3, 4, 5, 6, 7, 8)

Fig. 24 shows SER, BER, SE (spectral efficiency) and actual throughput performance as a function of Transmit (TX) power in the above DIDO system with 4-QAM and 1/2FEC (forward error correction) rates. The observation shows that SER and BER performance decreases with increasing N values. This effect is caused by two phenomena: for a fixed TX power, the input power to the DIDO array is split between more and more users (or data streams); the spatial diversity decreases as the number of users in the actual DIDO channel increases.

As shown in FIG. 24, to compare the relative performance of different levels of DIDO systems, the target BER is fixed at 10^-4(this value may vary depending on the system), this value roughly corresponds to SER-10^-2. We refer to the TX power value corresponding to the target as the TX Power Threshold (TPT). For any N, if the TX power is below TPT, we assume that it is not possible to transmit at DIDO level N and we need to switch to lower level DIDO. Further, in fig. 24, it can be observed that SE and actual throughput performance can saturate when TX power exceeds TPT for any value of N. Based on these results, the adaptive transmission strategy can be designed to switch between different levels of DIDO to enhance SE or actual throughput for a fixed predetermined target error rate.

ii. Performance in variable user location scenarios

The objective of this experiment was to evaluate DIDO performance for different user locations via simulations in spatially correlated channels. A DIDO2 x 2 system is considered to have 4QAM and 1/2FEC rates. As shown in fig. 25, user 1 is located in the broadside direction of the transmit array, while user 2 changes location from the broadside direction to the endfire direction. The transmit antennas are spaced-2/2 apart and-2.5 lambda apart from the users.

Fig. 26 shows SER and SE results per user for different locations of the user equipment 2. The angle of arrival (AOA) of the user equipment is 0 ° to 90 ° measured from the broadside direction of the transmit array. It is observed that as the angular separation of the user equipment increases, the DIDO performance will improve,since there is more diversity within the DIDO channel. In addition, at target SER ═ 10^-2There is a 10dB gap between AOA2 and AOA2 at 0 ° and 90 °. The results are consistent with the simulation results obtained for the angle extension of 10 ° in fig. 35. Further, note that for the case of AOA 1-AOA 2-0 °, there may be a coupling effect between the two users (due to their antennas being close together), which may make their performance different from the simulation results in fig. 35.

iii preferred case for DIDO8 × 8

Fig. 24 shows that DIDO8 x 8 produces a larger SE than lower-level DIDO, but with higher TX power requirements. The goal of this analysis is to show that there is a situation where DIDO8 x 8 outperforms DIDO2 x 2 not only in terms of peak Spectral Efficiency (SE), but also in terms of TX power demand (or TPT) to achieve the peak SE.

Note that in the i.i.d. (ideal) channel, there is a 6dB gap in TX power between DIDO8 x 8 and DIDO2 x 2 SE. This gap is due to the fact that DIDO8 x 8 splits the TX power between 8 data streams, whereas DIDO2 x 2 splits only between two streams. The results are shown via the simulation in fig. 32.

However, in spatially correlated channels, the TPT is a function of propagation environment characteristics (e.g., array orientation, user position, angular spread). For example, fig. 35 shows a 15dB gap for low angular spread between two different user equipment locations. Similar results are shown in fig. 26 of the present application.

Similar to MIMO systems, DIDO systems suffer performance degradation (due to lack of diversity) when the user is located in the end-fire direction of the TX array. This effect can be observed by measurements with current DIDO prototypes. Thus, one way to show that DIDO8 × 8 outperforms DIDO2 × 2 is to place the user in an endfire direction relative to a DIDO2 × 2 array. In this case, DIDO8 x 8 outperforms DIDO2 x 2 because 8-antenna arrays provide higher diversity.

In this analysis, the following systems are considered:

the system 1: DIDO8 × 8 for 4-QAM (8 parallel data streams sent per slot);

and (3) system 2: DIDO 2X 2 for 64-QAM (one transmission for transmitting users X and Y every 4 slots). For this system, we consider four combinations of TX and RX antenna positions: a) t1, T2U1, 2 (endfire direction); b) t3, T4U3, 4 (endfire direction); c) t5, T6U5, 6 (spaced-30 ° from the endfire direction); d) t7, T8U7, 8(NLOS (no line of sight));

and (3) system: DIDO8 × 8 for 64-QAM; and

and (4) system: MISO 8X 1 for 64-QAM (one transmission to transmitting user X every 8 slots).

For all these cases, an FEC rate of 3/4 was used.

FIG. 27 illustrates the location of a user.

In FIG. 28, the SER results show a-15 dB gap between systems 2a and 2c due to different array orientations and user positions (similar to the simulation results in FIG. 35). The first sub-graph in the second row shows the value of the saturated TX power of the SE curve (i.e., corresponding to BER1 e-4). We observe that system 1 produces a greater SE per user for lower TX power requirements (less than-5 dB) than system 2. Also, the benefit of DIDO8 × 8 over DIDO2 × 2 is more significant for DL (downlink) SE and DL actual throughput due to the multiplexing gain of DIDO8 × 8 over DIDO2 × 2. System 4 has a lower TX power requirement (less than 8dB) than system 1 due to the beamformed array gain (i.e., MRC with MISO8 x 1). But system 4 only produces 1/3 for each user's SE compared to system 1. System 2 has poorer performance than system 1 (i.e., produces a lower SE for larger TX power requirements). Finally, system 3 produces a much larger SE (due to the larger order (large order) modulation) than system 1 for the larger TX power requirements (-15 dB).

From these results, the following conclusions can be drawn:

one channel scenario is identified as DIDO8 x 8 outperforming DIDO2 x 2 (i.e., greater SE is generated for lower TX power requirements);

in this channel scenario, DIDO8 × 8 produces a larger SE and DL SE per user than DIDO2 × 2 and MISO8 × 1; and

the performance of DIDO8 x 8 may be further increased by using higher order modulation (i.e., 64-QAM instead of 4-QAM) at the expense of larger TX power requirements (greater than-15 dB).

DIDO with antenna selection

In the following, we evaluated the benefits of the antenna selection algorithm described in "Transmit selection direction for unified multiple antenna systems with linear receivers" published in 2005 by r.chen, r.w.heat and j.g.andrews on Signal Processing, received by IEEE std. We present the results for one particular DIDO system with FEC rates of two users, 4-QAM and 1/2. The following systems are compared in fig. 27:

DIDO2 × 2 with T1, 2 and U1, 2; and

DIDO3 × 2 with T1, 2, 3 and U1, 2 using antenna selection.

The transmit antenna position and user device position are the same as in fig. 27.

Fig. 29 shows that DIDO3 x 2 with antenna selection can provide-5 dB gain compared to DIDO2 x 2 system (without selection). Note that the channel is almost static (i.e. no doppler effect) so the selection algorithm is applicable to path loss and channel spatial correlation, rather than fast fading. We should see different gains in the case of high doppler effect. Also, in this particular experiment, it was observed that the antenna selection algorithm selects antennas 2 and 3 for transmission.

SNR threshold for LUT

In selection [0171], we claim that mode selection is implemented by LUT. The LUT may be pre-computed by evaluating SNR thresholds to achieve certain predefined target bit error rate performance for DIDO transmission modes in different propagation environments. In the following we provide the performance of DIDO systems with and without antenna selection and variable number of users, which can be used as a guide for constructing LUTs. Although fig. 24, 26, 28, 29 were obtained by actual measurement using the DIDO prototype, the following figures were obtained by simulation. The following BER results assume no FEC.

Fig. 30 shows the average BER performance of different DIDO precoding schemes in separate co-distributed channels. The curve labeled "no selection" refers to the case where BD is used. In the same figure, the performance of antenna selection (ASel) is shown for different numbers of additional antennas (for different numbers of users). It can be seen that as the number of additional antennas increases, the ASel provides better diversity gain (characterized by the slope of the BER curve for the high SNR region), resulting in better coverage. For example, if we fix the target BER to 10^-2(for the actual value of the uncoded system), then the SNR gain provided by the ASel grows with the number of antennas.

Fig. 31 shows the SNR gain of an ASel as a function of the number of additional transmit antennas in the independent co-distributed channels for different target BERs. It can be seen that by adding only 1 or 2 antennas, ASel yields a huge SNR gain compared to BD. In the following section we will fix the target BER to 10 only for the case of 1 or 2 extra antennas^-2The performance of the ASel is evaluated (for an uncoded system).

Fig. 32 shows the SNR threshold as a function of the number of users (M) for BD and ASel with 1 and 2 additional antennas in separate co-distributed channels. We observe that the SNR threshold increases with M due to the larger received SNR requirement for a larger number of users. Note that we assume a fixed total transmit power for any number of users (with different numbers of transmit antennas). Furthermore, fig. 32 shows that the gain due to antenna selection is constant for any number of users in independent co-distributed channels.

Below we show the performance of DIDO systems in spatially correlated channels. We simulated the Channel of each user by the COST-259 spatial Channel model described in "Channel models for link and system channels" published by x.zhuang, f.w. Vook, k.l. Baum, t.a. thomas and m.current on ieee802.16broadband wireless Access work Group, 2004, 9. We generate a single cluster for each user. As a case study we assume a NLOS channel with a Uniform Linear Array (ULA) at the transmitter with a 0.5 λ element spacing. For the case of a2 user system, we simulate the cluster with the average angle of AOA1 and AOA2 reached for the first and second users, respectively. The AOA is measured with respect to the lateral direction of the ULA. When there are more than two users in the system, we generate a signal with a range [ - φ_m，φ_m]Wherein we define a group of uniformly spaced average AOA users

K is the number of users and Δ φ is the angular separation between the users' average AOA. Note the angular range [ - φ_m，φ_m]The center is 0 deg., corresponding to the broadside direction of the ULA. In the following, we use the BD and ASel transmission schemes and different numbers of users to study the BER performance of the DIDO system AS a function of the channel angular distribution (AS) and the angular distance between users.

Fig. 33 shows the BER relative to the average SNR per user for two users with different AS values located in the same angular direction (i.e., AOA1= AOA2=0 ° relative to the broadside direction of the ULA). It can be seen that AS the AS increases, the BER performance improves and approaches the independent co-distributed case. In fact, a higher AS statistically yields less coverage between the characteristic modes of the two users and better performance of the BD precoder.

FIG. 34 shows similar results to FIG. 33, but with a higher angular separation between users. We consider AOA1=0 °, AOA2=90 ° (i.e. 90 ° angular separation). The best performance is achieved with low AS. In fact, for the case of high angular separation, there is less overlap between the user's feature patterns when the angular separation is low. Interestingly, we observe that for the same reasons just mentioned, the BER performance in low AS is better than in independent co-distributed channels.

Next, 10 for different relevant cases^-2We calculate the SNR threshold. Fig. 35 plots SNR threshold AS a function of AS for different values of average AOA for a user. For low user angular separation, reliable transmission with reasonable SNR requirements (i.e., 18dB) is possible only for channels characterized by high AS. On the other hand, when the users are spatially separated, a smaller SNR is required to satisfy the same target BER.

Fig. 36 shows SNR thresholds for the case of 5 users. Generating user average AOAs with different values of angular separation Δ φ according to the definition in (13). We observe that for Δ Φ =0 ° and AS <15 °, BD performance is poor due to the small angular separation between users, and the target BER is not met. For an increased AS, the SNR requirement to meet a fixed target BER decreases. On the other hand, for Δ Φ =30 °, the minimum SNR requirement is obtained at low AS, consistent with the results in fig. 35. AS the AS increases, the SNR threshold saturates to one of the independent co-distributed channels. Note that Δ Φ =30 ° with 5 users corresponds to an AOA range of [ -60 °, 60 ° ], which is typical for a base station in a cellular system with 120 ° sector cells.

Next, we investigated the performance of the ASel transmission scheme in spatially correlated channels. Fig. 37 compares the SNR thresholds for BD and ASel with 1 and 2 additional antennas for the two user case. We consider two different cases of angular separation between users: { AOA1=0 °, AOA2=0 ° } and { AOA1=0 °, AOA2=90 ° }. The curve for the BD scheme (i.e. without antenna selection) is the same as in fig. 35. We observe that ASel yields SNR gains of 8dB and 10dB with 1 and 2 additional antennas, respectively, for high AS. AS decreases, the gain on BD due to ASel becomes smaller due to the reduced number of degrees of freedom in the MIMO broadcast channel. Interestingly, for AS =0 ° (i.e. close to the LOS channel) and case { AOA1=0 °, AOA2=90 ° }, ASel does not provide any gain due to the difference in the spatial domain. Fig. 38 shows similar results to fig. 37, but for the case of 5 users.

We calculated the SNR threshold for BD and ASel transmission schemes as a function of the number of users (M) in the system (assume 10)^-2General target BER). The SNR threshold corresponds to the average SNR such that the total transmit power is constant for any M. We assume that in the azimuth range [ - φ_m，φ_m]=[-60°，60°]The maximum separation between average AOAs per user group within. Then, the angular separation between users is Δ Φ =120 °/(M-1).

Fig. 39 shows SNR thresholds for BD schemes with different AS values. We observe that the lowest SNR requirement is obtained for AS =0.1 ° (i.e. low angular spread) with a relatively small number of users (i.e. K ≦ 20) due to the large angular separation between users. However, for M >50, the SNR requirement is much greater than 40dB due to the very small Δ φ and the inability of BD to do so. Furthermore, for AS >10 °, the SNR threshold remains almost constant for any M, and the DIDO system in spatially correlated channels approaches the performance of independent co-distributed channels.

To reduce the value of the SNR threshold and improve the performance of the DIDO system, we apply the ASel transmission scheme. Fig. 40 shows the SNR threshold in a spatially correlated channel with AS =0.1 ° for BD and ASel with 1 and 2 additional antennas. For reference, we also report curves for the independent co-distribution case shown in fig. 32. It can be seen that for fewer users (i.e., M ≦ 10), antenna selection does not help reduce SNR requirements due to the lack of diversity in the DIDO broadcast channel. As the number of users increases, ASel benefits from multi-user diversity, yielding SNR gain (i.e., 4dB for M = 20). Furthermore, for M ≦ 20, the performance of ASels with 1 or 2 additional antennas in high spatial correlation channels is the same.

We then calculate SNR thresholds for two additional channel scenarios: AS =5 ° in fig. 41 and AS =10 ° in fig. 42. FIG. 41 shows that ASel produces SNR gains that are also used for a relatively small number of users (i.e., M ≦ 10) due to the larger angular spread compared to FIG. 40. AS reported in fig. 42, for AS =10 °, the SNR threshold is further reduced, AS the gain of the ASel becomes higher.

Finally, we summarize the results presented for the relevant channels at present. Fig. 43 and 44 show the SNR threshold AS a function of the number of users (M) and Angular Spread (AS) for the BD and ASel schemes with 1 and 2 additional antennas, respectively. Note that the case of AS =30 ° actually corresponds to an independent co-distributed channel, and we use this value of AS in the figure for graphical representation. We observe that although BD is affected by channel spatial correlation, ASel yields almost the same performance for any AS. Furthermore, for AS =0.1 °, ASel performs similarly with BD for low M and exceeds BD for large M (i.e., M ≧ 20) due to multi-user diversity.

Fig. 49 compares the performance of the DIDO schemes that differ in SNR threshold the considered DIDO schemes are BD, ASel, BD with eigenmode selection (BD-ESel) and Maximal Ratio Combining (MRC) note that MRC does not cancel the interference at the transmitter beforehand (unlike other methods), but provides a larger gain if the users are spatially separated, in fig. 49 we plot the BER =10 target BER for the DIDO N × 2 system when the two users are located at-30 ° and 30 ° to the broadside direction of the transmit array, respectively^-2We observe that for low AS, the MRC scheme provides a gain of 3dB compared to other schemes because the spatial channels of the users are well separated and the impact of interference between users is small, note that the gain of MRC on DIDO N × 2 is due to array gain for AS greater than 20, the QR-ASel scheme produces a gain of about 10dB over other schemes compared to BD2 × 2 without selectionCan be used.

Described above are new adaptive transmission techniques for DIDO systems. The method dynamically switches between DIDO transmission modes to different users to enhance throughput for a fixed target bit error rate. The performance of different levels of DIDO systems is measured under different propagation conditions, and it is observed that a large gain in throughput can be achieved by dynamically selecting the DIDO mode and the number of users as a function of the propagation conditions.

Precompensation of frequency and phase differences

a. Background of the invention

As previously described, wireless communication systems use carriers to communicate information. These carriers are typically sinusoids whose amplitude and/or phase are modulated in response to the information being transmitted. The nominal frequency of the sine wave is known as the carrier frequency. To create this waveform, the transmitter synthesizes one or two sinusoids and uses up-conversion to create a modulated signal superimposed on the sinusoid with the specified carrier frequency. This may be achieved by direct conversion, where the signal is directly modulated on a carrier or through multiple up-conversion stages. To process the waveform, the receiver must demodulate the received RF signal and effectively remove the modulated carrier. This requires the receiver to synthesize one or more sinusoidal signals to reverse the modulation process at the transmitter, known as down-conversion. Unfortunately, the sine wave signals generated at the transmitter and receiver are obtained from different reference oscillators. No reference oscillator creates a perfect frequency reference; in practice, there is usually some deviation from the actual frequency.

In a wireless communication system, the difference in the output of the reference oscillator at the transmitter and the receiver creates a phenomenon known as carrier frequency offset or simply frequency offset at the receiver. Essentially, after down conversion, there is some residual modulation in the received signal (corresponding to the difference in the transmitted and received carriers). This creates distortion in the received signal, resulting in a higher bit error rate and lower throughput.

There are different techniques for handling carrier frequency offset. Most methods estimate the carrier frequency offset at the receiver and then apply a carrier frequency offset correction algorithm. The carrier frequency offset estimation algorithm is blind (blid) using the following method: offset QAM (T.Fusco and M.Tanda, "blank Frequency-offset Estimation for OFDM/OQAM Systems," Signal Processing, IEEE Transactions on [ see also Acoustics, Speech, and Signal Processing, IEEE Transactions on ], vol.55, pp.1828-1838, 2007); periodic characteristics (e.serpin, a.chevreil, g.b.giannakis and p.loubataton, "blank channel and carrier frequency estimation using a periodic modulation coder," Signal Processing, IEEE Transactions on [ see also Acoustics, spech, and Signal Processing, IEEE Transactions on ], vol.48, No.8, pp.2389-2405, aug.2000); or cyclic prefixes in Orthogonal Frequency Division Multiplexing (OFDM) structural methods (J.J.van de Beek, M.Sandell and P.O.Borjesson, "ML estimation of time and frequency offset in OFDM systems," Signal Processing, IEEE Transactions on [ see also Acoustics, Speech, and Signalprocessing, IEEE Transactions on ], vol.45, No.7, pp.1800-1805, July1997, U.Tureli, H.Liu and M.D.Zoltowski, "OFDM glass transport estimation: EST, IEEEN.TRANS., vol.48, No.9, 1459-1461, Sept.2000, M.Luise, M.Marlii and R.Marlii," emission, III.S. transmission, P.K. P.1188, C.M.P.P.M.P.P.M.P.P.P.S. K, M.S. K. M.S. M.

Alternatively, a dedicated training signal may be utilized, comprising repeated data symbols (p. h. moose, "iterative for orthogonal frequency division multiplexing frequency offset correction," IEEE trans. command, vol.42, No.10, pp.2908-2914, oct.1994); two different symbols (t.m. schmidl and d.c. cox, "Robust frequency and timing synchronization for ofdm," IEEE trans. command, vol.45, No.12, pp.1613-1621, dec.1997); or a known symbol sequence inserted periodically (m.luise and r.reggaiannini, "Carrier frequency acquisition and tracking for OFDM systems," IEEE trans. command, vol.44, No.11, pp.1590-1598, nov.1996). The correction may occur in an analog or digital manner. The receiver may also pre-correct the transmitted signal using the carrier frequency offset estimate to remove the offset. Carrier frequency offset correction is widely studied for multi-carrier and OFDM systems due to their sensitivity to frequency offset (j.j.van de beam, m.sandell and p.o.borjesson, "ML estimation of time and frequency offset in OFDM systems," Signal Processing, IEEE Transactions on [ see also Acoustics, spech, and Signal Processing, IEEE Transactions on ], vol.45, No.7, pp.1800-1805, July 1997; u.tube, h.liu and m.d.z. wski, "OFDM symbol estimation: ESPRIT," eetrans.com., schmann, sche.48, 9, 1469-1, t.2000, sep.2000, m.sep. carrier estimation: ESPRIT, "IEEE transmission, synchronization, c.1188, IEEE, sample 1188, sample, cross, c.1188, c.21. synchronization, c.21, sample synchronization, c.1188. synchronization, c.21, c.2. synchronization, c.72, c.k.k.5, IEEE transmission.

Frequency offset estimation and correction is an important issue for multi-antenna communication systems or more generally MIMO (multiple input multiple output) systems. In a MIMO system, the transmit antennas are locked to one frequency reference and the receiver is locked to another frequency reference with a single offset between the transmitter and receiver. Several algorithms are proposed to deal with this problem using Training signals (K.Lee and J.Chun, "Frequency-offset estimation for MIMO and OFDM systems using orthogonal Frequency transmission sequences," IEEE Trans.Veh.Technol., vol.56, No.1, pp.146-156, Jan.2007; M.Ghogh and A.Swam, "transmitting estimation for multipath channel and Frequency estimation in MIMO systems," Signal Processing, IEEETransactions on [ see also Acoustics, Speech, and Signal Processing, EEtransitions on]，vol.54, No.10, pp.3957-3965, Oct.2006; and adaptive tracking C.Oberli and B.Daneshrad, "Maximum likelihood tracking algorithm for MIMOOFDM," in Communications, 2004IEEE International Conference on, vol.4, June20-24, 2004, pp.2468-2472). A more important problem is encountered in MIMO systems, where the transmit antennas are not locked to the same frequency reference, but the receive antennas are locked together. This actually occurs in the uplink of Spatial Division Multiple Access (SDMA) systems, which are considered MIMO systems, where different users correspond to different transmit antennas. In this case, the compensation of the frequency offset is more complicated. In particular, the frequency offset creates interference in the different transmitted MIMO streams. The correction can be performed using complex Joint estimation and equalization algorithms (A.Kannan, T.P. Krauss and M.D.Zoltowski, "isolation of channel signals under interference equalization and carrier equalization," IEEE Trans.Veh.Technolo., vol.50, No.1, pp.79-96, Jan.2001), and equalization after frequency offset estimation (T.Tang and R.W.Heath, "Joint frequency estimation and interference cancellation for MIMO-OFDM systems [ mobile radio)]，″2004.VTC2004-Fall.2004IEEE60^thVehicular Technology Conference, vol.3, pp.1553-1557, Sept.26-29, 2004; dai, "Carrier frequency offset estimation for OFDM/SDMA systems using coherent pilots," IEEE Proceedings-Communications, vol.152, pp.624-632, Oct.7, 2005). Some work addressed the issues related to Residual phase offset and tracking error, where the Residual phase offset was estimated and compensated after frequency offset estimation, but this work only considered the uplink of the sdma ofdma system (L Haring, s.bieder and a.czywik, "Residual carrier and sampling frequency synchronization in multi-user OFDM systems," 2006.VTC2006-spring. ie. ee63rdvehicular Technology Conference, vol.4, pp.1937-1941, 2006). The most severe case occurs in MIMO systems when all transmit and receive antennas have different frequency references. The only available work on this topic only deals with asymptotic analysis of the estimation error in flat fading channels (o.besson and p.stoica, ")On parameter of MIMO flat-facing channels with frequency offset, "Signal Processing, IEEE Transactions On [ see also Acoustics, Speech, and Signal Processing, IEEE Transactions On]，vol.51，no.3，pp.602-613，Mar.2003)。

A situation that has been extensively studied occurs when different transmit antennas of a MIMO system do not have the same frequency reference and the receive antennas process the signals independently. This occurs in what is known as a distributed input output (DIDO) communication system (also referred to in the literature as a MIMO broadcast channel). The DIDO system includes one access point with distributed antennas that transmit parallel data streams (via precoding) to multiple users to enhance downlink throughput, when using the same radio resources (i.e., the same slot duration and frequency band) as a conventional SISO system. A detailed description of the DIDO System is set forth in U.S. patent application 20060023803 entitled "System and method for distributed output wireless communications" filed 7.2004, by s.g. perlman and t.cooler. There are many ways to implement a DIDO precoder. One solution is Block Diagonalization (BD), described for example in the following documents: speech, a.l. swindlehurst and m.haardt, "Zero-forcing methods for downlink spatial multiplexing in multiplexer MIMO channels," ieee trans.sig.proc, vol.52, pp.461-471, feb.2004; k.k.wong, r.d.muth and k.b.letaief, "Ajoint-channel differentiation for multi-user MIMO antenna systems," ieee trans.wireless comm., vol.2, pp.773-786, JuI 2003; choi and r.d.mutch, "a transmission processing technique for a multi-user MIMO systems using a coordination approach," IEEE trans.wireless comm., vol.3, pp.20-24, Jan 2004; z.shen, j.g.andrews, r.w.heath and B.L Evans, "Low complex user selection algorithm for multiuser MIMO systems with block segmentation," accepted as published in ieee ns.sig.proc, sep.2005; z.shen, r.chen, j.g.andrews, r.w.heath and B.L Evans, "summary of multi-user MIMO channels with block diagonalization," submitted to IEEE trans.wireless comm., oct.2005; R.Chen, R.W.Heath and J.G.Andrews, "Transmit selection direction for unity encoded multiuser spatial multiplexing systems with linear receivers," is accepted into IEEE Transs, on Signal processing, 2005.

In DIDO systems, transmit precoding is used to separate data streams for different users. Carrier frequency offset causes several problems associated with system implementation when the transmit antenna rf chains do not share the same frequency reference. When this occurs, each antenna effectively transmits at a slightly different carrier frequency. This destroys the integrity of the DIDO precoder, resulting in each user experiencing additional interference. Several solutions to this problem are presented below. In one embodiment of the solution, the DIDO transmit antennas share a frequency reference through a wired, optical, or wireless network. In another embodiment of the solution, one or more users estimate the frequency offset difference (the relative difference in offset between antenna pairs) and send this information back to the transmitter. The transmitter then pre-corrects the frequency offset and proceeds with training and precoder estimation phase for DIDO. This embodiment has problems when the feedback channel has a delay. The reason is that there may be a residual phase error created by the correction process that does not take into account the subsequent channel estimates. To address this problem, a further embodiment uses a new frequency offset and phase estimator, which addresses this problem by estimating the delay. The results are given based on simulations and actual measurements performed by DIDO-OFDM prototypes.

The frequency and phase offset compensation method proposed in this document may be sensitive to estimation errors due to noise at the receiver. Therefore, another embodiment proposes a method for time and frequency offset estimation, which is also robust under low SNR conditions.

There are different methods for performing the time and frequency offset estimation. Many of these methods are specifically proposed for OFDM waveforms due to their sensitivity to synchronization errors.

These algorithms do not typically use the structure of the OFDM waveform and are therefore generally sufficient for single carrier and multi-carrier waveforms. The algorithms described below are among a class of techniques that use known reference symbols (e.g., training data) to assist in synchronization. Many methods are extensions of the frequency offset estimator of Moose (see p.h. Moose, "a technical for orthogonal frequency division multiplexing frequency offset correction," ieee trans. command, vol.42, No.10, pp.2908-2914, oct.1994). Moose proposes to use two repeated training signals and to use the phase difference between the received signals to obtain the frequency offset. The method of Moose is only able to correct fractional (fractional) frequency offsets. An extension of the method of Moose is proposed by Schmidl and Cox (t.m.schmidl and d.c.cox, "Robust frequency and timing synchronization for OFDM," ieee trains. command., vol.45, No.12, pp.1613-1621, dec.1997). Their main innovation is the use of one periodic OFDM symbol and additional differentially coded training symbols. Differential encoding in the second symbol achieves integer offset correction. Coulson considers similar settings described in t.m.schmidl and d.c.cox, "Robust frequency and timing synchronization for OFDM," IEEE trans.com., vol.45, No.12, pp.1613-1621, dec.1997, and in a.j.coulson, "Maximum likelihood synchronization for OFDM using a pilot symbol: analysis, "IEEE J.select.areas Commun., vol.19, No.12, pp.2495-2503, Dec.2001 and A.J.Coulson," Maximum likelihoodsynchonization for OFDM using a pilot symbol: a detailed discussion of algorithms and analyses is provided in algorithms, "IEEE J.select.areas Commun., vol.19, No.12, pp.2486-2494, Dec.2001. One major difference is that Coulson uses repeated maximal length sequences to provide good correlation properties. He also suggests the use of chirp signals because of their constant envelope properties in the time and frequency domains. Coulson considers the actual details but does not include integer estimation. Multiple repeated training signals are considered by Minn et al.in H.Minn, V.K.Bharagava and K.B.Letaief, "A robust timing and frequency synchronization for OFDM systems," IEEE trans.Wireless Commun., vol.2, No.4, pp.822-839, July2003, but the structure of the training is not optimized. Shi and Serpidin propose that the training architecture has some optimality with the idea of tangible framing synchronization (K.Shi and E.Serpidin, "Coarse frame and carrier synchronization of OFDM systems: a newmetric and compensation," IEEE Trans. Wireless Commun., vol.3, No.4, pp.1271-1284, July 2004). One embodiment of the present invention uses the methods of Shi and serpidin to perform frame synchronization and fractional frequency offset estimation.

Many methods in the literature focus on frame synchronization and fractional frequency offset correction. Integer offset correction is solved using additional training symbols in t.m.schmidl and d.c.cox, "Robust frequency and timing synchronization for OFDM," IEEE trans.com., vol.45, No.12, pp.1613-1621, dec.1997. For example, Morrelli et al, in M.Morelli, A.N.D' Andrea and U.M. Mengali, "Frequency amplification in OFDM systems," IEEE Commin.Lett., vol.4, No.4, pp.134-136, Apr.2000, obtained improvements of T.M.Schmidl and D.C.Cox, "Robust Frequency and timing synchronization for OFDM," IEEE Trans.Commun., vol.45, No.12, pp.1613-1621, Dec.1997. An alternative approach to using different preamble structures is proposed by Morelli and Mengali (m.morelli and u.mengali, "improved frequency offset estimator for OFDM applications," IEEE commu.lett., vol.3, No.3, pp.75-77, mar.1999). This method uses the correlation between M repetitions of the same training symbol to increase the range of the fractional frequency offset estimator by a factor of M. This is the best linear unbiased estimator and accepts the largest offset (with proper design), but does not provide good timing synchronization.

Description of the System

One embodiment of the present invention uses precoding based on channel condition information to remove frequency and phase offsets in a DIDO system. See fig. 11 and the associated description above for a description of this embodiment.

In one embodiment of the invention, each user uses a receiver equipped with a frequency offset estimator/compensator. As shown in fig. 45, in one embodiment of the invention, a system including a receiver includes a plurality of RF units 4508, a corresponding plurality of a/D units 4510, a receiver equipped with a frequency offset estimator/compensator 4512, and a DIDO feedback generator unit 4506.

RF unit 4508 receives signals transmitted from the DIDO transmitter unit, down-converts the signals to baseband, and provides the down-converted signals to a/D unit 4510. The a/D unit 4510 then converts the signal from analog to digital and sends it to the frequency offset estimator/compensator unit 4512. The frequency offset estimator/compensator unit 4512 estimates and compensates for a frequency offset as described herein, and then transmits the compensated signal to the OFDM unit 4513. The OFDM unit 4513 removes a cyclic prefix and performs Fast Fourier Transform (FFT) to report a signal to the frequency domain. During training, OFDM unit 4513 sends an output to channel estimation unit 4504 to calculate a channel estimate in the frequency domain. Alternatively, the channel estimate may be calculated in the time domain. During a data period, OFDM unit 4513 sends output to DIDO receiver unit 4502, which DIDO receiver unit 4502 demodulates/decodes the signal to obtain data. Channel estimation unit 4504 sends the channel estimates to DIDO feedback generator unit 4506, which DIDO feedback generator unit 4506 may quantize the channel estimates and send them back to the transmitter via a feedback control channel, as shown.

Description of one embodiment of an algorithm for DIDO2 × 2 scenario

Described below are embodiments of algorithms for frequency/phase offset compensation in DIDO systems. The DIDO system model begins to be described with and without frequency/phase offsets. For simplicity, a specific implementation of the DIDO2 x 2 system is provided. However, the underlying principles of the invention may also be implemented in higher-order DIDO systems.

DID with/without frequency and phase offsetO System model

The received signal of DIDO2 × 2 may be written to the first user as:

r₁[t]＝h₁₁(w₁₁x₁[t]+w₂₁x₂[t])+h₁₂(w₁₂x₁[t]+w₂₂x₂[t]) (1)

and for a second user to write:

r₂[t]＝h₂₁(w₁₁x₁[t]+w₂₁x₂[t])+h₂₂(w₁₂x₁[t]+w₂₂x₂[t]) (2)

where t is the discrete time index, h_mnAnd W_mnChannel and DIDO precoding weights, x, between the mth user and the nth transmit antenna, respectively_mIs the transmit signal for user m. Note that h_mnAnd w_mnIs not a function of t because we assume that the channel is constant over the period between training and data transmission.

In the presence of frequency and phase offsets, the received signal is represented as

And

wherein, T_sIs the symbol period; for the nth transmit antenna, ω_Tn＝2Πf_Tn(ii) a For the m-th user, ω_Um＝2Πf_Um(ii) a And f_TnAnd f_UmThe actual carrier frequencies (affected by the offset) for the nth transmit antenna and the mth user, respectively. Value t_mnIs indicated in channel h_mnThe random delay that results in the phase offset fig. 46 depicts a model of the DIDO2 × 2 system.

For time, we use the following definitions:

Δω_mn＝ω_Um-ω_Tn(5)

to represent the frequency offset between the mth user and the nth transmit antenna.

Description of one embodiment of the invention

A method according to one embodiment of the invention is shown in fig. 47. The method comprises the following general steps (including sub-steps, as shown): training period 4701 for frequency offset estimation; training period 4702 for channel estimation; data transmission 4703 via DIDO precoding with compensation. These steps are described in detail below.

(a) Training period for frequency offset estimation (4701)

During a first training period, the base station transmits one or more training signals from each transmit antenna to one of the users (4701 a). As described herein, a "user" is a wireless client device. For the DIDO2 × 2 case, the signal received by the mth user is given by:

wherein p is₁And p₂Respectively, training sequences transmitted from the first and second antennas.

The mth user can use any type of frequency offset estimator (i.e., by convolution of the training sequence) and estimate the offset Δ ω_m1And Δ ω_m2. From these values, the user then calculates the frequency offset between the two transmit antennas:

Δω_T＝Δω_m2-Δω_m1＝ω_T1-ω_T2(7)

finally, the value in (7) is fed back to the base station (4701 b).

Note that p in (6)₁And p₂Designed to be orthogonal so that the user can estimate Δ ω_m1And Δ ω_m2. Alternatively, in one embodiment, the same training sequence is used in two consecutive time slots from which the user estimates the offset. Furthermore, to improve the estimation of the offset in (7), the same calculations described above can be done for all users of the DIDO system (not just for the mth user), and the final estimation can be a (weighted) average of the values obtained from all users. However, this solution requires more computation time and amount of feedback. Finally, an update of the frequency offset estimate is only needed if the frequency offset changes over time. Thus, depending on the stability of the clock at the transmitter, step 4701 of the algorithm may be performed for a long period of time (i.e., for each data transmission), such that the above-described feedback is reduced.

(b) Training period for channel estimation (4702)

During the second training period, the base station first feeds back frequency offsets from the mth user or from multiple users or even from the users having the value in (7). The value in (7) is used to pre-compensate for the frequency offset at the transmitting end. The base station then transmits training data to all users for channel estimation (4702 a).

For the DIDO2 x 2 system, the signal received at the first user is given by:

and at the second user:

wherein,and Δ t is the random or known delay between the first and second transmissions of the base station. Furthermore, p₁And p₂The training sequences transmitted from the first and second antennas are the user frequency offset and channel estimate, respectively.

Note that the pre-compensation is applied to the second antenna only in this embodiment.

Unfolding (8), we obtain

Similarly for the second user:

wherein,

at the receiving end, the user uses the training sequence p₁And p₂To compensate for the remaining frequency offset. Then, the user estimates through the training vector channel (4702 b):

these channel or channel condition information (CSI) in (12) are fed back to the base station (4702b), which computes the DIDO precoder as described in the following section.

(c) DIDO precoding with precompensation (4703)

The base station receives (12) the channel condition information (CSI) from the user and calculates precoding weights (4703a) by Block Diagonalization (BD) such that

Wherein, the vector h₁Is defined in (12), and w_m＝[w_m1，w_m2]. Note that the present invention proposed in the present disclosure can be used in any other DIDO precoding method than BD. The base station also pre-compensates for the frequency offset by using the estimate in (7) and by estimating the delay (Δ t) between the second training transmission and the current transmission₀) To pre-compensate for the phase offset (4703 a). Finally, the base station transmits data to the user via the DIDO precoder (4703 b).

After the transmission process, the signal received at the user 1 is given by:

wherein,using attribute (13), we obtain

Similarly, for user 2, we get:

deployment (16):

wherein,

finally, the user calculates the remaining frequency offset and channel estimate to demodulate data stream x₁[t]And x₂[t](4703c)。

Generalizing to DIDON × M

In this section, the previously described techniques are generalized to DIDO systems with N transmit antennas and M users.

i. Training period for user frequency offset estimation

During the first training period, the signal received by the mth user due to the training sequence transmitted from the N antennas is given by:

wherein p is_nIs the training sequence transmitted from the nth antenna.

At the estimated offset Δ ω_mnAfter that time, the user can use the device,the mth user calculates the frequency offset between the first and nth transmit antennas:

Δω_T，1n=Δω_mn-Δω_m1=ω_T1-ω_Tn(19)

finally, the value in (19) is fed back to the base station.

For channel estimationTraining period of

During the second training period, the base station first obtains frequency offset feedback from the mth user or from multiple users with a value in (19). The value in (19) is used to pre-compensate for the frequency offset at the transmitting end. The base station then sends training data to all users for channel estimation.

For the DIDO nxm system, the signal received at the mth user is given by:

wherein,and Δ t is the random or known delay between the first and second transmissions of the base station. Furthermore, P_nIs a training sequence transmitted from the nth antenna for frequency offset and channel estimation.

On the receiving side, the user uses the training sequence P_nTo compensate for the remaining frequency offset. Then, each user m estimates through the training vector channel:

and fed back to the base station, which calculates the DIDO precoder as described in the following section.

DIDO precoding with precompensation

The base station receives (12) channel condition information (CSI) from the users and calculates precoding weights by Block Diagonalization (BD) such that

Wherein, the vector h_mIs defined in (21), and w_m＝[w_m1，w_m2，...，w_mN]. The base station also pre-compensates for the frequency offset by using the estimate in (19) and by estimating the delay (Δ t) between the second training transmission and the current transmission₀) To pre-compensate for the phase offset. Finally, the base station transmits data to the user via the DIDO precoder.

After the transmission process, the signal received at user i is given by:

wherein,using attributes (22), we get:

finally, the user calculates the remaining frequency offset and channel estimate to demodulate data stream x_i[t]。

Results

Fig. 48 shows SER results for DIDO2 x 2 systems with and without frequency offset. It can be seen that the proposed method completely removes the frequency/phase offset, yielding the same SER as a system without offset.

Next, we evaluated the sensitivity of the proposed compensation method to fluctuations in frequency offset error and/or real-time offset. Therefore, we rewrite (14) to:

wherein the estimation error and/or variation of the frequency offset between training and data transmission is represented. Note that the effect of (c) is to break the orthogonality properties in (13) so that the interference terms in (14) and (16) are not completely pre-cancelled at the transmitter. Because of this, SER performance decreases with increasing values.

FIG. 48 shows the SER performance of the frequency offset compensation method for different ∈ values_s=0.3ms (i.e. a signal with a 3KHz bandwidth). We observed that for =0.001Hz (or less), the SER performance was similar to the case without the offset.

f. Description of one embodiment of an algorithm for time and frequency offset estimation

Next, we describe another embodiment of performing time and frequency offset estimation (4701b in fig. 47). The transmit signal structures considered are set forth in h.minn, v.k.bhragova and k.b.leief, "a robust timing and frequency synchronization for OFDM systems," IEEE trans.wireless communication, vol.2, No.4, pp.822-839, July2003, in k.shi and e.serpin, "Coarse frame and carrier synchronization of OFDM systems: a new measurement and compliance, "IEEETranss. Wireless Commun., vol.3, No.4, pp.1271-1284, July 2004. Sequences that generally have good correlation properties are used for training. For example, for our system, Chu sequences are used, as described in D.Chu, "polypeptide codes with good periodic characterization properties (coresp.)," IEEE trans. info. Therory, vol.18, No.4, pp.531-532, July 1972. These sequences have the interesting property that they have perfect circular correlation. Let L_cpIndicating the length of the cyclic prefix, N_tRepresenting the length of the component training sequence. So that N is_t=M_tWherein M is_tIs the length of the training sequence. Under these assumptions, the transmitted symbol sequence for the start can be written as:

s[n]=t[n-N_t]for n = -1, …, -L_cp

s[n]=t[n]For N =0, …, N_t-1

s[n]=t[n-N_t]For N = N_t，…，2N_t-1

s[n]=-t[n-2N_t]For N =2N_t，…，3N_t-1

s[n]＝t[n-3N_t]For N =3N_t，…，4N_t-1

Note that the structure of the training signal may be extended to other lengths, but the block structure is repeated. For example, to use 16 training signals, we consider a structure such as:

[CP，B，B，-B，B，B，B，-B，B，-B，-B，B，-B，B，B，-B，B，]。

by using this structure, and making N_t=4M_tAll algorithms to be described can be used without modification. Effectively, we repeat the training sequence. This is particularly useful in situations where a suitable training signal may not be available.

After matched filtering and down-sampling of the symbol rate, consider the following received signal:

where is the unknown discrete-time frequency offset, Δ is the unknown frame offset, h1 is the unknown discrete-time channel coefficient, and v n is additive noise. To explain the key idea in the following section, the presence of additional noise is ignored.

i. Coarse frame synchronization

The purpose of the coarse frame synchronization is to account for the unknown frame offset delta. Let us make the following definitions:

r₁[n]：=[r[n],r[n+1],...,r[n+N_t-1]]^T，

r₂[n]：=[r[n+N_t],r[n+1+N_t]，...，r[n+2N_t-1]]^T，

r₃[n]：=[r[n+2N_t]，r[n+1+2N_t]，...，r[n+3N_t-1]]^T，

r₄[n]：=[r[n+3N_t]，r[n+1+3N_t]，...，r[n+4N_t-1]]^T，

the proposed Coarse frame synchronization algorithm is from K.Shi and E.Serpidin, "Coarse frame and carrier synchronization of OFDM systems: algorithms in new measurement and compliance, "ieee trains. wireless Commun., vol.3, No.4, pp.1271-1284, July2004, inspired by the maximum likelihood criterion.

Method 1-improved coarse frame synchronization: the coarse frame sync estimator solves the following optimization:

wherein,

so that the corrected signal is defined as:

additional correction terms are used to compensate for small initial pulses in the channel and may be adjusted based on the application. This extra delay will then be included in the channel.

Fractional frequency offset correction

The fractional frequency offset correction follows the coarse frame sync block.

Method 2-improved fractional frequency offset correction: the fractional frequency offset is the following solution:

this is known as fractional frequency offset, since the algorithm can only correct for offset

This problem will be solved in the next section. Let the fine frequency offset correction signal be defined as:

note that methods 1 and 2 are k.shi, e.serpin, "coaleframe and carrier synchronization of OFDM systems: a new measurement and balance, "IEEE Trans. Wireless Commun., vol.3, No.4, PP.1271-1284, July 2004. One particular innovation here is the use of r and r as described aboveThe use of (c) improves the previous estimator in that it ignores samples that are affected due to internal symbol interference.

integer frequency offset correction

In order to correct for integer frequency offsets, it is necessary to write an equivalent system model for the received signal after fine frequency offset correction. Absorbing the retained timing error into the channel, the received signal without noise having the following structure:

wherein N =0, 1.., 4N_t-1. The integer frequency offset is k and the unknown equivalent channel is g l]。

Method 3-improved integer frequency offset correction: the integer frequency offset is the solution:

wherein:

r=D[k]Sg

this gives an estimate of the total frequency offset:

in practice, method 3 has a high complexity. To reduce complexity, the following observations can be made. First, the product S (S)^-1S may be pre-calculated. Unfortunately, this still leaves a considerable matrix multiplication. An alternative is to use an observation with the proposed training sequence, S ≈ I. This results in the following reduced load type approach.

Method 4-low complexity improved integer frequency offset correction:

the integer frequency offset estimator with low complexity solves

Results iv

In this section we compare the performance of different proposed estimators.

First, atIn fig. 50, we compare the amount of overhead required for each method. Note that the two new methods reduce overhead by a factor of 10 to 20. To compare the performance of the different estimators, MonteCarlo experiments were performed. The settings considered are our usual NVIS transmit waveform constructed from linear modulation with a symbol rate of 3K symbols per second, corresponding to a passband bandwidth of 3KHz, and raised cosine pulse shaping. For each Monte Carlo implementation, the frequency offset is from being at [ -f_max，f_max]Are uniformly distributed.

Having f_maxA simulation of a small frequency offset of =2Hz without integer offset correction is shown in fig. 51. It can be seen from this comparison of properties that there is N_t／M_tThe performance of =1 is slightly degraded from the original estimator, although the overhead is substantially reduced. Having N_t/M_tThe performance of =4 is better, almost 10 dB. All curves experience a meandering at low SNR points due to errors in the integer offset estimation. Small errors in the integer offset can create large frequency errors and large splice square errors. Integer offset correction can be turned off in small offsets to improve performance.

The performance of the frequency offset estimator generally degrades in the presence of multipath channels. However, in fig. 52, the off integer offset estimator exhibits very good performance. Therefore, in multipath channels, an improved fine correction algorithm after performing robust coarse correction is more important. Note that having N_t/M_tThe offset performance of =4 is much better in the multipath case.

Embodiments of the invention may include various steps set forth above. The steps may be implemented in machine-executable instructions, which cause a general-purpose or special-purpose processor to perform certain steps. For example, various components within the base station/AP and client devices described above may be implemented as software executing on a general purpose or special purpose processor. Various well-known personal computer components, such as computer memory, hard disks, input devices, etc., have been omitted from the figures in order not to obscure the relevant aspects of the present invention.

Alternatively, in one embodiment, the various functional blocks and associated steps illustrated herein may be performed by specific hardware components that contain hardwired logic for performing the steps, such as an Application Specific Integrated Circuit (ASIC), or by any combination of programmed computer components and custom hardware components.

In one embodiment, certain modules, such as the encoding, modulation, and signal processing logic 903 described above, may be implemented on a programmable Digital Signal Processor (DSP) (or set of DSPs), such as a DSP (e.g., TMS320C6000, TMS320C5000, etc.) using the TMS320x architecture of texas instruments (texas instruments). The DSP in this embodiment may be embedded in a plug-in card of a personal computer, such as a PCI card. Of course, a variety of different DSP architectures may be used in keeping with the underlying principles of the invention.

Various components of the present invention may also be provided as a machine-readable medium for storing the machine-executable instructions. The machine-readable medium may include, but is not limited to, flash memory, optical disks, CD-ROMs, DVD ROMs, RAMs, EPROMs, EEPROMs, magnetic or optical cards, propagation media or other type of machine-readable media suitable for storing electronic instructions. For example, the invention may be downloaded as a computer program which may be transferred from a remote computer (e.g., a server) to a requesting computer (e.g., a client) by way of data signals embodied in a carrier wave or other propagation medium via a communication link (e.g., a modem or network connection).

Throughout the foregoing description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present systems and methods. It will be apparent, however, to one skilled in the art that the systems and methods may be practiced without some of these specific details. Accordingly, the scope and spirit of the invention should be judged in terms of the claims which follow.

Furthermore, in the foregoing description, numerous documents are cited to provide a more thorough understanding of the present invention. All of these cited references are incorporated by reference into this application.

Claims (24)

1. A method for compensating for frequency and phase offsets in a multi-antenna system MAS MU-MAS with multi-user MU transmissions, the method comprising:

at each of a plurality of transceivers of a base station, transmitting or receiving a first plurality of training signals and analyzing each training signal to obtain frequency and phase offset compensation at the transceiver;

transmitting a second plurality of training signals on a link between each transceiver of the base station and one or each of a plurality of wireless client devices, and analyzing each training signal to obtain channel characterization data at the base station;

calculating a plurality of MU-MAS precoder weights based on the channel characteristic data and using frequency and phase offset compensation, the MU-MAS precoder weights calculated to pre-cancel inter-user interference;

precoding data using the MU-MAS precoder weights to generate precoded data signals for each transceiver of the base station; and

transmitting the precoded data signals to each respective client device through each transceiver of the base station.

2. The method of claim 1, further comprising:

transmitting the second plurality of training signals from each of the plurality of wireless client devices to each transceiver of the base station, the base station analyzing each training signal to generate channel characterization data.

3. The method of claim 1, further comprising:

precoding a third plurality of training signals using the MU-MAS precoder weights to generate precoded training signals for each transceiver of the base station;

transmitting the precoded training signals from each transceiver of the base station to each of the plurality of wireless client devices, each of the client devices analyzing each training signal to generate channel characterization data and frequency offset compensation data and using these data to demodulate the precoded data signals.

4. The method of claim 1, wherein the base station is an access point coupling the wireless client device to a wide area network.

5. The method of claim 1, wherein one centralized transceiver unit estimates the frequency offset between all transceivers and pre-compensates for the offset, or the transceivers of the base station share a frequency reference over a wired, optical, or wireless network.

6. The method of claim 1, wherein the first plurality of training signals are transmitted for a long period of time to reduce overhead.

7. The method of claim 1, wherein the transceiver estimates a rate of change of the frequency offset and determines an update rate of the training.

8. The method according to claim 1, wherein data precoding is performed using block diagonalization BD techniques.

9. The method in claim 1 wherein the MU-MAS system is a distributed input distributed output, DIDO, communication system and wherein the MU-MAS precoder weights are DIDO precoder weights.

10. A method for compensating in-phase-quadrature I/Q imbalance in a multi-antenna system MAS MU-MAS with multiuser MU transmissions, the method comprising:

transmitting a plurality of training signals on a link between each transceiver of a base station and one or each of a plurality of wireless client devices, and analyzing each training signal to obtain channel characteristic data at the base station;

calculating a plurality of MU-MAS precoder weights based on the channel characteristic data, the MU-MAS precoder weights calculated to pre-cancel interference due to I/Q gain and phase imbalance and/or inter-user interference;

precoding data using the MU-MAS precoder weights to generate precoded data signals for each transceiver of the base station; and

transmitting the precoded data signals to each respective client device through each transceiver of the base station.

11. The method of claim 10, further comprising:

transmitting the plurality of training signals from each of a plurality of wireless client devices to each transceiver of the base station, the base station analyzing each training signal to generate channel characterization data.

12. The method of claim 10, further comprising:

a training signal is transmitted from each transceiver of a base station to each of a plurality of wireless client devices, each of the client devices analyzes each training signal to generate channel characterization data, and the channel characterization data is received at the base station.

13. The method of claim 10, wherein the base station is an access point coupling the wireless client device to a wide area network.

14. The method of claim 10, further comprising:

the data stream is demodulated at each user device using a zero-forcing ZF receiver, a minimum mean square error, MMSE, receiver, or a maximum likelihood, ML, receiver to suppress residual interference.

15. The method according to claim 10, wherein the pre-coding is performed using a block diagonalized BD technique.

16. The method in claim 10 wherein the MU-MAS system is a distributed input distributed output, DIDO, communication system and wherein the MU-MAS precoder weights are DIDO precoder weights.

17. The method of claim 16 wherein the precoder weights are calculated to cancel inter-user interference instead of inter-carrier interference ICI, and wherein the wireless client device comprises a receiver with a filter for canceling the ICI.

18. A method for dynamically adapting communication characteristics of a multi-antenna system MASMU-MAS with multi-user MU transmissions, the method comprising:

transmitting a plurality of training signals on a link between each transceiver of a base station and one or each of a plurality of wireless client devices, and analyzing each training signal to obtain channel characteristic data at the base station;

determining an instantaneous or statistical channel quality, link quality metric, for the wireless client device using the channel characterization data;

determining a subset of users and an MU-MAS transmission mode based on the link quality metrics;

calculating a plurality of MU-MAS precoder weights based on the channel characteristic data;

precoding data using the MU-MAS precoder weights to generate precoded data signals for each transceiver of the base station; and

transmitting the precoded data signals through each transceiver of the base station to each respective client device within the selected subset.

19. The method of claim 18, comprising:

transmitting the plurality of training signals from each of the plurality of wireless client devices to each transceiver of the base station, the base station analyzing each training signal to generate channel characterization data.

20. The method of claim 18, comprising:

a training signal is transmitted from each transceiver of a base station to each of a plurality of wireless client devices, each of the client devices analyzes each training signal to generate channel characterization data, and the channel characterization data is received at the base station.

21. The method as in claim 18 wherein the MU-MAS transmission modes include different combinations of transceiver selection/diversity or multiplexing, modulation/coding scheme MCS and array configuration/geometry.

22. The method of claim 18, wherein the link quality metric is estimated in the time domain, frequency domain and/or spatial domain.

23. The method of claim 18, wherein the link quality metric comprises a signal-to-noise ratio (SNR) of the signal received at the client device.

24. The method in claim 18 wherein the MU-MAS system is a distributed input distributed output, DIDO, communication system, wherein the MU-MAS transmission mode is a DIDO transmission mode based on the link quality metric, and wherein the MU-MAS precoder weights are DIDO precoder weights.