CN1156999C - Doppler spread estimation using channel autocorroelation function hypotheses - Google Patents

Abstract

The Doppler spread for the communication channel is measured by providing an estimate of the communication channel and generating an autocorrelation function for the communication channel estimate. One of a plurality of autocorrelation function hypotheses is chosen to approximate the autocorrelation function for the communication channel estimate, where each autocorrelation function hypothesis corresponds to a respective Doppler spread estimation hypothesis. One of the Doppler spread estimate hypotheses corresponding to the selected autocorrelation function hypothesis is selected as the Doppler spread estimate for the communication channel.

Description

Doppler Spread Estimation Using Channel Autocorrelation Function Assumption

Technical field

The present invention relates to the field of communications, and more particularly, the present invention relates to receiving radio communications.

Background technique

A radio channel for mobile terminals in a cellular radiotelephone communication system can be difficult to operate. In particular, transmitted signals are often reflected, scattered, diffracted, delayed, and attenuated by the surrounding environment. Furthermore, the radio channel for a mobile terminal is often not stationary because of the movement of the mobile terminal and the movement of objects close to the mobile terminal. When the mobile terminal is used in a car, it can move quickly, and other vehicles can also move close to the mobile terminal.

Radio channel characteristics may also vary from one area to another due to differences in terrain/buildings, climate, and/or other factors. Propagation of radio signals along radio channels may therefore be subject to multipath fading, shadowing and path loss. Among these factors, multipath fading is probably the most important, and multipath fading can be characterized by envelope fading, Doppler spread and time delay spread.

Doppler shift is the frequency shift experienced by a radio signal when a mobile terminal moves, and Doppler spread is a measure of the spectral spread caused by the temporal rate of change of a mobile radio channel. The Doppler spread thus leads to dispersion, and the Doppler spread in the frequency domain is closely related to the rate of change in the observed signal. Therefore, the adaptive time of the algorithm used in the adaptive receiver should be faster than the rate of change of the channel in order to be able to accurately track the fluctuations in the received signal.

For example, a mobile terminal in a DAMPS cellular radiotelephone communication system may experience Doppler spread in the range of 0 Hz to 250 Hz, depending on vehicle speed, carrier frequency, and other factors. This knowledge of the rate of change of the radio channel can be used to improve the performance and/or reduce the complexity of the receiver. Furthermore, the adaptation parameters for an adaptive receiver can be changed as a function of the Doppler spread. Instead of fixing the parameters of the tracker and interpolator for the worst-case expected Doppler spread, for example, these parameters can be adaptively changed as a function of the Doppler information to improve performance. Similarly, The Doppler spread information can be used to control the receiver adaptively to the different speeds at which the mobile station may be moving. In other words, different receiver algorithms may be used depending on how fast the mobile terminal is moving.

The estimation of the Doppler spread can thus be used to increase the performance of the receiver. The parameters of the receiver adaptation algorithm can be varied as a function of the Doppler spread to adaptively optimize, for example, a coherent detector in the receiver. Additionally, handoff procedures in cellular mobile telephone systems can be enhanced if Doppler estimates are available. Handover of a fast-moving mobile terminal to a microcell can thus be avoided.

For example, in U.S. Patent No. 4,723,303 granted to Koch, entitled "Method of and Circuit Arrangement for Measuring the Quality of Radio-Transmission Channels of a Radio-Transmission System", and U.S. Patent No. 5,016,017 granted to Raith, entitled "Method of Doppler spread estimation is discussed in Controlling the Frequency of a Coherent Radio Receiver and Apparatus for Carrying Out the Method". Accordingly, the disclosure of each of these patents is hereby incorporated by reference in its entirety.

For example, in Lars Lindbom's paper, titled "Adaptive Equalization for Fading Mobile Radio Channels", (Techn, Lic. Thesis No. UPTEC 92124R, November 1992, Department of Technology, Uppsala University, Uppsala Sweden), it is discussed from a series of method for estimating Doppler spread by channel estimation, the disclosure of which is hereby incorporated by reference in its entirety. In the Lindbom paper, the difference in channel estimates, including the difference in value between two points in time, is used to estimate the Doppler spread. However, these differences can be noise, so averaging is required. As a result, this average can give an estimate of the Doppler spread of the offset.

In references Karim Jamal et al. entitled "Adaptive MSLE Performance On The D-AMPS 1900 Channel" (IEEE Trans.Vehic.Technol., Vol.46, Aug.1997), and references M.Morelli et al. titled "Further Results In Carrier Other Doppler spread estimation techniques are discussed in "Frequency Estimation for Transmissions Over FlatFading Channels" (IEEE Commun Letters, Vol.2, pp.327-330, Dec.1998).

Notwithstanding the various methods discussed above, there continues to be a need for improved techniques for Doppler spread estimation methods.

Contents of the invention

It is therefore an object of the present invention to provide an improved method of estimating the Doppler spread of a communication channel and related systems and receivers.

Another object of the present invention is to provide less complex methods of estimating Doppler spread and related systems and receivers.

These and other objects are achieved in accordance with the present invention by providing an estimate of the communication channel and by generating an autocorrelation function for the estimate of the communication channel. One of a plurality of autocorrelation function assumptions is selected to approximate the autocorrelation function of the communication channel estimate, wherein each autocorrelation function assumption corresponds to a respective Doppler spread estimate assumption. The Doppler spread estimate hypothesis corresponding to the selected autocorrelation function hypothesis is then selected as the estimate of the Doppler spread for the communication channel.

This autocorrelation function hypothesis can be stored in the memory of the Doppler spread estimator according to the present invention and compared with the autocorrelation function estimate for the communication channel, the closest autocorrelation function hypothesis being accepted as the one for the communication channel The actual autocorrelation function estimate for . In this way, the Doppler spread hypothesis corresponding to the closest autocorrelation function can be used as an estimate of the actual Doppler spread of the communication channel. The computational complexity for estimating Doppler spread can thus be reduced while providing a more accurate estimate of Doppler spread. Additionally, the number of autocorrelation function hypotheses used can be increased to provide more accurate estimates, or decreased to reduce the number of calculations and the total amount of memory used.

In more detail, selecting one of the autocorrelation function hypotheses may include comparing the autocorrelation function used for the communication channel estimate with each of the plurality of autocorrelation function hypotheses. Additionally, selecting one of the autocorrelation function hypotheses may include selecting one of the plurality of autocorrelation function hypotheses that most closely approximates the autocorrelation function used to estimate the communication channel.

Also, selecting one of the autocorrelation function hypotheses may include generating a plurality of error signals respectively corresponding to the plurality of autocorrelation function hypotheses, wherein each error signal represents the autocorrelation function between the respective autocorrelation function hypothesis and the communication channel estimate The difference between , the error signals are compared, and the autocorrelation assumption is chosen to approximate the autocorrelation function used to estimate the communication channel. In particular, the error signals may be compared in order to select the error signal representing the smallest difference between the respective autocorrelation function hypotheses and the autocorrelation function used to estimate the communication channel. Additionally, each error signal is averaged to provide an averaged error signal prior to the comparison of the error signals, wherein the comparison of the error signals includes the comparison of the averaged error signals.

The method, system and receiver according to the invention can thus be used to provide an estimate of the Doppler spread of a communication channel with low complexity.

According to the present invention, there is provided a method for estimating the Doppler spread of a communication channel, the method comprising: providing an estimate of the communication channel; generating an autocorrelation function of the estimate of the communication channel; selecting a plurality of autocorrelation function hypotheses One of the autocorrelation functions to approximate the communication channel estimate, wherein each autocorrelation function among these autocorrelation function assumptions corresponds to a respective Doppler spread estimation assumption; and selection corresponds to the selected autocorrelation function assumption One of these estimates of the Doppler spread of α is assumed to be an estimate of the Doppler spread of the communication channel.

According to the present invention, there is also provided a Doppler spread estimator for estimating the Doppler spread of a communication channel, the Doppler spread estimator comprising: corresponding to a plurality of Doppler spread estimation hypotheses respectively A plurality of autocorrelation function assumptions for the communication channel; a channel estimator for estimating the communication channel; an autocorrelation generator for generating an autocorrelation function for the communication channel estimate; selecting these autocorrelations that approximate the autocorrelation function for the communication channel estimate an autocorrelation function hypothesis tester for one of the function assumptions; and a Doppler spread that selects one of the Doppler spread estimates corresponding to the selected autocorrelation function hypothesis as an estimate of the Doppler spread of the communication channel Assumption selector.

According to the present invention, there is also provided a method for receiving a communication, the method comprising: receiving a signal on a communication channel, wherein the signal represents data from a remote transmitter; generating an estimate of the communication channel over which the signal was received ; Generate an autocorrelation function for the communication channel estimate; select one of a plurality of autocorrelation function assumptions corresponding to a corresponding plurality of Doppler spread estimation assumptions, so as to approximate the autocorrelation function for the communication channel estimate; select a corresponding the selected one of the assumed Doppler spread estimates of the autocorrelation function as an estimate of the Doppler spread of the communication channel; and reproducing the estimate of the data transmitted by the remote transmitter.

According to the present invention, a kind of receiver is also provided, comprising: a radio receiver and a transducer for receiving signals on a communication channel; a channel estimator for estimating the communication channel from signals received on the communication channel; and a respective A plurality of autocorrelation function assumptions corresponding to a plurality of Doppler expansion estimation assumptions; an autocorrelation generator for generating the autocorrelation function estimated by the communication channel; an autocorrelation function hypothesis tester for selecting one of these autocorrelation function assumptions an autocorrelation function for approximating the communication channel estimate; and a Doppler spread hypothesis selector for selecting one of the Doppler spread estimate assumptions corresponding to the selected autocorrelation function hypothesis as the Doppler spread for the communication channel Extended valuation.

Description of drawings

Fig. 1 is a block diagram of a communication system including a receiver according to the invention.

2-4 are block diagrams of receivers in accordance with the present invention.

Figure 5 is a block diagram of a Doppler spread estimator in accordance with the present invention.

Figure 6 is a diagram illustrating the radio channel autocorrelation function at receivers at different speeds relative to the base station.

FIG. 7 is a summary table illustrating the storage of samples of the autocorrelation function of FIG. 6. FIG.

Detailed ways

The present invention will now be described more fully hereinafter with reference to the accompanying drawings showing preferred embodiments of the invention. However, this invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Like numbers are assigned to like parts throughout.

FIG. 1 shows a transmitter T and a receiver R according to the invention, wherein data d is transmitted by the transmitter T via a radio channel h. The received signal r is a function of the transmitted data d, the radio channel h and the noise n. In flat fading channels:

                                                  (Equation 1)

As discussed above, receiver performance can be improved by estimating the Doppler spread and adapting the functionality of the receiver using the estimated Doppler spread. More specifically, the estimated Doppler spread can be used to more accurately estimate the radio channel h. The use of Doppler spread estimators is discussed in US Pat. No. 6,563,861, "DOPPLER SPREAD ESTIMATION SYSTEM," filed March 22, 1999, to Leonid Krasny et al. (DocKet No. P10367-RCVR and 1280.00105). The Krasny et al application is assigned to the assignee of the present invention and the Krasny et al application shares a common inventor with the present application. Furthermore, the disclosure of the Krasny et al. application is hereby incorporated by reference in its entirety.

Various receivers Ra, Rb and Rc including a Doppler spread estimator according to the invention are shown in FIGS. 2-4. In particular, the receiver Ra of FIG. 2 is adapted to use known pilot symbols and comprises an antenna 21a receiving the signal r, a radio receiver and converter 23a, a channel estimator 25a, Doppler spread estimator 27a, known symbol estimator 29a and signal processor 31a. The antenna 21a receives the radio signal r, and the radio receiver and converter 23a filters, amplifies and converts the received radio signal r into digital samples for processing. More specifically, the received radio signal can be transformed into a form suitable for processing, such as integrated digital sample values. The channel estimator receives the transformed radio signal and the known symbols from the known symbol estimator 29a.

In particular, the known symbol estimator 29a may provide pilot symbols or other known reference symbols, such as synchronization symbols included in the received signal r, which may be used to calculate the channel estimate. Furthermore, the known symbol estimator 29a may comprise a memory in which known symbols are stored, or a code generator capable of generating known symbols. Channel estimator 25a correlates the received digital samples with known symbols to provide an estimate of channel h which is used by Doppler spread estimator 27a. The Doppler spread estimator uses the channel estimate to estimate the Doppler spread and then sends the estimated Doppler spread to the signal processor 31a. The operation of the Doppler spread estimator will be discussed in more detail below. The signal processor 31a processes the sampled signal to extract information, the signal processor 31a provides feedback to the channel estimator 25a so that the channel estimate can be improved after the Doppler spread estimation.

The receiver Rb of Fig. 3 is adapted not to use known pilot symbols. The receiver comprises an antenna 21b for receiving the signal r, a radio receiver and converter 23b, a channel estimator 25b, a Doppler spread estimator 27b, a symbol estimator 29b and a signal processor 31b. Receiver Rb is similar to receiver Ra of FIG. 2, except that symbol estimator 29b is used instead of known symbol estimator 29a used in the receiver of FIG. The symbol estimator 29b may be used in applications where symbols are unknown. For example, symbol estimator 29b may use error detection and correction techniques to estimate received symbols with a high level of accuracy. These estimated symbols may then be used by channel estimator 25b to estimate channel h. As discussed in more detail below, the Doppler spread estimator 27b estimates the Doppler spread using the channel estimate.

In a Code Division Multiple Access (CDMA) cellular system, such as the IS-95 system, the transmitter sends a stream of known symbols called pilot codes. The pilot code is transmitted simultaneously on the same channel as the other information-bearing symbols using a different spreading code. Receiver Rc of Figure 4 provides an estimate of the Doppler spread in such a CDMA system. The receiver Rc of Figure 4 is adapted to use known pilot symbols and comprises an antenna 21c for receiving signal r, a radio receiver and converter 23c, a channel estimator 25c, a Doppler spread estimator 27c , and signal processor 31c. In such a CDMA receiver, the channel estimation can be performed directly by the channel estimator 25c without the known symbol estimator 29a of FIG. 2 or with the channel estimator 25c of the symbol estimator 29b of FIG. , and these channel estimates are used for Doppler spread estimates.

The channel estimator 25c correlates the received signal r including the known pilot code and other codes in an additive and superposition manner, and filters the obtained integrated correlation value to obtain a channel estimate. The received signal may also be correlated with other codes carrying information to be decoded. The result associated with the code-carrying information is multiplied by the conjugate value associated with the pilot code at the same delay, and the results are summed to combine the multipath signals in a coherent manner. In Wideband CDMA (WBCDMA) systems, the modulation symbol spacing can be very short, thus enabling multiple propagation paths to be resolved with finer time resolution.

The receivers of Figures 2-4 thus illustrate various receivers including a Doppler spread estimator in accordance with the present invention. In each receiver, the channel estimate is provided to a Doppler spread estimator for computing a Doppler spread estimate. A receiver comprising a Doppler spread estimator according to the present invention is not limited to the channel estimators discussed above, and those skilled in the art will recognize that any channel estimation technique may be used to provide the channel estimate.

In particular, the channel estimator may generate a channel estimate over a TDMA slot using symbols representing data samples received during the slot. For example, the receiver Rb of Fig. 3 may comprise a symbol estimator 29b which evaluates the symbols. The Doppler spread estimator can then compute the Doppler spread estimate for the current slot using the channel estimate for the current slot, and the signal processor can use this Doppler spread estimate in its calculations for the subsequent slot. value, such as computing the channel estimate for subsequent slots. In other words, the Doppler spread estimate for the current time slot can be used in the signal processor to update the long-term Doppler estimate used in subsequent calculations. For example, the long-term Doppler estimate may be updated using the averaging technique discussed in more detail below.

When a symbol estimator as discussed with reference to FIG. 3 is used, it may be useful to perform a cyclic redundancy check (CRC) on the estimated symbols used to calculate the Doppler spread estimate for the slot. If the cyclic redundancy check passes, the channel estimate should be relatively accurate, and the resulting Doppler spread estimate can be used to update the long-term Doppler estimate. However, if the cyclic redundancy check fails, the channel estimate may be unreliable, so it may not be desirable to update the long-term Doppler estimate with a Doppler spread estimate based on a potentially unreliable channel estimate.

The Doppler spread estimate will now be discussed in more detail with reference to the Doppler spread estimate of FIG. 5 . For example, a Doppler spread estimator may be used with a receiver of a radiotelephone communication system operating according to the DAMPS or DAMPS ⁺ standard. In DAMPS ⁺ , known boot symbols are provided. In DAMPS, with multiple demodulations, bits of level 1 demodulated for the first time can be used as pilot symbols if they pass a cyclic redundancy check (CRC). Preferably, the phase ambiguity between pilot slots is reduced to increase the reliability of the results. For example, a method for resolving phase ambiguities is discussed in reference, T. Fulghum: "Channel Interpolation On Secondpass Demodulation" (Tech. Rep. Tr/X 98: 1230, Ericsson, RTP, NC, Feb. 22, 1999). method, the disclosure of which is incorporated herein by reference in its entirety.

的样本。然后可以利用假设比较器53将自相关函数的样本与不同多普勒扩展值的实际自相关函数的不同假设(H₁，H₂，H₃，…H_k)相比较，对于相对每种假设的自相关函数样本提供误差计算(e₁(m)，e₂(m)，e₃(m)，…e_k(m))。然后可以利用平均器55在几个时隙上对每个误差计算进行平均，以提供各自的多个平均误差值(e_av，1，e_av，2，e_av，3，…e_av，k)。然后通过最小误差选择器57选出与得到最低平均误差值的假设对应的多普勒扩展，提供被估值的多普勒扩展

Assuming that pilot symbols are available and that the phase ambiguity between the pilot symbols has been reduced to an acceptable level, the autocorrelation function can be found by the autocorrelation calculator 51 using the channel estimate obtained from the pilot symbols

of samples. The samples of the autocorrelation function can then be compared with different hypotheses (H ₁ , H ₂ , H ₃ , ... H _k ) of the actual autocorrelation function for different values of Doppler spread using a hypothesis comparator 53, for each hypothesis A sample of the autocorrelation function of provides error calculations (e ₁ (m), e ₂ (m), e ₃ (m), . . . e _k (m)). Each error calculation can then be averaged over several time slots using an averager 55 to provide a respective plurality of averaged error values ( _eav,1 , _eav,2 , _eav,3 ,... _eav,k ). Then the Doppler spread corresponding to the assumption that the lowest average error value is obtained is selected by the minimum error selector 57, providing the estimated Doppler spread

In more detail, channel estimates can be determined using techniques available now or in the future, hypotheses (H ₁ , H ₂ , H ₃ , . . . H _k ) for different autocorrelation functions corresponding to different values of Doppler spread can be is calculated and stored in memory. In particular, the actual radio channel and the corresponding Doppler spread values can be determined at different speeds of the mobile terminal relative to the base station, and the resulting autocorrelation function can be calculated for each speed. A graphical example (correlation value pair τ) is shown in FIG. 6 for different autocorrelation function assumptions for radio channels measured at different speeds and for different Doppler spread values.

A sample of each autocorrelation function representing a respective hypothesis _Hk may be stored in memory for the hypothesis comparator 53, providing the hypotheses _H1 , _H2 , _H3 , . . . _Hk shown in Fig. 5 . For example, samples of each autocorrelation function in the graph of FIG. 6 corresponding to different values of τ may be stored in a memory as shown in FIG. 7 . Although 5 different hypotheses are shown in the diagram of Fig. 6, any number may be used, a greater number (k) of hypotheses may be used in order to provide higher precision in the estimation of the Doppler spread value. Whereas a smaller number (k) of hypotheses may provide fewer computations, less complex operations and lower memory requirements. Furthermore, the number of samples L stored for each hypothesis may vary depending on the level of accuracy desired for the Doppler spread estimate.

The autocorrelation function estimate for a channel with frequency error can be calculated using an autocorrelation estimator 51 using the channel estimate on the known domain:

${\hat{R}}_e\left(\mathrm{\τ}\right)=\frac{1}{L-\mathrm{\τ}}\underset{k=\mathrm{\τ}+1}{\overset{L}{\mathrm{\Σ}}}{\hat{h}}_e\left(k\right){\hat{h}}_e^{*}(k-\mathrm{\τ}).$ (equation 2)

In this equation, is the estimated autocorrelation function including the frequency error, is the channel estimate over the known domain (or pilot symbols). Note that when there is a frequency error in the received signal, which directly affects the channel estimate, it can be rotated by the amount of frequency error, thereby affecting the autocorrelation estimate. Denoting the frequency error as f _e , the rotation in the channel estimate can be computed as:

${\hat{h}}_e\left(k\right)=\hat{h}{e}^{j2\mathrm{\π}{f}_e\mathrm{kT}},$ (equation 3)

表示没有频率误差时的信道估值。因此带有频率误差的自相关函数可被写成：in

Indicates the channel estimate without frequency error. So the autocorrelation function with frequency error can be written as:

${\hat{R}}_e\left(\mathrm{\τ}\right)=\frac{1}{L-\mathrm{\τ}}\underset{k=\mathrm{\τ}+1}{\overset{L}{\mathrm{\Σ}}}\hat{h}\left(k\right)e^{j2\mathrm{\π}{f}_e\mathrm{kT}}{\hat{h}}^{*}(k-\mathrm{\τ})e^{j2\mathrm{\π}{f}_e(k-\mathrm{\τ})T},$ (equation 4)

or:

${\hat{R}}_e\left(\mathrm{\τ}\right)=\hat{R}\left(\mathrm{\τ}\right)e^{-j2\mathrm{\π}{f}_e\mathrm{\τT}},$ (equation 5)

是没有频率误差的自相关估值。in

is the autocorrelation estimate without frequency error.

The frequency can be calculated using known methods as discussed in the reference Morelli et al. error, the disclosure of which is hereby incorporated by reference in its entirety. The effect of frequency error can thus be removed from the correlation of the channel estimates.

$R\left(\mathrm{\τ}\right)=R_e\left(\mathrm{\τ}\right)e^{j2{\mathrm{\π}}_e\mathrm{\τT}},$ (equation 6)

The frequency error-free correlation estimate can then be compared with the different hypotheses (H ₁ , H ₂ , H ₃ , . . . H _k ) of the hypothesis comparator 53 .

的误差e_k(m)：The hypotheses (H ₁ , H ₂ , H ₃ , . . . H _k ) can be determined and stored in memory as discussed above with respect to FIGS. 6 and 7 . Calculate the autocorrelation estimates for each hypothesis using

The error e _k (m) of :

$e_k\left(m\right)=\underset{\mathrm{no}=1}{\overset{N}{\mathrm{\Σ}}}{|\hat{R}\left(\mathrm{no}\right)-\hat{R}\left(o\right)R_{h,k}\left(\mathrm{no}\right)|}^2.$ (Equation 7)

In this equation, e _k (m) is between the estimated autocorrelation function and the normalized true autocorrelation function R _H,k (n) given by Error corresponding to the m-th slot: R _H,k (n)=J ₀ (2πf _doppler,k nT), (Equation 8) where fdoppler,k is the k-th Doppler spread hypothesis. The error term _e is averaged over several time slots on the averager 55 (filter) using the following equation:

$e_{\mathrm{av},k}=\frac{1}{m}\underset{m=1}{\overset{m}{\mathrm{\Σ}}}{e}_k\left(m\right),$ (Equation 9)

where eav _,k is the average error corresponding to the kth hypothesis and M is the length of the averaging window. In this example, block averaging is used to average the errors. Alternatively, other averaging techniques such as running averaging or sliding window averaging may be used. In this way, the averaging technique reduces statistical error.

的估值。换句话说，平均误差的最小值e_av，1-e_av，k被用于选择得到最小平均误差(在最小误差选择器57上)的自相关函数假设，与所选的自相关函数假设对应的多普勒扩展假设被按以下等式选作多普勒扩展的估值(在多普勒扩展假设选择器59上)：The minimum error selector 57 determines the lowest average error _eav,k , selecting the autocorrelation function hypothesis _Hk that most closely approximates the autocorrelation function for the received channel. The Doppler extension hypothesis selector selects the Doppler extension hypothesis corresponding to the selected autocorrelation function hypothesis as the Doppler extension

valuation. In other words, the minimum value of the average error _eav,1 - _eav,k is used to select the autocorrelation function hypothesis that results in the minimum average error (on the minimum error selector 57), corresponding to the selected autocorrelation function hypothesis The Doppler spread hypothesis of is chosen as the estimate of Doppler spread according to the following equation (on Doppler spread hypothesis selector 59):

${\hat{f}}_{\mathrm{doppler}}={\arg }_{f_{\mathrm{doppler},k}^{\min }}{e}_{\mathrm{av},k}.$ (equation 10)

Alternatively, the autocorrelation function with frequency error can be calculated as discussed in the Morelli et al. reference. Instead of finding the frequency error and removing it from the estimated autocorrelation function, we can take the absolute value of the estimated autocorrelation, obtaining the enveloped autocorrelation function as:

$\tilde{R}\left(\mathrm{\τ}\right)=\left|\hat{R}\left(\mathrm{\τ}\right)\right|=\left|\hat{R}\left(\mathrm{\τ}\right)e^{-f2\mathrm{\π}{f}_e\mathrm{\τT}}\right|=\left|\hat{R}\left(\mathrm{\τ}\right)\right|.$ (Equation 11)

This absolute value of the autocorrelation function estimate (without frequency error) can then be compared with different assumptions of the actual envelope autocorrelation function corresponding to different values of Doppler spread as follows:

$e_k\left(m\right)=\underset{\mathrm{no}=1}{\overset{N}{\mathrm{\Σ}}}{\left|\tilde{R}\left(\mathrm{no}\right)-\tilde{R}\left(o\right){\tilde{R}}_{h,k}\left(\mathrm{no}\right)\right|}^2$ (Equation 12)

in,

${\tilde{R}}_{h,k}\left(\mathrm{no}\right)=\left|J_0\left({2\mathrm{\πf}}_{\mathrm{doppler},k}n\right)\right|.$ (Equation 13)

The error term is used as before to determine the assumption that provides the smallest error in the estimate of the Doppler spread value.

In a DAMPS system, phase ambiguity can be substantially removed within a time slot using known techniques. However, phase ambiguity across slots may still exist. According to the invention, the correlation within a time slot can be estimated and compared with different hypotheses. However, the errors across time slots are averaged.

As discussed previously, the number of hypotheses can vary depending on the desired accuracy and complexity of the Doppler spread estimator and receiver. A higher number of hypotheses can provide higher accuracy while requiring more memory and computation. A smaller number of assumptions can provide less accurate estimates of Doppler spread by reducing memory usage and computation.

Therefore, the Doppler spread estimation method according to the invention can be implemented with high computational efficiency and good results. In particular, using an envelope correlation function can provide performance levels close to other techniques that do not require frequency estimation.

Doppler estimation, according to the invention, can be carried out, for example, within the scope of the DAMPS cellular radiotelephone system using the following downlink time slots and transmission formats: π/4-DQPSK with root raised cosine pulse formation; 20 ms of TDMA frames; and 3 users sharing TDMA frames. Also, each user can transmit twice during a frame with a slot duration of 6.667 ms. The transmission medium can be a Rayleigh fading channel which can be simulated using the Jake fading model. A single antenna receiver and a carrier frequency of 1900MHz can be used.

In downlink joint demodulation, only one Doppler spread estimate is needed for either the desired or the interfering signal, since both the desired and the interfering base station are fixed and the mobile terminal is moving. Assuming there is no direct path is a reasonable assumption. The Doppler spread can thus be estimated using the information corresponding to the stronger user, which in most cases is the desired user.

In the receiver, it can be assumed that the ideal synchronization position is known and the received samples can be used to coherently demodulate the user information sequence. The Kalman method based on channel estimation using a second-order autoregressive model (AR-2) can be used for channel tracking. As discussed above, channel tracking depends on Doppler information. Since the Doppler information may not be known initially, the tracker parameters may initially be set to a higher Doppler spread of 100 Hz. Since the Doppler spread estimate converges to the actual Doppler spread, the tracker parameters should also be changed in the adaptive receiver.

The invention can be implemented as a method or an apparatus. Additionally, the present invention can take the form of an entirely hardware embodiment, an entirely software embodiment or a combination of hardware and software embodiment. The invention has been described in the section relating to the block diagrams of Figures 1-5. It will be understood that each illustrated block, and combinations of blocks, can be implemented by computer program instructions. These program instructions, which may represent steps, may be provided to a processor to create a machine.

Accordingly, blocks in the block diagrams support various combinations of means for achieving the specified functions, in combinations of steps for achieving the specified functions. It will be understood that each illustrated block, and combinations of blocks, can be implemented by special purpose hardware-based systems which perform specific functions or steps, or combinations of special purpose hardware and computer instructions.

In the drawings and specification, there have been disclosed typical preferred embodiments of the invention. While specific terms are employed, they are used in a generic and descriptive sense only and not for purposes of limitation, the scope of the invention being set forth in the following claims.

Claims (57)

1. A method for estimating the Doppler spread of a communication channel, the method comprising: provide an estimate of the communication channel; an autocorrelation function producing an estimate of the communication channel; selecting one of a plurality of autocorrelation function assumptions to approximate the autocorrelation function of the communication channel estimate, wherein each of the autocorrelation function assumptions corresponds to a respective Doppler spread estimation assumption; and One of these Doppler spread estimate assumptions corresponding to the selected autocorrelation function assumption is selected as the Doppler spread estimate for the communication channel. 2. The method according to claim 1, wherein selecting one of the autocorrelation function hypotheses comprises comparing the autocorrelation function of the communication channel estimate with each of the plurality of autocorrelation function hypotheses. 3. The method according to claim 1, wherein selecting one of the autocorrelation function hypotheses comprises selecting one of the plurality of autocorrelation function hypotheses that most closely approximates the autocorrelation function of the communication channel estimate. 4. The method according to claim 1, wherein selecting one of the autocorrelation function assumptions comprises: generating a plurality of error signals respectively corresponding to the plurality of autocorrelation function hypotheses, wherein each error signal represents the difference between the corresponding autocorrelation function hypothesis and the autocorrelation function of the communication channel estimate; and The error signals are compared to select an autocorrelation hypothesis that approximates the autocorrelation function of the communication channel estimate. 5. The method according to claim 4, wherein comparing the error signals includes selecting the error signal representing the smallest difference between the corresponding autocorrelation function hypothesis and the autocorrelation function of the communication channel estimate. 6. A method according to claim 5, wherein selecting the error signal comprises selecting the smallest of the error signals. 7. The method according to claim 4, wherein before comparing the error signals: Each error signal is averaged to provide an average error signal, wherein comparing the error signals includes comparing the average error signals. 8. The method according to claim 7, wherein averaging each of the error signals comprises one of block averaging, running averaging and sliding window averaging. 9. The method according to claim 1, wherein each of the plurality of autocorrelation function hypotheses comprises a plurality of samples, and wherein the autocorrelation function for the communication channel estimate comprises a plurality of samples. 10. The method according to claim 1, wherein the communication channel comprises a radio channel. 11. The method according to claim 1, wherein the autocorrelation function includes a plurality of samples, and wherein generating the autocorrelation function includes reducing frequency error of the plurality of samples. 12. The method according to claim 1, further comprising: A second estimate of the communication channel is provided using an estimate of the Doppler spread of the communication channel. 13. The method according to claim 1, wherein providing the communication channel estimate comprises receiving a plurality of data samples over the communication channel, wherein the data samples are used to generate the channel estimates, the method further comprising: performing a cyclic redundancy check on the data samples used to generate the channel estimate; When the data samples pass the cyclic redundancy test, the Doppler expansion estimate is used to update the long-term Doppler estimate. 14. The method according to claim 1, wherein providing an estimate of the communication channel comprises: receiving a plurality of pilot symbols over the communication channel; reduce phase ambiguity between pilot symbols; and An estimate of the communication channel is provided using the reduced ambiguity pilot symbols. 15. A Doppler spread estimator for estimating Doppler spread of a communication channel, the Doppler spread estimator comprising: a plurality of autocorrelation function assumptions corresponding to respective plurality of Doppler spread estimation assumptions; a channel estimator for estimating the communication channel; an autocorrelation generator for generating an autocorrelation function of the communication channel estimate; an autocorrelation function hypothesis tester that selects one of the autocorrelation function hypotheses that approximate the autocorrelation function of the communication channel estimate; and A Doppler spread hypothesis selector that selects one of the Doppler spread estimates corresponding to the selected autocorrelation function hypothesis as an estimate of the Doppler spread for the communication channel. 16. The Doppler spread estimator according to claim 15, wherein the hypothesis tester compares the autocorrelation function of the communication channel estimate with each of the plurality of autocorrelation function hypotheses. 17. The Doppler spread estimator according to claim 15, wherein the hypothesis tester selects one of the plurality of autocorrelation function hypotheses that most closely approximates the autocorrelation function of the communication channel estimate. 18. The Doppler spread estimator according to claim 15, wherein the hypothesis tester generates a plurality of error signals respectively corresponding to the plurality of autocorrelation function hypotheses, wherein each error signal among these error signals is represented in the difference between the corresponding autocorrelation function hypothesis and the autocorrelation function of the communication channel estimate, and wherein the hypothesis tester compares the error signals to select an autocorrelation approximating the autocorrelation function of the communication channel estimate assumption. 19. The Doppler spread estimator according to claim 18, wherein the hypothesis tester selects the error signal representing the smallest difference between the corresponding autocorrelation function hypothesis and the communication channel estimated autocorrelation function. 20. The Doppler spread estimator according to claim 19, wherein the hypothesis tester selects the smallest one of the error signals. 21. The Doppler spread estimator according to claim 18, wherein the hypothesis tester averages each of the error signals to provide an average error signal, wherein the hypothesis tester compares the average error signals . 22. The Doppler spread estimator according to claim 21, wherein the hypothesis tester averages each of the error signals using one of block averaging, running averaging and sliding window averaging. 23. The Doppler spread estimator according to claim 15, wherein each of the plurality of autocorrelation function hypotheses comprises a plurality of samples, and the autocorrelation function of the communication channel estimate comprises a plurality of samples. 24. The Doppler spread estimator according to claim 15, wherein the communication channel comprises a radio channel. 25. The Doppler spread estimator according to claim 15, wherein the autocorrelation function comprises a plurality of samples, and the autocorrelation generator reduces the frequency error of the plurality of samples. 26. The Doppler spread estimator according to claim 15, wherein the channel estimator uses an estimate of the Doppler spread of the communication channel to provide a second estimate of the communication channel. 27. The Doppler spread estimator according to claim 15, wherein the channel estimator receives a plurality of data samples via the communication channel, wherein the data samples are used to estimate the communication channel, wherein the channel estimator is used for The Doppler spread estimator also includes: A long-term Doppler estimator, wherein the long-term Doppler estimator is updated with an estimate of the Doppler extension when the data samples pass the cyclic redundancy check. 28. The Doppler spread estimator according to claim 15, wherein the channel estimator receives a plurality of pilot symbols through the communication channel, reduces the phase ambiguity between the pilot symbols and utilizes the reduced ambiguity pilot symbols to An estimate of the communication channel is provided. 29. A method for receiving communications, the method comprising: receiving a signal on the communication channel, wherein the signal represents data from a remote transmitter; generating an estimate of the communication channel over which the signal was received; an autocorrelation function that produces an estimate of the communication channel; selecting one of a plurality of autocorrelation function hypotheses corresponding to a corresponding plurality of Doppler spread estimate hypotheses to approximate the autocorrelation function of the communication channel estimate; selecting one of the Doppler spread estimate hypotheses corresponding to the selected autocorrelation function hypothesis as the Doppler spread estimate for the communication channel; and An estimate of the data sent by the remote transmitter is reproduced. 30. The method according to claim 29, wherein selecting one of the autocorrelation function hypotheses comprises comparing the autocorrelation function of the communication channel estimate with each of the plurality of autocorrelation function hypotheses. 31. The method according to claim 29, wherein selecting one of the autocorrelation function hypotheses comprises selecting one of the plurality of autocorrelation function hypotheses that most closely approximates the autocorrelation function of the communication channel estimate. 32. The method according to claim 29, wherein selecting one of the autocorrelation function assumptions comprises: generating a plurality of error signals respectively corresponding to the plurality of autocorrelation function hypotheses, wherein each error signal represents a difference between a corresponding autocorrelation function hypothesis and an autocorrelation function of the communication channel estimate; and These error signals are compared to select an autocorrelation hypothesis that approximates the autocorrelation function of the communication channel estimate. 33. A method according to claim 32, wherein comparing the error signals includes selecting the error signal representing the smallest difference between the corresponding autocorrelation function hypothesis and the communication channel estimate's autocorrelation function. 34. A method according to claim 33, wherein selecting the error signal comprises selecting the smallest one of the error signals. 35. The method according to claim 32, wherein before comparing the error signals: Each error signal is averaged to provide an average error signal, wherein comparing the error signals includes comparing the average error signals. 36. The method of claim 35, wherein averaging each error signal comprises one of block averaging, running averaging, and sliding window averaging. 37. The method according to claim 29, wherein each of the plurality of autocorrelation function hypotheses comprises a plurality of samples, and wherein the autocorrelation function of the communication channel estimate comprises a plurality of samples. 38. The method according to claim 29, wherein the communication channel comprises a radio channel. 39. The method of claim 29, wherein the autocorrelation function includes a plurality of samples, and generating the autocorrelation function includes reducing a frequency error of the plurality of samples. 40. The method according to claim 29, further comprising: A second estimate of the communication channel is provided using an estimate of the Doppler spread of the communication channel. 41. The method according to claim 29, wherein providing the communication channel estimate comprises receiving a plurality of data samples over the communication channel, wherein the data samples are used to generate the channel estimate, the method further comprising: performing a cyclic redundancy check on the data samples used to generate the channel estimate; When the data samples pass the cyclic redundancy test, the Doppler expansion estimate is used to update the long-term Doppler estimate. 42. The method according to claim 29, wherein the data received from the remote transmitter includes pilot symbols, and wherein generating the communication channel estimate comprises: reduce phase ambiguity between these pilot symbols; and These pilot symbols with reduced ambiguity are utilized to provide communication channel estimates. 43. A receiver comprising: radio receivers and transducers for receiving signals on communication channels; a channel estimator for estimating the communication channel from signals received on the communication channel; a plurality of autocorrelation function assumptions corresponding to respective plurality of Doppler spread estimation assumptions; an autocorrelation generator for generating an autocorrelation function of the communication channel estimate; an autocorrelation function hypothesis tester that selects one of the autocorrelation function hypotheses in order to approximate the autocorrelation function of the communication channel estimate; and The Doppler spread hypothesis selector selects one of the Doppler spread estimate hypotheses corresponding to the selected autocorrelation function hypothesis as the Doppler spread estimate of the communication channel. 44. The receiver of claim 43, wherein the hypothesis tester compares the autocorrelation function of the communication channel estimate to each of the plurality of autocorrelation function hypotheses. 45. The receiver of claim 43, wherein the hypothesis tester selects one of the plurality of autocorrelation function hypotheses that most closely approximates the autocorrelation function of the communication channel estimate. 46. The receiver according to claim 43, wherein the hypothesis tester generates a plurality of error signals respectively corresponding to the plurality of autocorrelation function hypotheses, wherein each error signal represents a difference between the corresponding autocorrelation function hypothesis and the communication channel estimate and wherein the hypothesis tester compares the error signals to select an autocorrelation hypothesis that approximates the autocorrelation function of the communication channel estimate. 47. A receiver according to claim 46, wherein the hypothesis tester selects the error signal representing the smallest difference between the corresponding autocorrelation function hypothesis and the autocorrelation function of the communication channel estimate. 48. A receiver according to claim 47, wherein the hypothesis tester selects the smallest one of the error signals. 49. The receiver of claim 46, wherein the hypothesis tester averages each error signal to provide an average error signal, wherein the hypothesis tester compares the average error signals. 50. The receiver of claim 49, wherein the hypothesis tester averages each error signal using one of block averaging, running averaging, and sliding window averaging. 51. A receiver according to claim 43, wherein each of the plurality of autocorrelation function hypotheses comprises a plurality of samples, and the autocorrelation function of the communication channel estimate comprises a plurality of samples. 52. The receiver according to claim 43, further comprising: A signal processor coupled between the Doppler spread estimator and the channel estimator, wherein the signal processor modifies the operation of the channel estimator based on the estimate of the Doppler spread of the communication channel. 53. A receiver according to claim 43, wherein the communication channel comprises a radio channel. 54. The receiver according to claim 43, wherein the autocorrelation function of the communication channel estimate comprises a plurality of samples, and the autocorrelation generator reduces the frequency error of the plurality of samples. 55. A receiver according to claim 43, wherein the channel estimator provides a second estimate of the communication channel using an estimate of the Doppler spread of the communication channel. 56. The receiver according to claim 43, wherein the signal received on the communication channel comprises a plurality of data samples, wherein the channel estimator uses the data samples to estimate the communication channel, wherein the channel estimator is used for A cyclic redundancy check is performed on the data samples from which the channel estimate is generated, the receiver further comprising: A long-term Doppler estimator, wherein the long-term Doppler estimator is updated with the Doppler expansion estimate when the data samples pass the cyclic redundancy check. 57. The receiver according to claim 43, wherein the signal received on the communication channel comprises a plurality of pilot symbols, wherein the channel estimator reduces the phase ambiguity between the pilot symbols, and the channel estimator utilizes These pilot symbols that reduce phase ambiguity provide an estimate of the communication channel.

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