WO2018010778A1

WO2018010778A1 - Beam computing device, transmitting device and methods thereof

Info

Publication number: WO2018010778A1
Application number: PCT/EP2016/066601
Authority: WO
Inventors: Majid NASIRI KHORMUJI; Renaud-Alexandre PITAVAL
Original assignee: Huawei Technologies Co., Ltd.
Priority date: 2016-07-13
Filing date: 2016-07-13
Publication date: 2018-01-18

Abstract

The present invention relates to a beam computing device for beam scanning (100) in a wireless communication system (500). The beam computing device (100) comprises a processor (102) configured to obtain a statistical property for a first transmission angle associated with a transmitted beam, determine a second transmission angle based on the statistical property for the first transmission angle, compute a second beam for a second beam scanning transmission based on the second transmission angle. Furthermore, the present invention also relates to corresponding methods, a computer program, and a computer program product.

Description

BEAM COMPUTING DEVICE, TRANSMITTING DEVICE AND METHODS THEREOF

Technical Field

The present invention relates to a beam computing device and to a transmitting device comprising such a beam computing device. Furthermore, the present invention also relates to corresponding methods, a computer program, and a computer program product.

Background

Beam scanning is an appealing approach proposed for massive multiple input multiple output (mMIMO) communications. With beam scanning, the transmitter creates and radiates multiple beams in different directions to find the best transmission direction through feedback from the receiver side. In mMIMO systems, when the number of antennas increases, the transmitter becomes capable of generating narrow beams that enable so-called pencil beamforming. The pencil beamforming provides high signal to noise ratio (SNR) links due to the fact the transmitted energy is concentrated in a desirable direction. In addition, communication through pencil beamforming creates less interference on unintended neighbouring nodes as the transmitted energy is mostly directed toward the indented receiver. The beam scanning can be used for applications including frequency division duplex (FDD) mMIMO as well as millimetre wave (mmW) communications where the channel is very directional. That is, the channel response contains some main directions, and often a line-of-sight (LoS) component is present.

A common solution for beam scanning is to divide the range of the angles uniformly and to construct beams whose main lobes are located at the uniformly selected angles in the given range. Normally it is also desirable to optimize the width of the main lobe such that the set of beams cover a range of angles to avoid outage. Assuming that the channel response is highly directional with departure angles ranging from <p_min to (p_max , i.e., any transmission outside of this range is considered too attenuated to be detectable by the receiver. The parameters <p_min and <p_max can also be decided a priori in some cases, e.g. in sectorized cellular system in which each sector covers a certain range of transmission angles. Otherwise, without any prior information, the parameters can be pre-set, such that <p_min = -π to <p_max = π.

In one conventional solution, beams are constructed with fixed width that depends only on the number of transmit antennas and independent of the size of the beam codebook used for generating the beams. The advantage of this conventional solution is to provide the maximum beamforming gain when the angles are estimated exactly. Another advantage is that the beams can be created with a low complexity operation where one phase-shift per antenna is required to create t e beams. This conventional beam scanning method constructs M beams with their main lobes separated by -^ ((p_max - <Pmin) from one another. For the transmission of these M beams, at least M pilot symbols are required and the feedback required for reporting the strongest beam equals to log₂ M bits.

A drawback of using codebooks is that thin beams are more prone to detection outage. For this reasons, methods to construct also wide beams (a.k.a. broad beams) have been described in other conventional solutions. Such methods are often used in a multi-resolution algorithm and are targeted to avoid detection outage in the first phases of the algorithm.

One conventional solution to create wide beams consists of the following steps:

1) construct a larger codebook with thinner resolution.

2) assign an angular sector for each desired broad beam.

3) combine high-resolution beams in each sector to form broad beams.

In step 3) the most common combining strategy is to directly make an arithmetic average, i.e. equal gain combining of the high-resolution beams. An advantage of this method is that this method only requires phase-shift that can be implemented with an analogue circuit with a limited number of radio-frequency chains. The beams generated by this method always satisfy a good angular coverage at the cost of power fluctuation in the assigned sector. It is also possible to employ more sophisticated combining methods to flatten the beam at cost of much higher complexity.

Summary

An objective of embodiments of the present invention is to provide a solution which mitigates or solves the drawbacks and problems of conventional solutions.

An "or" in this description and the corresponding claims is to be understood as a mathematical OR which covers "and" and "or", and is not to be understand as an XOR (exclusive OR).

The indefinite article "a" in this disclosure and claims is not limited to "one" and can also be understood as "one or more", i.e., plural.

The above and further objectives are solved by the subject matter of the independent claims. Further advantageous implementation forms of the present invention can be found in the dependent claims. According to a first aspect of t e invention, the above mentioned and other objectives are achieved with a beam computing device for beam scanning in a wireless communication system, the beam computing device comprising a processor configured to

obtain a statistical property for a first transmission angle associated with a transmitted beam,

determine a second transmission angle based on the statistical property for the first transmission angle,

compute a second beam for a second beam scanning transmission based on the second transmission angle.

The beam computing device according to the first aspect provides a number of advantages over conventional solutions. One such advantage is that the beams for beam scanning according to the first aspect, in contrast to conventional solutions, is adapted to the statistical distribution of the angles. This provides a higher rate link transmission when the emitted angles are concentrated toward main angles considered in the beam scanning design. This can also reduce the feedback overhead for reporting the strongest beam since the size of the beams codebook can be adapted to the statistical properties. In a first implementation form of a beam computing device according to the first aspect, the statistical property for the first transmission angle is a cumulative distribution function, and wherein the processor is configured to

partition the cumulative distribution function into M number of intervals, wherein M is a positive integer larger than or equal to 1 ;

determine the second transmission angle based on the partitioned cumulative distribution function.

The first implementation form provides the statistical information necessary to design the codebook by partitioning the cdf into M number of intervals wherein the angles of the beams are adjusted toward the cdf, which provides a better link budget (e.g. SNR) which enables a higher rate transmission. Choice of M also affects the number of feedback overhead necessary to report the beam indices from the receiver which can be decreased by adapting the codebook to the cdf. Thus overhead can be also adapted to the statistical properties. In a second implementation form of a beam computing device according to the first implementation form of the first aspect, the processor is configured to partition the cumulative distribution function into the M number of intervals so that first transmission angles associated to each interval have the same probability.

Generally, the statistical property may be any of: an angular range, a mean, a variance, and a probability density function, and a cumulative distribution function.

The second implementation form enables the beams constructed for each interval to equally cover the angular range for the beam codebook in order to minimize the outage probability. In a third implementation form of a beam computing device according to the first or second implementation form of the first aspect, the processor is configured to

determine a second transmission angle for each interval based on the partitioned cumulative distribution function,

compute a second beam for each interval for the second beam forming transmission based on the second transmission angle for each interval.

The third implementation form enables the selected angles to cover the entire angular range and to minimize the outage probability of the beam scanning. In a fourth implementation form of a beam computing device according to the third implementation form of the first aspect, the processor is configured to

determine the second transmission angle for each interval based on the inverse of the cumulative distribution function of a centre for each interval. The fourth implementation form provides a beam computation strategy in which the main lobe of the beams samples the angular range such that with a high probability the beam codebook covers the entire angular range.

In a fifth implementation form of a beam computing device according to the first or second implementation form of the first aspect, the processor is configured to

quantize each interval into a plurality of sub-intervals,

determine a second transmission angle for each quantized sub-interval,

compute the second beam for the second beam scanning transmission based on the second transmission angles for the quantized sub-intervals.

The fifth implementation form enables to formation of broad beams for each interval by quantizing each interval into a plurality of sub-intervals. In this way, the computed beams comprises main lobes that are spanned over all sub-intervals which broadens the beam. In other words, this implementation form can optimize and adapt the width of the beams for beam scanning codebooks. This reduces the outage probability since the computed beams emit the energy in a larger width.

In a sixth implementation form of a beam computing device according to the fifth implementation form of the first aspect, the processor is configured to

compute an intermediate beam for each quantized sub-interval based on the second transmission angle for each quantized sub-interval,

compute the second beam for the second beam scanning transmission based on the intermediate beams, wherein each beam for an interval is a linear combination of the intermediate beams for the quantized sub-intervals of the interval.

The sixth implementation form provides a low complexity design for computing the broad beams in which intermediate beams that are potentially narrower are combined to generate a broader beam.

In a seventh implementation form of a beam computing device according to the fifth or sixth implementation form of the first aspect, the quantization of each interval into a plurality of sub- intervals is a uniform quantization.

The seventh implementation form provides a uniform sampling of the cdf to provide a beam codebook that with high probability covers all angles. In an eight implementation form of a beam computing device according to any of the preceding implementation forms of the first aspect or to the first aspect as such, wherein the processor is configured to

obtain the statistical property from a first beam scanning transmission preceding the second beam scanning transmission.

The eight implementation form provides a means to update the beam codebook by acquiring the statistical information from the earlier beam scanning transmission. Thus the beam scanning remains updated based on the collected statistics from earlier beam scanning transmissions which enhances the transmission rates further. In a ninth implementation form of a beam computing device according to any of the eight implementation form of the first aspect, the processor is configured to

obtain a feedback indicating channel state information associated with the transmitted beam in the first beam scanning transmission,

obtain the statistical property based on the obtained feedback.

The ninth implementation form by using the feedback from the receiver enables the collection of the statistical properties to update the beam codebook for beam scanning transmission. In a tenth implementation form of a beam computing device according to any of the eight or ninth implementation form of the first aspect, the first beam scanning transmission is an initial beam scanning transmission, and wherein the transmitted beam in the initial beam scanning transmission is uniformly distributed over a predetermined transmission angular range. The tenth implementation form ensures to minimize the outage probability at the absence of any prior statistical information by starting the beam scanning with a codebook where the initial uniform distribution is used.

In an eleventh implementation form of a beam computing device according to any of the preceding implementation forms of the first aspect or to the first aspect as such, the processor is configured to obtain the statistical property by

receiving the statistical property from a network node.

The eleventh implementation form ensures the flexibility in the design where the statistical information could be found and informed using another network node in the systems. This is particularly useful to reduce the interference and to increase the link budget.

According to a second aspect of the invention, the above mentioned and other objectives are achieved with a transmitting device for a Multiple-Input Multiple-Output, MIMO, communication system, the transmitting device comprising a beam computing device according to any of the preceding claims.

According to a third aspect of the invention, the above mentioned and other objectives are achieved with a method comprising:

obtaining a statistical property for a first transmission angle associated with a transmitted beam, determining a second transmission angle based on the statistical property for the first transmission angle,

computing a second beam for a second beam scanning transmission based on the second transmission angle.

In a first implementation form of a method according to the third aspect, the statistical property for the first transmission angle is a cumulative distribution function, and the method comprises partitioning the cumulative distribution function into M number of intervals, wherein M is a positive integer larger than or equal to 1 ;

determining the second transmission angle based on the partitioned cumulative distribution function.

In a second implementation form of a method according to the first implementation form of the third aspect, the method comprises

partitioning the cumulative distribution function into the M number of intervals so that first transmission angles associated to each interval have the same probability.

In a third implementation form of a method according to the first or second implementation form of the third aspect, the method comprises

determining a second transmission angle for each interval based on the partitioned cumulative distribution function,

computing a second beam for each interval for the second beam forming transmission based on the second transmission angle for each interval.

In a fourth implementation form of a method according to the third implementation form of the third aspect, the method comprises

determining the second transmission angle for each interval based on the inverse of the cumulative distribution function of a center for each interval.

In a fifth implementation form of a method according to the first or second implementation form of the third aspect, the method comprises

quantizing each interval into a plurality of sub-intervals,

determining a second transmission angle for each quantized sub-interval, computing t e second beam for the second beam scanning transmission based on the second transmission angles for the quantized sub-intervals.

In a sixth implementation form of a method according to the fifth implementation form of the third aspect, the method comprises

computing an intermediate beam for each quantized sub-interval based on the second transmission angle for each quantized sub-interval,

computing the second beam for the second beam scanning transmission based on the intermediate beams, wherein each beam for an interval is a linear combination of the intermediate beams for the quantized sub-intervals of the interval.

In a seventh implementation form of a method according to the fifth or sixth implementation form of the third aspect, the quantization of each interval into a plurality of sub-intervals is a uniform quantization.

In an eight implementation form of a method according to any of the preceding implementation forms of the third aspect or to the third aspect as such, the method comprises

obtaining the statistical property from a first beam scanning transmission preceding the second beam scanning transmission.

In a ninth implementation form of a method according to any of the eight implementation form of the third aspect, the method comprises

obtaining a feedback indicating channel state information associated with the transmitted beam in the first beam scanning transmission,

obtaining the statistical property based on the obtained feedback.

In a tenth implementation form of a method according to any of the eighth or ninth implementation form of the third aspect, the first beam scanning transmission is an initial beam scanning transmission, and wherein the transmitted beam in the initial beam scanning transmission is uniformly distributed over a predetermined transmission angular range.

In an eleventh implementation form of a method according to any of the preceding implementation forms of the third aspect or to the third aspect as such, wherein the statistical property is obtained by

receiving the statistical property from a network node. The advantages of any method according to the third aspect is the same as those for the corresponding beam computing device according to the first aspect.

Embodiments of the invention also relates to a computer program, characterized in code means, which when run by processing means causes said processing means to execute any method according to the present invention. Further, the invention also relates to a computer program product comprising a computer readable medium and said mentioned computer program, wherein said computer program is included in the computer readable medium, and comprises of one or more from the group: ROM (Read-Only Memory), PROM (Programmable ROM), EPROM (Erasable PROM), Flash memory, EEPROM (Electrically EPROM) and hard disk drive.

Further applications and advantages of the present invention will be apparent from the following detailed description.

Brief Description of the Drawings

The appended drawings are intended to clarify and explain different embodiments of the present invention, in which:

Fig. 1 shows a beam computing device according to an embodiment of the invention.

Fig. 2 shows a flow chart of a method according to an embodiment of the invention.

Fig. 3 shows a transmitting device according to an embodiment of the invention.

Fig. 4 illustrates the interaction between a transmitting device and a receiving device according to an embodiment of the invention.

Fig. 5 shows a flow chart of an embodiment of the invention.

Fig. 6 illustrates cdf-partitioning.

Fig. 7 shows a flow chart of an embodiment of the invention.

Fig. 8 illustrates signalling aspects according to embodiments of the invention.

Figs. 9 and 10 show examples of radiation patterns of the beams in the beam codebook according to embodiments of the invention.

Figs. 1 1 and 12 show performance results for embodiments of the invention.

Detailed Description

Fig. 1 shows a beam computing device 100 according to an embodiment of the invention. The beam computing device 100 comprises a processor 102 configured to obtain at least one statistical property for at least one first transmission angle associated with at least one transmitted beam. The terminology "at least one" in this context has the same meaning as "one or more". The processor 102 is further configured to determine a second transmission angle based on the statistical property for the first transmission angle. The processor 102 is further configured to compute a second beam for a second beam scanning transmission based on the second transmission angle.

Fig. 2 shows a corresponding method 200 which may be executed in a beam computing device 100, such as the one shown in Fig. 1 . The method 200 comprises obtaining 202 a statistical property for a first transmission angle associated with a transmitted beam. The method 200 further comprises determining 204 a second transmission angle based on the statistical property for the first transmission angle. The method 200 further comprises computing 206 a second beam for a second beam scanning transmission based on the second transmission angle.

The beam computing device 100 may be a standalone device in an embodiment. However, the beam computing device 100 may in another embodiment be partially or fully integrated in another device. Fig. 3 shows an embodiment in which the computing device 100 is partially or fully integrated in a transmitting device 300. As shown in Fig. 3 the transmitting device 300 comprises a beam computing device 100 according to embodiments of the invention. The transmitting device 300 further comprises a transceiver 304 communicably coupled with the processor 102 of the beam computing device 100 with communication means 308. The transmitting device 300 further comprises an antenna 306 coupled to the transceiver 304 for transmissions in a wireless communication system 500. The transmitting device 300 may e.g. be or be part of a network node, such as a base station. Generally, embodiments of the invention disclose a solution to construct codebooks corresponding to beams (i.e. beam codebooks) to be used for beam scanning in a wireless communication system 500. The beam codebook is computed based on the statistical properties of the transmission angles e.g. used in one or more previous beam scanning transmissions.

In a high-level design the statistical properties of the transmission angles include any of mean, variance, pdf and cdf. The mentioned statistical properties are used to find main lobes or the border of the main lobes for beam scanning and then to construct beams for further beam scanning transmissions. Note that for a number of statistical distributions, determining the variance and mean of the random variables (i.e. the beams in this case) gives a closed-form description of the cdf if the type of the cdf is already estimated. Cdf-fitting over the statistical data can used to find an appropriate cdf which to be used to determine the beam codebook. In one embodiment, the transmission angles are found using the inverse of the cdf of the angles, wherein the cdf is uniformly partitioned. The statistical properties can be found by operation of the transmitting device 300 with an initial base beam codebook.

The transmitted beams may in an embodiment have been transmitted in a first beam scanning transmission preceding the second beam scanning transmission.

In a further embodiment, the processor 102 of the beam computing device 100 is configured to obtain at least one feedback indicating channel state information associated with the transmitted beam in the first beam scanning transmission, and to obtain the statistical property based on the obtained feedback. This embodiment provides an updating mechanism based on feedback received from at least one receiver of beam scanning transmissions. In one example, the first beam scanning transmission is an initial beam scanning transmission. Further, the transmitted beam in the initial beam scanning transmission is uniformly distributed over a predetermined transmission angular range <p_max - <p_min.

With reference to Fig. 4, the updating mechanism is based on the insight of using statistical properties of transmitted beams in beam scanning transmission to design and update the beam codebook for further beam scanning transmissions. The statistical properties can be obtained by operation of the radio network during a time interval using an initial base beam codebook. The optimal base beam codebook at the absence of any prior angular information is uniform in the angular domain to cover all angles in order to minimize the outage performance. With reference to Fig. 4, the network node 300 which comprises the present beam computing device 100 starts beam scanning transmissions 502 and receives feedback 504 from a user device 400 (or any other network node not shown in Fig. 4) to indicate the most suitable beam for data transmission. The most suitable beam is the beam that e.g. provides the highest Channel Quality Index (CQI), SNR, or signal to noise plus interference ratio (SINR). The data transmission using the initial base beam codebook, during a time interval creates a history of transmission angles which can be used to obtain statistical properties of the transmitted beams in the beam codebook. By using the obtained statistical properties one can create and update the beam codebook. The network node 300 may be an access network node, or a base station, or an access point, or a remote radio head node, or a relay, etc.

Fig. 5 illustrates the updating mechanism in a flow chart according to an embodiment of the invention. In step I) the network node 300 determines an initial beam codebook, e.g. the mentioned uniformly distributed codebook in the interval ⁽p_max - <Pmin- ^{ln ste}P ") ^tne network node 300 transmits one or more beams from the initial beam codebook in beam scanning transmissions during a time period. In step III) the network node 300 receives and records feedback associated with beams transmitted in step II). In step IV) the network node 300 determines the statistical properties of the beams transmitted in step II) based on the on records of the feedback received in step III). In step V) the network node 300 updates the beam codebook based on the determined statistical properties in step IV). In step VI) the network node 300 transmits beams from the updated beam codebook during a new time period. At VII) steps III) to VI) are repeated a number of times until the beam scanning procedure is ended.

In the following disclosure further aspects and embodiments of the invention are described and explained. The statistical property for the first transmission angle is the cdf F in some embodiments of the invention as aforementioned. Especially, the processor 102 may be configured to partition the cdf F into M number of intervals, where M is a positive integer larger than or equal to 1 . The processor 102 is further configured determine the second transmission angle based on the partitioned cdf F.

In the following disclosure it is explained how a beam codebook could be designed and updated using the cdf F as the statistical property for the transmission angles. Note that for a number of statistical distributions, determining the variance and mean of the random variables (beam angles in this case) gives a closed-form description of the cdf if the type of the cdf F is already estimated.

First, the beam computing device 100 estimates or receives the cdf of the transmission angles, denoted as F(» where φ is transmission angle, at the mMIMO array using the initial beam codebook as explained above. Using the cdf F(», we construct intervals

where d₀ = 0, and d_M = 1. The partitioning of the angular range [(p_min, (p_max] is performed in a way that the probability that the transmission angles belongs each sub-range, i.e.,

is almost the same where S_t denotes the sector of the i^th beam. In other words, we uniformly partition the cdf F(» by letting d_t =— (i = 1, ... , M) . Embodiment A

In an embodiment A, each beam is simply determined by a single transmission angle with results in narrow beams. We construct this angle by obtaining the mean angle (i.e. expected angle) of the beam sector given in equation (2).

Then the expected angles for each sub-range can be calculated as

1 rF-^di)

<P^{i =} T^aj ^{φ dF}^

i - "t-lJ ' F^{~ 1} ( (d¾i-i) (3)

For a smooth cdf F(<p) and when M is large, the cdf curve in each interval I_t can be approximated to be linear. Hence the expected angles (p_t can be simply approximated to be the central angle of each sub-range, i.e.,

<Pi 2 ^■ (4)

Since the inverse of a linear function is also a linear function, we can further approximate the angles in equation (4) as

which is then used as the main lobe angle for the construction of the i-th beam.

Fig. 6 illustrates an example of cdf-partitioning with 6 intervals, which results in a codebook with 6 beams according to Equations (5). In this figure M = 6; i.e. six intervals are used to generate a beam codebook with 6 beams. The main lobe of the beams herein is set to

<Pi =

-

Fig. 7 shows a flow chart for the beam construction according to this embodiment. In step I) the beam computing device 100 receives statistical properties of the transmission angles and determines the cdf F. In step II) the beam computing device 100 partitions the cdf into M intervals with almost equal probabilities, i.e. [d₀, d₁, d₂, - , d_M] . Further, the number of beams may be set in step II) as indicated by step lib). In step III) the beam computing device 100 finds the centre for each interval. In step IV) the beam computing device 100 finds the transmission angel for each interval based on the inverse cdf F^_1 of for the centre for each interval. In step V) the beam computing device 100 computes the beams for each interval having its main lobe at the found transmission angle.

Fig. 8 shows some signalling and processing aspects according to embodiments of the invention. In P1 the transmitting device 300 initiates beam scanning by obtaining an initial beam codebook. In S1 the transmitting device 300 transmits beams in the initial beam codebook to the receiving device 600 (such as a user device 400) during a time period. The time period may vary depending on communication scenario and the design. The beams may be transmitted in the time, frequency and/or code domain. In S2 the transmitting device 300 receives feedback from the receiving device 600. The feedback may indicate the CQI for a subset of beams transmitted in S1. The subset could e.g. comprise the strongest beams; i.e. the beams with highest CQI. The transmitting device 300 derives statistical properties of the transmission angles in P2 using the collected feedback. The transmitting device 300 further computes new beams for beam scanning based on the statistical properties. In S3 the transmitting device 300 transmits beams computed in P2 to the receiving device 600 in beam scanning. In S4 the transmitting device 300 receives feedback from the receiving device 600. The transmitting device 300 again derives statistical properties of the transmission angles in P3. The transmitting device 300 further computes new beams for beam scanning based on the statistical properties derived in P3. In S3 the transmitting device 300 transmits updated beams computed in P3 to the receiving device 600 in beam scanning. This process of computing beams based on received feedback is repeated in the transmitting device 300 as long as the beam scanning process continues. In some implementation, the knowledge of the beam codebook which is updated based on the statistical properties is not required at the receiving device 600, which increases the flexibility of the disclosed invention. In cases when the transmitting device 300 may change the size of the beam codebook (i.e. number of beams), it may be beneficial to signal the size of the new beam codebook to the receiving device 600.

In another embodiment, the beam codebooks [ϋ^ ϋ^ ... , ϋ_Μ} is constructed for linear arrays according to

where with uniform partitioning, we have 1

exp (-y ^ sin [F^{" 1}

= : for i = 1, ... M

(7) exp {-j ²r (n_t - l sm [F-ⁱ (¾ )])

Note that the solution can be easily extended to any type of arrays. For two-dimensional arrays such as rectangular and cylindrical arrays, the statistical information of both azimuth and elevation angles can be used to design beam codebooks according to embodiments of the invention.

Embodiment B

In an embodiment B, statistical beam codebook is applied to enable broad beams. Therefore, each beam sector S_t is quantized by N_q quantization points where N_q is a design parameter. We present two alternative methods of sector quantization in this respect.

The first method for sector quantization comprises of uniformly quantizing the sector S_t as

S_q,i = [F- di- , q + F-Hdi-x) , 2q + F^-i) (8) where

This method will target a uniform coverage inside each sector S_t and thus provide low angular outage probability.

The second method for sector quantization starts by uniformly quantizing the interval I_t = [d_i_₁ di] i.e., the portion of CDF corresponding to sector S_t as

Iq,i = [di-i, q + di_ , 2q + d_t] (10) where q =

Then the quantized angles in the sector S_t are constructed by inverse mapping of the cdf as Hdi-x) , F-¹(q + d_i_₁) , F-¹(2q + d_i_₁) F^"1^] (1 1 )

This second method allocates more radiation power to angles with higher likelihood inside sector s_t. This will increase the average SNR.

Finally, once the quantized sector S_{q i} has been determined, a beam for sector s_t is constructed by averaging as

for i = l, ... M where a_t is a normalization constant to ensure total power constituent.

The proposed solution can be directly generalized to more sophisticated combining methods that consider any combining method including arbitrary weighted average. Similarly, this embodiment be easily extended to any type of arrays including two-dimensional arrays such as rectangular and cylindrical arrays where the statistical information of both azimuth and elevation angles are used to design the beam codebook.

One of the common distributions of the transmission angles is Laplacian distribution whose pdf is given by

where <p_min≤πι_φ≤ (p_max denotes the mean of the angles and σ_φ is the standard deviation.

The parameters <p_min and <p_max denote the minimum and maximum angle, respectively. The constant k is a normalization factor given as

The corresponding cdf F(<p) can be found to be V2~|<p— m,

F(SP) = - c + - sign(<p - τη_φ) 1— exp I—

2 2 (15) where c = 1 and sign(. ) denotes the sign function which returns the

sign of its argument. The inverse cdf F ¹ can be computed as

2p (16)

F ¹(p) = m_(p - -≡sign (^- - c In - - c

V2 Using the proposed beam construction algorithm for the general case, the beam codebook {ϋ₁, ϋ_2> - , ϋ_Μ} for Laplacian distribution with uniform partitioning of the cdf, the i-th beam ( i = l, ... M) becomes

(1 7)

Using the proposed beam construction algorithm for the general case using the second method, the beam codebook {i?_!,^, - , -d_M} for Laplacian distribution is given by

Fig. 9 shows radiation patterns of an example of the narrow beams for M = 16 for transmission angles in the interval [-60 60] in Embodiment A. Fig. 9 shows the present beam design when the transmission angles are distributed according to Laplacian distribution when there are 16 beams in the beam codebook.

Fig. 10 shows radiation patterns of examples of broad beams for M = 16 and M = 32 for transmission angles in the interval [-60 60]. It can be seen in Fig. 10 that more beams are concentrated around zero which is the mean value of the distribution of transmission angles in this case. Broad beams provide better angular coverage. Fig. 10 shows codebooks of broad beams according to Embodiment B. AoD is assumed to be in the interval [-60 60] degrees and Laplacian distributed with πι_φ = 0 and σ_φ=15. Fig. 10 (a) shows the first quantization method with 16 beams. Fig. 10 (b) shows the second quantization method with 16 beams. Fig. 10 (c) shows first quantization method with 32 beams. Fig. 10 (d) shows second quantization method with 32 beams.

Fig. 1 1 and 12 show the transmission rate that is achievable using the present solution in which the X-axis shows SNR in dB and the Y-axis shows the rate in bit/s/Hz. In the plots in Fig. 1 1 and 12, a single-path LoS channel is considered, where the transmission angles follow Laplacian distribution with mean πι_φ = 0 and variance σ_φ = 5 and σ_φ = 15 between -60 and 60 degrees. The network node 300 is equipped with a n_t = 64 element uniform linear antenna array (ULA) with half-wavelength antenna spacing. The user device 400 is equipped with a single antenna. The following three schemes are considered in Fig. 1 1 and 12:

• Proposed solution: the beams are constructed using the proposed algorithm in Fig. 5 for Laplacian distribution. The closed-from expression for the beams is given in equation (19) for narrow beams and in equation (20) for broad beams.

• Baseline scheme: the beams are constructed by uniform quantization of angles between -60 and 60 degrees for two cases of narrow and broad beams.

• Upper Bound: an infinite number of beams are considered. This is similar to the perfect CSI with optimal beamforming.

Fig. 1 1 shows the achievable transmission rate over LoS channels versus SNR with a codebook of M = 16 and 32 narrow beams for σ_φ = 5. It is seen from Fig. 1 1 that the proposed scheme significantly outperforms the conventional solution and operates within 1 -2 dB SNR from the upper bound at 6 bits/s/Hz. Fig. 1 1 shows the achievable rates over LoS channels with Fig. 1 1 (a) 16 beams and Fig. 1 1 (b) 32 beams using the baseline uniform narrow beam design for angles in the interval [-60 60] degrees and the proposed Laplacian design in Embodiment A with πι_φ = 0 and σ_φ = 5. The upper bound indicates the performance of the hypothetical transmission scheme with infinite number of beams. The number of antennas is set to 64.

Fig. 12 shows the achievable transmission rate over LoS channels versus SNR with a codebook of M = 16 and 32 broad beams for σ_φ = 15, respectively. It is seen that the proposed scheme significantly outperforms the conventional solution for broad beam as well. Fig. 12 shows the achievable rates over LoS channels with Fig. 12(a) 16 beams and Fig. 12(b) 32 beams using the baseline uniform broad beam design for angles in the interval [-60 60] degrees and the proposed Laplacian design in Embodiment B with πι_φ = 0 and σ_φ = 15. The upper bound indicates the performance of the hypothetical transmission scheme with infinite number of beams. The number of antennas is set to 64.

In summary, the proposed beam design according to the actual distribution of transmission angles provides much better performance. For a given number of beams in the beam codebook, a higher transmission rate can be obtained. In other words, for a given transmission rate, the beam codebook based on the distribution results to a less number of beams, which reduces the overhead due to pilot and feedback transmission. The gain can be observed for both narrow beams and broad beams. Broad beams in general perform better than narrow beams at the cost of higher complexity in beam generation.

A network node 300 described herein may also be denoted as an access node or an access point or a base station, e.g., a Radio Base Station (RBS), which in some networks may be referred to as transmitter, "eNB", "eNodeB", "NodeB", "gNB" or "B node", depending on the technology and terminology used. The access network nodes may be of different classes such as, e.g., macro eNodeB, home eNodeB or pico base station, based on transmission power and thereby also cell size. The access network node can be a Station (STA), which is any device that contains an IEEE 802.1 1 -conformant Media Access Control (MAC) and Physical Layer (PHY) interface to the Wireless Medium (WM). The access network node 300a, 300b may also be a network node in a wired communication system. Further, standards promulgated by the IEEE, the Internet Engineering Task Force (IETF), the International Telecommunications Union (ITU), the 3GPP standards, fifth-generation (5G) standards and so forth are supported. In various embodiments, the network node 400 may communicate information according to one or more IEEE 802 standards including IEEE 802.1 1 standards (e.g., 802.1 1 a, b, g/h, j, n, and variants) for WLANs and/or 802.16 standards (e.g., 802.16-2004, 802.16.2-2004, 802.16e, 802.16f, and variants) for WMANs, and/or 3GPP LTE standards. The access network node 300a, 300b may communicate information according to one or more of the Digital Video Broadcasting Terrestrial (DVB-T) broadcasting standard and the High performance radio Local Area Network (HiperLAN) standard.

A user device 400 described herein may be any of a User Equipment (UE), mobile station (MS), wireless terminal or mobile terminal which is enabled to communicate wirelessly in a wireless communication system, sometimes also referred to as a cellular radio system. The UE may further be referred to as mobile telephones, cellular telephones, computer tablets or laptops with wireless capability. The UEs in the present context may be, for example, portable, pocket-storable, hand-held, computer-comprised, or vehicle-mounted mobile devices, enabled to communicate voice or data, via the radio access network, with another entity, such as another receiver or a server. The UE can be a Station (STA), which is any device that contains an IEEE 802.1 1 -conformant Media Access Control (MAC) and Physical Layer (PHY) interface to the Wireless Medium (WM). Further, standards promulgated by the IEEE, the Internet Engineering Task Force (IETF), the International Telecommunications Union (ITU), the 3GPP standards, fifth-generation (5G) standards and so forth, are supported. In various embodiments, the receiving device 100 may communicate information according to one or more IEEE 802 standards including IEEE 802.1 1 standards (e.g., 802.1 1 a, b, g/h, j, n, and variants) for WLANs and/or 802.16 standards (e.g., 802.16-2004, 802.16.2-2004, 802.16e, 802.16f, and variants) for WMANs, and/or 3GPP LTE standards. The receiving device 100 may communicate information according to one or more of the Digital Video Broadcasting Terrestrial (DVB-T) broadcasting standard and the High performance radio Local Area Network (HiperLAN) standard.

Furthermore, any method according to the present invention may be implemented in a computer program, having code means, which when run by processing means causes the processing means to execute the steps of the method. The computer program is included in a computer readable medium of a computer program product. The computer readable medium may comprise of essentially any memory, such as a ROM (Read-Only Memory), a PROM (Programmable Read-Only Memory), an EPROM (Erasable PROM), a Flash memory, an EEPROM (Electrically Erasable PROM), or a hard disk drive.

Moreover, it is realized by the skilled person that the network node 300 or user device 400 comprises the necessary communication capabilities in the form of e.g., functions, means, units, elements, etc., for performing the present solution. Examples of other such means, units, elements and functions are: processors, memory, buffers, control logic, encoders, decoders, rate matchers, de-rate matchers, mapping units, multipliers, decision units, selecting units, switches, interleavers, de-interleavers, modulators, demodulators, inputs, outputs, antennas, amplifiers, receiver units, transmitter units, DSPs, MSDs, TCM encoder, TCM decoder, power supply units, power feeders, communication interfaces, communication protocols, etc. which are suitably arranged together for performing the present solution.

Especially, the processor of the beam computing device 100 may comprise, e.g., one or more instances of a Central Processing Unit (CPU), a processing unit, a processing circuit, a processor, an Application Specific Integrated Circuit (ASIC), a microprocessor, or other processing logic that may interpret and execute instructions. The expression "processor" may thus represent a processing circuitry comprising a plurality of processing circuits, such as, e.g., any, some or all of the ones mentioned above. The processing circuitry may further perform data processing functions for inputting, outputting, and processing of data comprising data buffering and device control functions, such as call processing control, user interface control, or the like. Finally, it should be understood that the present invention is not limited to the embodiments described above, but also relates to and incorporates all embodiments within the scope of the appended independent claims.

Claims

1 . Beam computing device for beam scanning (1 00) in a wireless communication system (500) , the beam computing device (1 00) comprising a processor (102) configured to

2. Beam computing device (1 00) according to claim 1 , wherein the statistical property for the first transmission angle is a cumulative distribution function (F), and

wherein the processor (1 02) is configured to

partition the cumulative distribution function (F) into M number of intervals, wherein M is a positive integer larger than or equal to 1 ;

determine the second transmission angle based on the partitioned cumulative distribution function (F).

3. Beam computing device (1 00) according to claim 2, wherein the processor (1 02) is configured to

partition the cumulative distribution function (F) into the M number of intervals so that first transmission angles associated to each interval have the same probability.

4. Beam computing device (1 00) according to claim 2 or 3, wherein the processor (1 02) is configured to

determine a second transmission angle for each interval based on the partitioned cumulative distribution function (F),

5. Beam computing device (1 00) according to claim 4, wherein the processor (1 02) is configured to

determine the second transmission angle for each interval based on the inverse of the cumulative distribution function (F^-1) of a center for each interval.

6. Beam computing device (100) according to claim 2 or 3, wherein the processor (102) is configured to

quantize each interval into a plurality of sub-intervals,

determine a second transmission angle for each quantized sub-interval,

7. Beam computing device (100) according to claim 6, wherein the processor (102) is configured to

8. Beam computing device (100) according to claim 6 or 7, wherein the quantization of each interval into a plurality of sub-intervals is a uniform quantization.

9. Beam computing device (100) according to any of the preceding claims, wherein the processor (102) is configured to

10. Beam computing device (100) according to claim 9, wherein the processor (102) is configured to

obtain the statistical property based on the obtained feedback.

1 1 . Beam computing device (100) according to claim 9 or 10, wherein the first beam scanning transmission is an initial beam scanning transmission, and wherein the transmitted beam in the initial beam scanning transmission is uniformly distributed over a predetermined transmission angular range (<p_max - <p_mi_n) .

12. Beam computing device (100) according to any of the preceding claims, wherein the processor (102) is configured to obtain the statistical property by

receiving the statistical property from a network node (300).

13. Transmitting device for a Multiple-Input Multiple-Output, MIMO, communication system (500), the transmitting device (300) comprising a beam computing device (100) according to any of the preceding claims.

14. Method (200) comprising:

obtaining (202) a statistical property for a first transmission angle associated with a transmitted beam,

determining (204) a second transmission angle based on the statistical property for the first transmission angle,

computing (206) a second beam for a second beam scanning transmission based on the second transmission angle.

15. Computer program with a program code for performing a method according to claim 14 when the computer program runs on a computer.