WO2023116703A1 - 预编码矩阵获取方法、装置、电子设备和存储介质 - Google Patents

预编码矩阵获取方法、装置、电子设备和存储介质 Download PDF

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WO2023116703A1
WO2023116703A1 PCT/CN2022/140399 CN2022140399W WO2023116703A1 WO 2023116703 A1 WO2023116703 A1 WO 2023116703A1 CN 2022140399 W CN2022140399 W CN 2022140399W WO 2023116703 A1 WO2023116703 A1 WO 2023116703A1
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spatial grid
matrix
target
precoding matrix
centroid
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French (fr)
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余泽浩
侯越涛
林伟
芮华
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ZTE Corp
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ZTE Corp
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • H04B7/0478—Special codebook structures directed to feedback optimisation

Definitions

  • the embodiments of the present application relate to the technical field of wireless communication, and in particular, to a method, device, electronic device, and storage medium for acquiring a precoding matrix.
  • precoding digital beamforming
  • the precoding scheme adopted in the current wireless communication system is generally based on the precoding matrix selection during precoding based on the precoding matrix indicator (Precoding Matrix Indicator, PMI) feedback of the user equipment (User Equipment, UE), or based on the sounding reference signal ( Sounding Reference Signal, SRS) measurement quantity selects the precoding matrix used.
  • PMI Precoding Matrix Indicator
  • UE User Equipment
  • SRS Sounding Reference Signal
  • the precoding scheme based on PMI feedback requires the UE to feed back the selection of the PMI codebook, and the precoding scheme based on SRS requires the UE to send measurement signals, both of which will cause additional time-frequency resource occupation, which brings large channel resource overhead;
  • the precoding method based on Singular Value Decomposition (SVD) needs to call huge computing resources for calculation when the number of antennas is large, which brings relatively large Large computing resource overhead;
  • the PMI-based precoding scheme requires the UE to measure the downlink channel before feedback, while the SRS-based precoding scheme needs to use the uplink and downlink reciprocity of the channel.
  • the main purpose of the embodiments of the present application is to propose a precoding matrix acquisition method, device, electronic equipment, and storage medium, aiming at reducing the cost of precoding matrix acquisition while improving the accuracy and adaptability of precoding matrix acquisition. Guarantee the high-performance data transmission of the communication system as much as possible.
  • an embodiment of the present application provides a precoding matrix acquisition method, including: determining the acquisition method of the precoding matrix; wherein, the acquisition method includes online acquisition and acquisition based on offline learning results; when the acquisition method is determined to be online acquisition
  • the acquisition method includes online acquisition and acquisition based on offline learning results; when the acquisition method is determined to be online acquisition
  • the centroid of the spatial grid to which the channel matrix belongs is obtained, and the precoding matrix of the target UE is obtained based on the centroid;
  • the spatial grid where the target UE is located is obtained, and based on the optimal precoding matrix of each spatial grid learned offline, the optimal precoding matrix of the spatial grid where the target UE is located is obtained as the precoding matrix of the target UE .
  • the embodiment of the present application also provides a precoding matrix acquisition device, including: a determination module, used to determine the precoding matrix acquisition method; wherein, the acquisition method includes online acquisition and acquisition based on offline learning results; the first An acquisition module, configured to acquire the centroid of the spatial grid to which the channel matrix belongs according to the clustering result of the channel matrix of the target user equipment UE when the acquisition mode is determined to be online acquisition, and acquire the precoding of the target UE based on the centroid Matrix; the second acquisition module is used to obtain the spatial grid where the target UE is located when the acquisition method is determined to be based on offline learning results, and obtain the optimal precoding matrix based on the offline learning of each spatial grid.
  • the optimal precoding matrix of the spatial grid where the target UE is located is used as the precoding matrix of the target UE.
  • an embodiment of the present application further provides an electronic device, the device includes: at least one processor; and a memory connected to the at least one processor in communication; wherein, the memory stores information that can be Instructions executed by the at least one processor, where the instructions are executed by the at least one processor, so that the at least one processor can execute the method for obtaining a precoding matrix as described above.
  • an embodiment of the present application further provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the method for obtaining a precoding matrix as described above is implemented.
  • Fig. 1 is the flow chart of the precoding matrix acquisition method in the embodiment of the present application.
  • FIG. 2 is a schematic structural diagram of a device for obtaining a precoding matrix in another embodiment of the present application
  • Fig. 3 is a schematic structural diagram of an electronic device in another embodiment of the present application.
  • an embodiment of the present application provides a precoding matrix acquisition method, including: determining the acquisition method of the precoding matrix; wherein, the acquisition method includes online acquisition and acquisition based on offline learning results; In the case of acquisition, according to the clustering result of the channel matrix of the target user equipment UE, the centroid of the spatial grid to which the channel matrix belongs is obtained, and the precoding matrix of the target UE is obtained based on the centroid; when the acquisition method is determined to be based on offline learning results In the case of , the spatial grid where the target UE is located is obtained, and based on the optimal precoding matrix of each spatial grid learned offline, the optimal precoding matrix of the spatial grid where the target UE is located is obtained as the precoding matrix of the target UE matrix.
  • the precoding matrix acquisition method determines the precoding matrix acquisition method according to the current state of the communication system and other factors.
  • the precoding matrix is acquired in an online acquisition method, according to the channel matrix of the target user equipment UE, Obtain the spatial grid to which the channel matrix belongs by clustering, and obtain the precoding matrix of the target UE based on the centroid of the spatial grid; when acquiring the precoding matrix based on offline learning results, determine the spatial grid to which the target UE belongs , and determine the precoding matrix of the target UE in the optimal precoding matrix based on each spatial grid learned offline.
  • the target UE is divided into spatial grids according to the clustering results of the channel matrix obtained in real time, and the precoding matrix of the target UE is obtained based on the centroid of the spatial grid to which it belongs, which can accurately Efficiently obtain the precoding matrix of the target UE, improve the accuracy of the precoding matrix acquisition and the adaptability to the terminal motion state; when the offline learning result is obtained, the optimal coding matrix of each spatial grid determined based on the offline data Among them, the precoding matrix of the target UE is determined according to the spatial grid to which the target UE belongs, which greatly reduces the occupation of channel resources and real-time computing resources, improves the timeliness of precoding matrix feedback, reduces the cost of precoding matrix acquisition, and improves user shape performance.
  • the first aspect of the embodiment of the present application relates to a method for obtaining a precoding matrix.
  • this embodiment takes the application in the base station as an example for illustration, and the method for obtaining the precoding matrix includes at least but not limited to the following steps:
  • Step 101 determine the way to acquire the precoding matrix; where the way to acquire includes online acquisition and acquisition based on offline learning results.
  • the base station After the base station is put into use, it starts to set the precoding matrix for the data transmission of different user equipments.
  • the channel matrix of any user equipment within the jurisdiction When the channel matrix of any user equipment within the jurisdiction is acquired, it detects the status of its own database, and according to the database status The detection result determines how to obtain the precoding matrix of the user equipment, where the obtaining method includes online obtaining and obtaining based on offline learning results.
  • the acquisition mode of the precoding matrix is determined according to the state of the database, so as to ensure the accuracy of the acquired precoding matrix.
  • the base station determines the manner of obtaining the precoding matrix, including: determining the manner of obtaining the precoding matrix according to the amount of data of the channel matrix of the UE stored in the database; wherein, when the amount of data is greater than a preset threshold, determining The acquisition method is based on offline learning results; when the amount of data is less than or equal to the preset threshold, it is determined that the acquisition method is online acquisition.
  • the base station may detect the amount of data of the channel matrix of the user equipment stored in its own database, and if the amount of data of the channel matrix stored in the database is greater than the preset threshold , the base station determines the acquisition method as acquisition based on offline learning results; when the data amount of the channel matrix stored in the database is less than or equal to the preset threshold, the base station determines the acquisition method as online acquisition.
  • the acquisition method is determined according to the amount of channel matrix data stored in the database, and the precoding matrix is obtained through online learning when the amount of stored data is insufficient to ensure the accuracy and timeliness of the precoding matrix acquisition.
  • the acquisition based on offline learning results ensures the accuracy of precoding matrix acquisition while avoiding the occupation of channel resources and real-time computing resources.
  • the base station can also detect the validity of the channel matrix stored in its own database according to the current channel environment when it detects that the channel environment has changed, and delete the historical data that is not suitable for the current channel environment. Or update the historical data according to the current signal environment. By detecting and dynamically maintaining the validity of the stored channel matrix data according to the current channel environment, the accuracy of the precoding matrix obtained based on the off-line learning results is further improved.
  • Step 102 if it is determined that the acquisition method is online acquisition, according to the clustering result of the channel matrix of the target user equipment UE, the centroid of the spatial grid to which the channel matrix belongs is acquired, and the precoding matrix of the target UE is acquired based on the centroid.
  • the base station acquires the channel matrix of the target user equipment for which the precoding matrix needs to be determined, and performs clustering on the channel matrix of the target user equipment when the base station determines that the acquisition method of the precoding matrix is online acquisition according to the database state detection result Processing, according to the clustering result of the channel matrix of the target UE, determine the spatial grid to which the channel matrix of the target UE belongs, then obtain the centroid of the spatial grid to which the channel matrix belongs, and obtain the precoding matrix of the target UE based on the obtained centroid.
  • the centroid of the spatial grid to which the channel matrix of the target UE belongs is obtained, the precoding matrix of the target UE is obtained based on the obtained centroid, and the precoding matrix of the target UE is accurately obtained by using the channel matrix obtained in real time matrix, and complete the accurate acquisition of the precoding matrix without feedback from the target UE.
  • the base station acquires the centroid of the spatial grid to which the channel matrix belongs according to the clustering result of the channel matrix of the target user equipment UE, including: determining whether there is a space grid to which the channel matrix belongs according to the clustering result of the channel matrix of the target UE. Spatial grid; if there is no spatial grid to which it belongs, create a spatial grid as the spatial grid to which the target UE belongs, and determine the centroid of the spatial grid to which it belongs according to the channel matrix of the target UE; if there is a spatial grid to which it belongs In the case of a grid, update the centroid of the spatial grid to which it belongs according to the channel matrix of the target UE.
  • the base station first performs clustering processing on the channel matrix of the target UE, and detects whether there is a spatial grid to which the channel matrix of the target UE belongs. If the spatial grid to which the channel matrix of the target UE belongs is detected, the The channel matrix is to update the centroid of the spatial grid to which the channel matrix of the target UE belongs, and then use the updated centroid as the centroid of the spatial grid to which the channel matrix of the target UE belongs, and continue to obtain the subsequent precoding matrix. If the spatial grid to which the channel matrix of the target UE belongs is not detected, a new spatial grid is created as the spatial grid to which the target UE belongs according to the channel matrix of the target UE, and the created spatial grid is calculated according to the channel matrix of the target UE.
  • the centroid of the new spatial grid and then continue to obtain the subsequent precoding matrix according to the calculated centroid of the new spatial grid.
  • updating the centroid of the spatial grid in time when there is a spatial grid to which the target UE channel matrix belongs creating a spatial grid to which the target UE channel matrix belongs and calculating the centroid of the spatial grid when there is no spatial grid to which the target UE channel matrix belongs ; Ensure that the centroid of the spatial grid to which the channel matrix of the target UE belongs can be accurately obtained, thereby ensuring the accuracy of the precoding matrix determined by online learning.
  • the base station determines whether there is a spatial grid to which the channel matrix belongs according to the clustering result of the channel matrix of the target UE, including: obtaining the autocorrelation matrix of the channel matrix of the target UE; detecting that the autocorrelation matrix is respectively compared with the existing Correlation of the centroids of each spatial grid, get the correlation result S k of each centroid; where, k is the serial number of the spatial grid, and the maximum value of k is the number of existing spatial grids; In the case of less than the preset correlation threshold, it is determined that there is no spatial grid to which the channel matrix belongs; in the case of S k greater than or equal to the preset correlation threshold, the spatial grid corresponding to the maximum value in S k Determine the spatial raster to which the channel matrix belongs.
  • the base station when it performs spatial grid detection on the channel matrix of the target UE, it first obtains the channel matrix H of the target UE in a preset manner, for example, calculates the channel matrix H of the target UE based on the SRS measurement results, Then, according to the channel matrix H of the target UE, the autocorrelation matrix H H H of the channel matrix H is calculated, where the superscript H indicates that the channel matrix H is conjugated and transposed. Then, according to the calculated autocorrelation matrix, the correlation between the autocorrelation matrix and the centroid of each existing spatial grid is calculated. For example, the correlation S k between the autocorrelation matrix and the centroids of existing spatial grids is calculated by the following formula:
  • k is the number of the current existing spatial grid in the grid set
  • R is the number set of the grid set
  • C k is the centroid of the kth spatial grid
  • cov(H H H,C k ) is Preset correlation calculation functions.
  • the following correlation calculation function can be used for calculation:
  • real() means to take the real part
  • trace() means to take the trace of the matrix
  • sqrt() means to take the square root. This embodiment does not limit the specific correlation calculation function used.
  • the size relationship between each obtained correlation S k and the preset correlation threshold is detected, and all S k are In the case of less than the preset correlation threshold, for example, the preset correlation threshold is 0.2, in the case that all S k are less than 0.2, it is determined that the channel matrix does not belong to any existing spatial grid, that is, there is no channel matrix
  • the preset correlation threshold can be set according to experience or determined according to the networking characteristics of the current communication system.
  • the preset correlation threshold can be set to is larger, and the preset correlation threshold can be set smaller when the existing spatial grid tends to be updated. This embodiment does not limit the specific determination method and setting of the preset correlation threshold.
  • the base station before the base station detects the correlation between the autocorrelation matrix and the centroids of the existing spatial grids, it also includes: detecting whether there is currently a spatial grid; When the number of grids is less than the preset number, then perform the detection of the correlation between the autocorrelation matrix and the centroids of the existing spatial grids; if there is no spatial grid or the number of existing spatial grids is greater than or If it is equal to the preset number, it is determined that there is no spatial grid to which the channel matrix belongs. Specifically, before calculating the correlation between the autocorrelation matrix and the centroid of each existing spatial grid, the base station detects whether there is an existing spatial grid and the number of existing spatial grids.
  • the number of existing spatial grids is less than the preset number, it is determined that there may be a space to which the channel matrix belongs in the current spatial grid, and then the detection of the autocorrelation matrix and the existing spatial grids is carried out. Correlation of the centroid; if there is no spatial grid currently or the number of existing spatial grids is greater than or equal to the preset number, it is determined that there is no spatial grid to which the channel matrix belongs, and a new spatial grid is directly created.
  • the base station determines the centroid of the spatial grid to which it belongs according to the channel matrix of the target UE, including: determining the autocorrelation matrix of the channel matrix of the target UE as the centroid of the spatial grid to which it belongs; The matrix updates the centroid of the spatial grid to which it belongs, including: obtaining the centroid of the spatial grid to which it belongs; performing weighted average of the obtained centroid and the autocorrelation matrix of the channel matrix of the target UE to obtain the updated centroid of the spatial grid to which it belongs .
  • the base station determines the centroid of the spatial grid to which the channel matrix belongs according to the channel matrix of the target UE, if the spatial grid to which the channel matrix of the target UE belongs is not detected, the base station enters the process of creating a spatial grid, and creates A new spatial grid is used as the spatial grid to which the channel matrix of the target UE belongs, and then the autocorrelation matrix of the channel matrix of the target UE is used as the centroid of the new spatial grid; when the channel matrix of the target UE is detected In the case of a spatial grid, enter the spatial grid update process to obtain the current centroid of the spatial grid to which the target UE channel matrix belongs, and then weight the obtained current centroid of the spatial grid and the autocorrelation matrix of the target UE channel matrix Averaging, using the weighted average result as the latest centroid of the spatial grid to which the channel matrix of the target UE belongs, and using the latest centroid as the centroid of the spatial grid to which the channel matrix of the target UE belongs to obtain the subsequent precoding
  • a spatial grid numbered K is created, and the value of K may be the current existing spatial grid number plus 1.
  • the channel matrix H of the target UE is converted to obtain the autocorrelation matrix H H H of the channel matrix H, and H H H is used as the centroid of the spatial grid K to which the target UE belongs.
  • the centroid C k of the spatial grid k to which it belongs is obtained, and then according to the autocorrelation matrix H H H of the channel matrix H of the target UE, the following formula is used to obtain the The updated centroid C k′ of spatial grid k:
  • N is the total number of existing channel matrices in the spatial grid k.
  • the spatial grid k to which the target UE channel matrix belongs can also be obtained according to the following formula:
  • the spatial grid k with the greatest correlation between the centroid and the autocorrelation matrix of the target UE channel matrix is selected as the spatial grid to which the target UE channel matrix belongs.
  • the base station obtains the precoding matrix of the target UE based on the centroid, including: performing singular value decomposition on the centroid to obtain a right singular matrix V; obtaining the first RI column of V as the precoding matrix of the target UE; wherein, RI is the rank indicator of V.
  • the base station calculates the precoding matrix of the target UE based on the centroid, it can perform calculation based on singular value decomposition to ensure the accuracy of precoding matrix acquisition while simplifying the calculation process and improving acquisition efficiency.
  • Step 103 if it is determined that the acquisition method is based on offline learning results, acquire the spatial grid where the target UE is located, and acquire the spatial grid where the target UE is located based on the optimal precoding matrix of each spatial grid learned offline
  • the optimal precoding matrix of is used as the precoding matrix of the target UE.
  • the base station directly acquires the spatial grid where the target UE is located when the base station determines that the acquisition method of the precoding matrix is based on the offline learning result according to the database state detection result, and then calculates each spatial grid based on the offline learning result.
  • the optimal precoding matrix of the grid the optimal precoding matrix corresponding to the spatial grid where the target UE is located is used as the precoding matrix of the target UE.
  • the base station obtains the spatial grid where the target UE is located, including: obtaining the measurement quantity reported by the target UE; where the measurement quantity is used to obtain the precoding matrix recommended by the UE side, the uplink channel matrix between the target UE and the base station, or The location information of the target UE; the spatial grid where the target UE is located is acquired according to the measured quantity.
  • the base station when determining the precoding matrix of the target UE, the base station obtains the measurement quantity that can reflect the spatial grid where the target UE is located, for example, Type I PMI, Type II PMI reported by the target UE, SRS information obtained by the base station, or The location information of the UE, etc., and then based on the acquired measurement quantity, obtain the precoding matrix recommended by the UE side, the uplink channel matrix between the target UE and the base station, or the location information of the target UE. For example, the precoding matrix recommended by the UE side is obtained based on the Type II PMI reported by the target UE. Then, based on the information calculated according to the measured quantity, the spatial grid where the target UE is located is acquired.
  • the measurement quantity that can reflect the spatial grid where the target UE is located, for example, Type I PMI, Type II PMI reported by the target UE, SRS information obtained by the base station, or The location information of the UE, etc.
  • the power spectrum analysis method is used to obtain the spatial angle capability distribution, and the beam direction during data transmission is calculated; according to the calculated horizontal angle and vertical angle of the beam direction, the target The spatial grid where the UE is located is judged to obtain the spatial grid where the target UE is located.
  • the base station learns the optimal precoding matrix for each spatial grid off-line in the following manner: according to the location information or beam directions of multiple UEs stored in the database, and based on the spatial grids divided by preset angular intervals , determine the spatial grid to which each UE belongs, and obtain the channel matrix set of the UE in each spatial grid; for each spatial grid, solve the objective function with constraints according to the channel matrix set of the UE in the spatial grid, and obtain The optimal precoding matrix of the spatial grid, wherein the constraint condition is the constraint condition of the precoding matrix.
  • the base station regularly (period can be changed) reads stored data from the database to perform offline learning, for example, once a day.
  • the base station reads the location information of multiple UEs stored in the database, calculate the horizontal and vertical angles from each location to the base station according to the location information, and then divide the spatial grid according to the preset angle intervals to determine the location of each UE.
  • the associated spatial grid for example, divides the spatial grid at intervals of 5 degrees in horizontal angle and 5 degrees in vertical angle. After the grid division is completed for each spatial grid and the spatial grid to which each UE belongs is determined, the channel matrix set of the UEs contained in each spatial grid is obtained according to the contained UEs.
  • the preset objective function with constraints is solved according to the channel matrix set corresponding to the spatial grid to obtain the optimal precoding matrix of the spatial grid. For example, according to the solution of the following function with constraints to obtain the optimal precoding matrix in the spatial grid k:
  • C is the channel capacity
  • ⁇ H C (H, W) ⁇ log 2 (1+SINR)
  • the superscript H indicates conjugate transposition
  • the superscript -1 indicates matrix inversion
  • the expression diag is the diagonal element of the matrix in brackets
  • Rnn is the noise covariance matrix.
  • RI is a current rank indication
  • I RI is a unit matrix whose dimension is equal to the current RI.
  • the set of candidate precoding matrix W whose modulus square is 1, calculate the value of channel capacity under different W, and use W with the largest value of the objective function as the optimal precoding matrix in the spatial grid.
  • the channel matrix set in the spatial grid is used to solve the objective function with precoding matrix constraints, and on the basis of not occupying too much real-time computing resources, the information of each spatial grid can be accurately obtained.
  • the optimal precoding matrix improves the adaptability of the communication system to scenarios such as low signal-to-noise ratio and UE movement.
  • the spatial grid can also be divided according to the geographic location of the UE on the map or other information that can be used to calculate or characterize the UE position.
  • the spatial grid division The specific information used at the time is not limited.
  • the objective function with constraints includes: maximizing the channel capacity, maximizing the minimum channel capacity, maximizing the average signal-to-noise ratio or maximizing the minimum signal-to-noise ratio, etc., in specific applications can be selected as required
  • the objective function of the present embodiment does not limit the specific objective function used.
  • the base station before the base station solves the objective function with constraints according to the channel matrix set of the UE in the spatial grid, it further includes: performing singular value decomposition on each channel matrix in the channel matrix set to obtain each channel matrix The right singular matrix V of ; obtain the first RI column of each V as a precoding matrix substituted into the objective function, where RI is the rank indicator of V.
  • the base station when it obtains the optimal precoding matrix corresponding to the spatial grid, in order to reduce the amount of calculation and the complexity of calculation, it can perform singular value decomposition on each channel matrix in advance to obtain the right singular matrix corresponding to each channel matrix, Then according to the current rank indication, multiple candidate precoding matrices composed of the front RI of the right singular matrix V corresponding to each channel matrix are obtained, and then the optimal precoding matrix is obtained directly based on multiple candidate precoding matrices A collection of , solve the objective function with constraints, and use the candidate precoding matrix when the objective function takes the maximum value as the optimal precoding matrix of the spatial grid. By reducing the set of candidate precoding matrices, the amount of calculation is greatly reduced, and the efficiency of precoding matrix generation is improved.
  • FIG. 2 Another aspect of the embodiment of the present application also provides a precoding matrix acquisition device, referring to Figure 2, including:
  • the determination module 201 is configured to determine an acquisition method of the precoding matrix; where the acquisition method includes online acquisition and acquisition based on offline learning results.
  • the first acquisition module 202 is configured to acquire the centroid of the spatial grid to which the channel matrix belongs according to the clustering result of the channel matrix of the target user equipment UE when the acquisition mode is determined to be online acquisition, and acquire the centroid of the target UE based on the centroid precoding matrix.
  • the second acquisition module 203 is configured to acquire the spatial grid where the target UE is located when the acquisition method is determined to be based on offline learning results, and acquire the target UE based on the optimal precoding matrix of each spatial grid learned offline.
  • the optimal precoding matrix of the spatial grid where the UE is located is used as the precoding matrix of the target UE.
  • this embodiment is an apparatus embodiment corresponding to a method embodiment applied to a functional network element, and this embodiment can be implemented in cooperation with the method embodiment.
  • the relevant technical details mentioned in the method embodiments are still valid in this embodiment, and will not be repeated here in order to reduce repetition.
  • the related technical details mentioned in this embodiment can also be applied in the method embodiment.
  • modules involved in this embodiment are logical modules.
  • a logical unit can be a physical unit, or a part of a physical unit, or multiple physical units. Combination of units.
  • units that are not closely related to solving the technical problem proposed by the present invention are not introduced in this embodiment, but this does not mean that there are no other units in this embodiment.
  • FIG. 3 Another aspect of the embodiment of the present application also provides an electronic device, referring to FIG. 3 , including: including at least one processor 301; Instructions executed by at least one processor 301, the instructions are executed by at least one processor 301, so that at least one processor 301 can execute the precoding matrix acquisition method described in any one of the above method embodiments.
  • the memory 302 and the processor 301 are connected by a bus.
  • the bus may include any number of interconnected buses and bridges.
  • the bus connects one or more processors 301 and various circuits of the memory 302 together.
  • the bus may also connect together various other circuits such as peripherals, voltage regulators, and power management circuits, all of which are well known in the art and therefore will not be further described herein.
  • the bus interface provides an interface between the bus and the transceivers.
  • a transceiver may be a single element or multiple elements, such as multiple receivers and transmitters, providing means for communicating with various other devices over a transmission medium.
  • the data processed by the processor 301 is transmitted on the wireless medium through the antenna, and further, the antenna receives the data and transmits the data to the processor 301 .
  • Processor 301 is responsible for managing the bus and general processing, and may also provide various functions including timing, peripheral interfacing, voltage regulation, power management, and other control functions. And the memory 302 can be used to store data used by the processor 301 when performing operations.
  • Embodiments of the present invention also provide a computer-readable storage medium storing a computer program.
  • the above method embodiments are implemented when the computer program is executed by the processor.
  • the aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-OnlyMemory), random access memory (RAM, RandomAccessMemory), magnetic disk or optical disk, and other media capable of storing program codes.

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Abstract

本申请公开了一种预编码矩阵获取方法、装置、电子设备和存储介质，方法包括：确定预编码矩阵的获取方式；获取方式包括在线获取和基于离线学习结果获取；在确定获取方式为在线获取的情况下，根据目标用户设备UE的信道矩阵的聚类结果，获取信道矩阵所属的空间栅格的质心，并基于质心获取目标UE的预编码矩阵；在确定获取方式为基于离线学习结果获取的情况下，获取目标UE所在的空间栅格，并基于离线学习到的各空间栅格的最优预编码矩阵，获取目标UE所在空间栅格的最优预编码矩阵，作为目标UE的预编码矩阵。

Description

预编码矩阵获取方法、装置、电子设备和存储介质
相关申请
本申请要求于2021年12月22日提交的、申请号为202111582610.3的中国专利申请的优先权，其全部内容通过引用结合在申请中。
技术领域
本申请实施例涉及无线通信技术领域，特别涉及一种预编码矩阵获取方法、装置、电子设备和存储介质。
背景技术
随着通信技术的发展进步，基于正交频分复用技术(Orthogonal Frequency Division Multiplexing，OFDM)的全新空口设计的全球性5G标准(5G New Radio，5GNR)逐渐成为下一代蜂窝技术的基础。在无线通信系统中，预编码(数字波束赋形)技术可以降低数据流间的干扰、改善信道条件数、提高数据吞吐量，是提升系统性能的关键技术之一。
当下无线通信系统中采用的预编码方案一般是基于用户设备(User Equipment，UE)的预编码矩阵指示(Precoding Matrix Indicator，PMI)反馈进行预编码时的预编码矩阵选择，或者基于探测参考信号(Sounding Reference Signal，SRS)的测量量选取采用的预编码矩阵。
但是，采用现有的预编码方案进行预编码的过程中，基于PMI反馈的预编码方案需要UE反馈PMI码本的选择，基于SRS的预编码方案则需要UE发送测量信号，两者都会造成额外的时频资源占用，带来较大的信道资源开销；另外，基于奇异值分解(Singular Value Decomposition，SVD)的预编码方法在天线数量较多时，需要调用巨大的计算资源进行计算，带来较大的计算资源开销；此外，基于PMI的预编码方案需要UE对下行信道进行测量后再反馈，基于SRS的预编码方案则需要利用信道的上下行互易性，在UE处于运动状态，尤其是高速运动状态时，使用计算出的预编码进行传输时的信道与计算预编码时的信道容易不一致的问题，导致性能损失。
发明内容
本申请实施例的主要目的在于提出一种预编码矩阵获取方法、装置、电子设备和存储介质，旨在降低预编码矩阵获取的开销的同时，提高预编码矩阵获取的准确性和自适应性，尽可能保证通信系统的高性能数据传输。
为实现上述目的，本申请实施例提供了一种预编码矩阵获取方法，包括：确定预编码矩阵的获取方式；其中，获取方式包括在线获取和基于离线学习结果获取；在确定获取方式为在线获取的情况下，根据目标用户设备UE的信道矩阵的聚类结果，获取信道矩阵所属的空间栅格的质心，并基于质心获取目标UE的预编码矩阵；在确定获取方式为基于离线学习结果获取的情况下，获取目标UE所在的空间栅格，并基于离线学习到的各空间栅格的最优预编码矩阵，获取目标UE所在空间栅格的最优预编码矩阵，作为目标UE的预编码矩阵。
为了实现上述目的，本申请实施例还提供了一种预编码矩阵获取装置，包括：确定模块，用于确定预编码矩阵的获取方式；其中，获取方式包括在线获取和基于离线学习结果获取；第一获取模块，用于在确定获取方式为在线获取的情况下，根据目标用户设备UE的信道矩阵的聚类结果，获取信道矩阵所属的空间栅格的质心，并基于质心获取目标UE的预编码矩阵；第二获取模块，用于在确定获取方式为基于离线学习结果获取的情况下，获取目标UE 所在的空间栅格，并基于离线学习到的各空间栅格的最优预编码矩阵，获取目标UE所在空间栅格的最优预编码矩阵，作为目标UE的预编码矩阵。
为实现上述目的，本申请实施例还提供了一种电子设备，所述设备包括：至少一个处理器；以及，与所述至少一个处理器通信连接的存储器；其中，所述存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够执行如上所述的预编码矩阵获取方法。
为实现上述目的，本申请实施例还提供了一种计算机可读存储介质，存储有计算机程序，所述计算机程序被处理器执行时实现如上所述的预编码矩阵获取方法。
附图说明
一个或多个实施例通过与之对应的附图中的图片进行示例性说明，这些示例性说明并不构成对实施例的限定。
图1是本申请实施例中的预编码矩阵获取方法流程图；
图2是本申请另一实施例中的预编码矩阵获取装置的结构示意图；
图3是本申请另一实施例中的电子设备的结构示意图。
具体实施方式
由背景技术可知，采用当下的预编码方案进行预编码时，预编码矩阵的获取存在较高的资源占用和成本，并且对于处于运动状态的用户设备具有较差的适应能力，容易导致通信系统整体性能下降。因此，如何简单、准确、高效的获取预编码矩阵，提高预编码方案的有效性和适应能力是一个迫切的技术问题。
为了解决上述问题，本申请的实施例提供了一种预编码矩阵获取方法，包括：确定预编码矩阵的获取方式；其中，获取方式包括在线获取和基于离线学习结果获取；在确定获取方式为在线获取的情况下，根据目标用户设备UE的信道矩阵的聚类结果，获取信道矩阵所属的空间栅格的质心，并基于质心获取目标UE的预编码矩阵；在确定获取方式为基于离线学习结果获取的情况下，获取目标UE所在的空间栅格，并基于离线学习到的各空间栅格的最优预编码矩阵，获取目标UE所在空间栅格的最优预编码矩阵，作为目标UE的预编码矩阵。
本申请实施例提供的预编码矩阵获取方法，根据通信系统的当前状态等因素，确定预编码矩阵的获取方法，采用在线获取的方式进行预编码矩阵获取时，根据目标用户设备UE的信道矩阵，通过聚类的方式获取信道矩阵所属空间栅格，基于所属空间栅格的质心获取目标UE的预编码矩阵；采用基于离线学习结果获取的方式进行预编码矩阵获取时，确定目标UE所属空间栅格，并在基于离线学习到的各空间栅格的最优预编码矩阵中，确定目标UE的预编码矩阵。在线获取时，根据实时获取的信道矩阵的聚类结果对目标UE进行空间栅格划分，并基于所属空间栅格的质心获取目标UE的预编码矩阵，在不需要借助UE反馈的情况下，准确高效的获取目标UE的预编码矩阵，提高预编码矩阵获取的准确性和对终端运动状态的自适应能力；基于离线学习结果获取时，在基于离线数据确定的各空间栅格的最优编码矩阵中，根据目标UE所属空间栅格确定目标UE的预编码矩阵，极大地减少对信道资源和实时计算资源的占用，提高预编码矩阵反馈的时效性，降低预编码矩阵获取成本的同时，提高用户的赋形性能。
为使本申请实施例的目的、技术方案和优点更加清楚，下面将结合附图对本申请的各实施例进行详细的阐述。然而，本领域的普通技术人员可以理解，在本申请各实施例中，为了 使读者更好地理解本申请而提出了许多技术细节。但是，即使没有这些技术细节和基于以下各实施例的种种变化和修改，也可以实现本申请所要求保护的技术方案。以下各个实施例的划分是为了描述方便，不应对本申请的具体实现方式构成任何限定，各个实施例在不矛盾的前提下可以相互结合相互引用。
下面将对结合具体的实施例的对本申请记载的预编码矩阵获取方法的实现细节进行具体的说明，以下内容仅为方便理解提供的实现细节，并非实施本方案的必须。
本申请实施例的第一方面涉及一种预编码矩阵获取方法，预编码矩阵获取方法的具体流程参考图1，预编码矩阵获取方法可以应用在通信系统的基站中，或者与通信系统基站通信连接的终端设备中，本实施例以应用在基站为例进行说明，预编码矩阵获取方法至少包括但不限于以下步骤：
步骤101，确定预编码矩阵的获取方式；其中，获取方式包括在线获取和基于离线学习结果获取。
具体地说，基站在投入使用后，开始对不同用户设备的数据传输进行预编码矩阵的设置，在获取到管辖范围内任意用户设备的信道矩阵时，对自身的数据库状态进行检测，根据数据库状态检测结果，确定用户设备的预编码矩阵的获取方式，其中，获取方式包括在线获取和基于离线学习结果获取。根据数据库状态确定预编码矩阵的获取方式，保证获取到的预编码矩阵的准确性。
在一个例子中，基站确定预编码矩阵的获取方式，包括：根据数据库存储的UE的信道矩阵的数据量，确定预编码矩阵的获取方式；其中，在数据量大于预设门限的情况下，确定获取方式为基于离线学习结果获取；在数据量小于或等于预设门限的情况下，确定获取方式为在线获取。具体而言，基站在确定预编码矩阵的获取方式时，可以对自身数据库中存储的用户设备的信道矩阵的数据量进行检测，在数据库中存储的信道矩阵的数据量大于预设门限的情况下，基站将获取方式确定为基于离线学习结果获取；在数据库中存储的信道矩阵的数据量小于或等于预设门限的情况下，基站将获取方式确定为在线获取。通过根据数据库中存储的信道矩阵的数据量决定采用的获取方式，在存储的数据量不足时通过在线学习的方式获取预编码矩阵，保证预编码矩阵获取的准确性和时效性，在存储的数据量足够时，通过基于离线学习结果获取，保证预编码矩阵获取的准确性的同时，避免对信道资源和实时计算资源的占用。
值得一提的是，基站还可以在检测到信道环境发生改变的情况下，根据当前信道环境对自身数据库中存储的信道矩阵的有效性进行检测，并将不适应当前信道环境的历史数据删除，或者根据当前信号环境对历史数据进行更新。通过根据当前信道环境对存储的信道矩阵数据的有效性进行检测和动态维护，进一步基于离线学习结果获取的预编码矩阵的准确性。
步骤102，在确定获取方式为在线获取的情况下，根据目标用户设备UE的信道矩阵的聚类结果，获取信道矩阵所属的空间栅格的质心，并基于质心获取目标UE的预编码矩阵。
具体地说，基站在根据数据库状态检测结果确定预编码矩阵的获取方式为在线获取的情况下，获取需要确定预编码矩阵的目标用户设备的信道矩阵，并对目标用户设备的信道矩阵进行聚类处理，根据目标UE的信道矩阵的聚类结果，确定目标UE的信道矩阵所属的空间栅格，然后获取信道矩阵所属空间栅格的质心，并基于获取到的质心获取目标UE的预编码矩阵。通过根据目标UE的信道矩阵聚类结果，得到目标UE信道矩阵所属的空间栅格的质 心，基于获取到的质心获取目标UE的预编码矩阵，利用实时获取的信道矩阵准确获取目标UE的预编码矩阵，在不借助目标UE的反馈的情况下，完成预编码矩阵的准确获取。
在一个例子中，基站根据目标用户设备UE的信道矩阵的聚类结果，获取信道矩阵所属的空间栅格的质心，包括：根据目标UE的信道矩阵的聚类结果，确定是否存在信道矩阵所属的空间栅格；在不存在所属的空间栅格的情况下，创建空间栅格作为目标UE所属的空间栅格，并根据目标UE的信道矩阵确定所属的空间栅格的质心；在存在所属的空间栅格的情况下，根据目标UE的信道矩阵更新所属的空间栅格的质心。
具体而言，基站先对目标UE的信道矩阵进行聚类处理，对是否存在目标UE信道矩阵所属的空间栅格进行检测，若检测到目标UE信道矩阵所属的空间栅格，则根据目标UE的信道矩阵，对目标UE信道矩阵所属空间栅格的质心进行更新，然后将更新后的质心作为目标UE信道矩阵所属空间栅格的质心，并继续后须预编码矩阵的获取。若未检测到目标UE信道矩阵所属的空间栅格，则根据目标UE的信道矩阵，创建一个新的空间栅格作为目标UE所属的空间栅格，并根据目标UE的信道矩阵，计算出创建的新空间栅格的质心，然后根据计算出的新空间栅格的质心继续后续预编码矩阵的获取。通过在存在目标UE信道矩阵所属空间栅格时及时更新空间栅格的质心；在不存在目标UE信道矩阵所属空间栅格时创建目标UE信道矩阵所属的空间栅格并计算出空间栅格的质心；保证能够准确的获取目标UE信道矩阵所属空间栅格的质心，进而保证在线学习确定出的预编码矩阵的准确性。
值得一提的是，对目标UE信道矩阵进行所属空间栅格检测时，不仅可以根据自相关矩阵进行聚类，还可以根据基于1范数、基于2范数、基于∞范数、Cosine距离等方式进行所属空间栅格的检测和判断，本实施例对具体采用的检测方式不做限制。
在一实施例中，基站根据目标UE的信道矩阵的聚类结果，确定是否存在信道矩阵所属的空间栅格，包括：获取目标UE的信道矩阵的自相关矩阵；检测自相关矩阵分别与已有的各个空间栅格的质心的相关性，得到各质心的相关性结果S k；其中，k为空间栅格的编号，k的最大取值为已有的空间栅格的数量；在S k均小于预设相关性门限的情况下，确定不存在信道矩阵所属的空间栅格；在S k中存在大于或等于预设相关性门限的情况下，将S k中的最大值对应的空间栅格确定为信道矩阵所属的空间栅格。
具体而言，基站在对目标UE的信道矩阵进行所属空间栅格检测的时候，先通过预设的方式获取目标UE的信道矩阵H，例如，基于SRS测量结果计算出目标UE的信道矩阵H，然后根据目标UE的信道矩阵H，计算出信道矩阵H的自相关矩阵H HH，其中，上标H表示对信道矩阵H取共轭转置。然后根据计算出的自相关矩阵，计算出自相关矩阵和每一个已有的空间栅格的质心之间的相关性。例如，通过如下公式计算自相关矩阵和已有各空间栅格的质心的相关性S k：
S k＝cov(H HH,C k)，k∈R
其中，k为当前已有的空间栅格在栅格集合中的编号，R为栅格集合的编号集合，C k为第k个空间栅格的质心，cov(H HH,C k)为预设的相关性计算函数。例如，可以采用如下的相关性计算函数进行计算：
cov(A,B)＝real(trace(A HB)/sqrt(trace(A HA))/sqrt(trace(B HB)))
其中，real()表示取实部，trace()表示取矩阵的迹，sqrt()表示取平方根。本实施例对采用的具体相关性计算函数不做限制。
在获取到自相关矩阵与已有各空间栅格的质心之间的相关性后，对得到的每一个相关性S k与预设相关性门限之间的大小关系进行检测，在所有S k均小于预设相关性门限的情况下，例如，预设相关门限为0.2，在所有S k均小于0.2的情况下，判定信道矩阵不属于任何一个已有的空间栅格，即，不存在信道矩阵所属的空间栅格；在检测到存在大于或等于预设相关性门限的一个或多个S k的情况下，将S k中最大值对应的空间栅格作为目标UE信道矩阵所属的空间栅格。通过检测目标UE信道矩阵对应的自相关矩阵与已有各空间栅格的质心之间的相关性，准确的对已有空间栅格中是否存在目标UE信道矩阵所属的空间栅格进行检测，便于后续采用对应的方式进行预编码矩阵的准确获取。
值得一提的是，预设相关性门限可以根据经验设置或者根据当前通信系统的组网特征确定，另外，在倾向于新增更多不同的空间栅格时，可以将预设相关性门限设置较大，在倾向于对已有空间栅格进行更新时，可以将预设相关性门限设置较小，本实施例对预设相关性门限的具体确定方式和设置不做限制。
更进一步的，基站在检测自相关矩阵分别与已有的各个空间栅格的质心的相关性之前，还包括：检测当前是否已有空间栅格；在当前已有空间栅格，且已有空间栅格的数量小于预设数量的情况下，再执行检测自相关矩阵分别与已有的各个空间栅格的质心的相关性；在当前不存在空间栅格或已有空间栅格的数量大于或等于预设数量的情况下，确定不存在信道矩阵所属的空间栅格。具体而言，基站在计算自相关矩阵与各已有空间栅格的质心的相关性之前，对当前是否存在已有空间栅格和已有空间栅格的数量进行检测，在当前已有空间栅格，且已有空间栅格的数量小于预设数量的情况下，判定当前空间栅格中可能存在信道矩阵所属的空间然后，然后再执行检测自相关矩阵分别与已有的各个空间栅格的质心的相关性；在当前不存在空间栅格或已有空间栅格的数量大于或等于预设数量的情况下，确定不存在信道矩阵所属的空间栅格，直接新建新的空间栅格。通过设置空间栅格的类别上线，优化信号矩阵所属空间栅格的获取过程，提高获取效率和准确性。
在另一个例子中，基站根据目标UE的信道矩阵确定所属的空间栅格的质心，包括：将目标UE的信道矩阵的自相关矩阵，确定为所属的空间栅格的质心；根据目标UE的信道矩阵更新所属的空间栅格的质心，包括：获取所属的空间栅格的质心；将获取的质心和目标UE的信道矩阵的自相关矩阵进行加权平均，得到更新后的所属的空间栅格的质心。具体而言，基站根据目标UE的信道矩阵确定信道矩阵所属的空间栅格的质心的时候，在未检测到目标UE的信道矩阵所属的空间栅格的情况下，进入空间栅格创建流程，创建一个新的空间栅格作为目标UE信道矩阵所属的空间栅格，然后将目标UE的信道矩阵的自相关矩阵，作为创建的新的空间栅格的质心；在检测到目标UE的信道矩阵所属的空间栅格的情况下，进入空间栅格更新流程，获取目标UE信道矩阵所属的空间栅格当前的质心，然后对获取到的空间栅格当前的质心和目标UE信道矩阵的自相关矩阵进行加权平均，将加权平均的结果作为目标UE信道矩阵所属空间栅格的最新质心，将最新的质心作为目标UE信道矩阵所属空间栅格的质心进行后续预编码矩阵的获取。根据检测结果采用对应方式进行目标UE所属空间栅格质心的获取，保证后续获取到的预编码矩阵的准确性。
例如，在未检测到目标UE的信道矩阵所属的空间栅格的情况下，创建一个编号为K的空间栅格，K的取值可以是当前已有空间栅格编号加1。然后对目标UE的信道矩阵H进行转换，获取信道矩阵H的自相关矩阵H HH，并将H HH作为目标UE所属空间栅格K的质心。 在检测到目标UE的信道矩阵所属的空间栅格的情况下，获取所属的空间栅格k的质心C k，然后根据目标UE的信道矩阵H的自相关矩阵H HH，按照如下公式获取所属空间栅格k更新后的质心C k′：
Figure PCTCN2022140399-appb-000001
其中，N为空间栅格k中已有信道矩阵的总数。
在对目标UE信道矩阵所属空间栅格的质心更新前，还可以根据如下公式获取目标UE信道矩阵所属空间栅格k：
k＝argmax(cov(H HH,C k))
选取质心与目标UE信道矩阵的自相关矩阵相关性最大的空间栅格k作为目标UE信道矩阵所属的空间栅格。
在另一个例子中，基站基于质心获取目标UE的预编码矩阵，包括：对质心进行奇异值分解，得到右奇异矩阵V；获取V的前RI列，作为目标UE的预编码矩阵；其中，RI为V的秩指示。基站基于质心计算目标UE的预编码矩阵时，可以通过基于奇异值分解的方式进行计算，保证预编码矩阵获取准确性的同时，简化计算过程，提高获取效率。
步骤103，在确定获取方式为基于离线学习结果获取的情况下，获取目标UE所在的空间栅格，并基于离线学习到的各空间栅格的最优预编码矩阵，获取目标UE所在空间栅格的最优预编码矩阵，作为目标UE的预编码矩阵。
具体地说，基站在根据数据库状态检测结果确定预编码矩阵的获取方式为基于离线学习结果获取的情况下，直接获取目标UE所在的空间栅格，然后在基于离线学习结果确定出的各空间栅格的最优预编码矩阵中，将目标UE所在空间栅格对应的最优预编码矩阵作为目标UE的预编码矩阵。
在一个例子中，基站获取目标UE所在的空间栅格，包括：获取目标UE上报的测量量；其中，测量量用于获取UE侧推荐的预编码矩阵、目标UE与基站间的上行信道矩阵或目标UE的位置信息；根据测量量获取目标UE所在的空间栅格。具体而言，基站在对确定目标UE的预编码矩阵时，获取可以反应目标UE所在空间栅格的测量量，例如，目标UE上报的Type I PMI、Type II PMI、基站获取的SRS信息、或UE的位置信息等，然后基于获取到的测量量，获取UE侧推荐的预编码矩阵、目标UE与基站间的上行信道矩阵或目标UE的位置信息。例如，基于目标UE上报的Type II PMI获得UE侧推荐的预编码矩阵。然后基于根据测量量计算出的信息，获取目标UE所在的空间栅格。例如，获取到UE侧推荐的预编码矩阵后，采用功率谱分析的方法获取空间角度能力分布，计算出数据传输过程中的波束方向；根据计算出的波束方向的水平角度和垂直角度，对目标UE所在空间栅格进行判断，获取目标UE所在空间栅格。通过根据获取到的测量量准确的对目标UE所在空间栅格进行判断，便于后续准确的获取预编码矩阵。
在另一个例子中，基站通过以下方式离线学习到各空间栅格的最优预编码矩阵：根据数据库中存储的多个UE的位置信息或波束方向，以及基于预设角度间隔划分的空间栅格，确定各UE所属的空间栅格，得到各空间栅格内的UE的信道矩阵集合；对每一个空间栅格，根据空间栅格内的UE的信道矩阵集合求解带约束条件的目标函数，得到空间栅格的最优预编码矩阵，其中，约束条件为预编码矩阵的约束条件。
具体而言，基站在进行离线学习时，定期(周期可变更)从数据库中读取存储的数据进行离线学习，例如每天一次。进行离线学习时，读取数据库中存储的多个UE的位置信息，根据位置信息计算每个位置到基站的水平垂直角度，再根据预设的角度间隔对空间栅格进行划分，确定每个UE所属的空间栅格，例如，水平角度5度、垂直角度5度的间隔对空间栅格进行划分。对各空间栅格完成栅格划分，并确定每个UE所属的空间栅格后，根据包含的UE，获取每个空间栅格内包含的UE的信道矩阵集合。对于每一个空间栅格，根据空间栅格对应的信道矩阵集合对预设的带约束条件的目标函数进行求解，得到空间栅格的最优预编码矩阵。例如，按照解下述带约束条件的函数获取空间栅格k内的最优预编码矩阵：
Figure PCTCN2022140399-appb-000002
S.T.‖W‖ 2＝1
其中，C为信道容量；
∑ HC(H，W)＝∑log 2(1+SINR)，
Figure PCTCN2022140399-appb-000003
上标H表示共轭转置、上标-1表示矩阵求逆、表达式diag为取括号内矩阵的对角线元素、Rnn为噪声协方差矩阵，在无法取得噪声协方差矩阵的情况下，可以设为单位阵、RI为当前的秩指示、I RI为维度等于当前RI的单位阵。
根据模的平方为1的备选预编码矩阵W集合，计算出不同W下信道容量的取值，并将目标函数取值最大的W，作为空间栅格内的最优预编码矩阵。通过在离线学习的状态下，利用空间栅格内的信道矩阵集合对带有预编码矩阵约束条件的目标函数进行求解，在不过多占用实时计算资源的基础上，准确获取每个空间栅格的最优预编码矩阵，提升通信系统对低信噪比和UE移动等场景的适应能力。
值得一提的是，在进行空间栅格划分的时候，还可以根据UE在地图上的地理位置或其余可以计算或表征UE位置的信息进行空间栅格的划分，本实施例对空间栅格划分时具体采用的信息不做限制。
在一实施例中，带约束条件的目标函数包括：最大化信道容量、最大化最小信道容量、最大化平均信噪比或最大化最小信噪比等，在具体的应用中可以根据需要选择适当的目标函数，本实施例对具体使用的目标函数不做限制。
在另一个例子中，基站在根据空间栅格内的UE的信道矩阵集合求解带约束条件的目标函数之前，还包括：将信道矩阵集合内的各信道矩阵分别进行奇异值分解，得到各信道矩阵的右奇异矩阵V；获取每个V的前RI列作为代入目标函数的预编码矩阵，其中，RI为V的秩指示。具体而言，基站在获取空间栅格对应的最优预编码矩阵时，为了减少计算量和计算复杂程度，可以预先对各信道矩阵进行奇异值分解，获取每个信道矩阵对应的右奇异矩阵，然后根据当前的秩指示，获取每个信道矩阵对应的右奇异矩阵V的前RI构成的多个备选预编码矩阵，再进行最优预编码矩阵获取时直接根据多个备选预编码矩阵构成的集合，对带有约束条件的目标函数进行求解，将目标函数取值最大时的备选预编码矩阵作为空间栅格的最优预编码矩阵。通过缩小备选的预编码矩阵集合，极大的降低计算量，提高预编码矩阵生成的效率。
此外，应当理解的是，上面各种方法的步骤划分，只是为了描述清楚，实现时可以合并为一个步骤或者对某些步骤进行拆分，分解为多个步骤，只要包括相同的逻辑关系，都在本 专利的保护范围内；对算法中或者流程中添加无关紧要的修改或者引入无关紧要的设计，但不改变其算法和流程的核心设计都在该专利的保护范围内。
本申请实施例的另一方面还提供了一种预编码矩阵获取装置，参考图2，包括：
确定模块201，用于确定预编码矩阵的获取方式；其中，获取方式包括在线获取和基于离线学习结果获取。
第一获取模块202，用于在确定获取方式为在线获取的情况下，根据目标用户设备UE的信道矩阵的聚类结果，获取信道矩阵所属的空间栅格的质心，并基于质心获取目标UE的预编码矩阵。
第二获取模块203，用于在确定获取方式为基于离线学习结果获取的情况下，获取目标UE所在的空间栅格，并基于离线学习到的各空间栅格的最优预编码矩阵，获取目标UE所在空间栅格的最优预编码矩阵，作为目标UE的预编码矩阵。
不难发现，本实施例为与应用在功能网元的方法实施例相对应的装置实施例，本实施例可与方法实施例互相配合实施。方法实施例中提到的相关技术细节在本实施例中依然有效，为了减少重复，这里不再赘述。相应地，本实施例中提到的相关技术细节也可应用在方法实施例中。
值得一提的是，本实施例中所涉及到的各模块均为逻辑模块，在实际应用中，一个逻辑单元可以是一个物理单元，也可以是一个物理单元的一部分，还可以以多个物理单元的组合实现。此外，为了突出本发明的创新部分，本实施例中并没有将与解决本发明所提出的技术问题关系不太密切的单元引入，但这并不表明本实施例中不存在其它的单元。
本申请实施例的另一方面还提供了一种电子设备，参考图3，包括：包括至少一个处理器301；以及，与至少一个处理器301通信连接的存储器302；其中，存储器302存储有可被至少一个处理器301执行的指令，指令被至少一个处理器301执行，以使至少一个处理器301能够执行上述任一方法实施例所描述的预编码矩阵获取方法。
存储器302和处理器301采用总线方式连接，总线可以包括任意数量的互联的总线和桥，总线将一个或多个处理器301和存储器302的各种电路连接在一起。总线还可以将诸如外围设备、稳压器和功率管理电路等之类的各种其他电路连接在一起，这些都是本领域所公知的，因此，本文不再对其进行进一步描述。总线接口在总线和收发机之间提供接口。收发机可以是一个元件，也可以是多个元件，比如多个接收器和发送器，提供用于在传输介质上与各种其他装置通信的单元。经处理器301处理的数据通过天线在无线介质上进行传输，进一步，天线还接收数据并将数据传输给处理器301。
处理器301负责管理总线和通常的处理，还可以提供各种功能，包括定时、外围接口、电压调节、电源管理以及其他控制功能。而存储器302可以被用于存储处理器301在执行操作时所使用的数据。
本发明的实施方式还提供了一种计算机可读存储介质，存储有计算机程序。计算机程序被处理器执行时实现上述方法实施例。
即，本领域技术人员可以理解，实现上述实施例方法中的全部或部分步骤是可以通过程序来指令相关的硬件来完成，该程序存储在一个存储介质中，包括若干指令用以使得一个设备(可以是单片机，芯片等)或处理器(processor)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-OnlyMemory)、 随机存取存储器(RAM，RandomAccessMemory)、磁碟或者光盘等各种可以存储程序代码的介质。
本领域的普通技术人员可以理解，上述各实施例是实现本申请的具体实施例，而在实际应用中，可以在形式上和细节上对其作各种改变，而不偏离本申请的精神和范围。

Claims (14)

  1. 一种预编码矩阵获取方法，包括：
    确定预编码矩阵的获取方式；其中，所述获取方式包括在线获取和基于离线学习结果获取；
    在确定所述获取方式为在线获取的情况下，根据目标用户设备UE的信道矩阵的聚类结果，获取所述信道矩阵所属的空间栅格的质心，并基于所述质心获取所述目标UE的预编码矩阵；
    在确定所述获取方式为基于离线学习结果获取的情况下，获取所述目标UE所在的空间栅格，并基于离线学习到的各空间栅格的最优预编码矩阵，获取所述目标UE所在空间栅格的最优预编码矩阵，作为所述目标UE的预编码矩阵。
  2. 根据权利要求1所述的预编码矩阵获取方法，其中，所述根据目标用户设备UE的信道矩阵的聚类结果，获取所述信道矩阵所属的空间栅格的质心，包括：
    根据所述目标UE的信道矩阵的聚类结果，确定是否存在所述信道矩阵所属的空间栅格；
    在不存在所述所属的空间栅格的情况下，创建空间栅格作为所述目标UE所属的空间栅格，并根据所述目标UE的信道矩阵确定所述所属的空间栅格的质心；
    在存在所述所属的空间栅格的情况下，根据所述目标UE的信道矩阵更新所述所属的空间栅格的质心。
  3. 根据权利要求2所述的预编码矩阵获取方法，其中，所述根据所述目标UE的信道矩阵的聚类结果，确定是否存在所述信道矩阵所属的空间栅格，包括：
    获取所述目标UE的信道矩阵的自相关矩阵；
    检测所述自相关矩阵分别与已有的各个空间栅格的质心的相关性，得到各质心的相关性结果S k；其中，k为空间栅格的编号，k的最大取值为已有的空间栅格的数量；
    在所述S k均小于预设相关性门限的情况下，确定不存在所述信道矩阵所属的空间栅格；
    在所述S k中存在大于或等于所述预设相关性门限的情况下，将所述S k中的最大值对应的空间栅格确定为所述信道矩阵所属的空间栅格。
  4. 根据权利要求3所述的预编码矩阵获取方法，其中，在所述检测所述自相关矩阵分别与已有的各个空间栅格的质心的相关性之前，还包括：
    检测当前是否已有空间栅格；
    在当前已有空间栅格，且已有空间栅格的数量小于预设数量的情况下，再执行所述检测所述自相关矩阵分别与已有的各个空间栅格的质心的相关性；
    在当前不存在空间栅格或已有空间栅格的数量大于或等于预设数量的情况下，确定不存在所述信道矩阵所属的空间栅格。
  5. 根据权利要求3或4所述的预编码矩阵获取方法，其中，所述根据所述目标UE的信道矩阵确定所述所属的空间栅格的质心，包括：
    将所述目标UE的信道矩阵的自相关矩阵，确定为所述所属的空间栅格的质心；
    所述根据所述目标UE的信道矩阵更新所述所属的空间栅格的质心，包括：
    获取所述所属的空间栅格的质心；
    将所述获取的所述质心和所述目标UE的信道矩阵的自相关矩阵进行加权平均，得到更新后的所述所属的空间栅格的质心。
  6. 根据权利要求1至4中任一项所述的预编码矩阵获取方法，其中，所述基于所述质心获取所述目标UE的预编码矩阵，包括：
    对所述质心进行奇异值分解，得到右奇异矩阵V；
    获取所述V的前RI列，作为所述目标UE的预编码矩阵；其中，所述RI为所述V的秩指示。
  7. 根据权利要求1所述的预编码矩阵获取方法，其中，所述方法还包括：通过以下方式离线学习到所述各空间栅格的最优预编码矩阵：
    根据数据库中存储的多个UE的位置信息或波束方向，以及基于预设角度间隔划分的空间栅格，确定各UE所属的空间栅格，得到各空间栅格内的UE的信道矩阵集合；
    对每一个所述空间栅格，根据所述空间栅格内的UE的信道矩阵集合求解带约束条件的目标函数，得到所述空间栅格的最优预编码矩阵，其中，所述约束条件为预编码矩阵的约束条件。
  8. 根据权利要求7所述的预编码矩阵获取方法，其中，所述目标函数的目标包括：最大化信道容量、最大化最小信道容量、最大化平均信噪比或最大化最小信噪比。
  9. 根据权利要求7所述的预编码矩阵获取方法，其中，在所述根据所述空间栅格内的UE的信道矩阵集合求解带约束条件的目标函数之前，还包括：
    将所述信道矩阵集合内的各信道矩阵分别进行奇异值分解，得到各信道矩阵的右奇异矩阵V；
    获取每个所述V的前RI列作为代入所述目标函数的预编码矩阵，其中，RI为所述V的秩指示。
  10. 根据权利要求7至9中任一项所述的预编码矩阵获取方法，其中，所述获取所述目标UE所在的空间栅格，包括：
    获取所述目标UE上报的测量量；其中，所述测量量用于获取UE侧推荐的预编码矩阵、所述目标UE与基站间的上行信道矩阵或所述目标UE的位置信息；
    根据所述测量量获取所述目标UE所在的空间栅格。
  11. 根据权利要求1所述的预编码矩阵获取方法，其中，所述确定预编码矩阵的获取方式，包括：
    根据数据库存储的UE的信道矩阵的数据量，确定预编码矩阵的获取方式；
    其中，在所述数据量大于预设门限的情况下，确定获取方式为所述基于离线学习结果获取；在所述数据量小于或等于所述预设门限的情况下，确定获取方式为所述在线获取。
  12. 一种预编码矩阵获取装置，包括：
    确定模块，设置为确定预编码矩阵的获取方式；其中，所述获取方式包括在线获取和基于离线学习结果获取；
    第一获取模块，设置为在确定所述获取方式为在线获取的情况下，根据目标用户设备UE的信道矩阵的聚类结果，获取所述信道矩阵所属的空间栅格的质心，并基于所述质心获取所述目标UE的预编码矩阵；
    第二获取模块，设置为在确定所述获取方式为基于离线学习结果获取的情况下，获取所述目标UE所在的空间栅格，并基于离线学习到的各空间栅格的最优预编码矩阵，获取所述目标UE所在空间栅格的最优预编码矩阵，作为所述目标UE的预编码矩阵。
  13. 一种电子设备，包括：
    至少一个处理器；以及，
    与所述至少一个处理器通信连接的存储器；其中，
    所述存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够执行如权利要求1至11中任一项所述的预编码矩阵获取方法。
  14. 一种计算机可读存储介质，存储有计算机程序，其中，所述计算机程序被处理器执行时实现如权利要求1至11中任一项所述的预编码矩阵获取方法。
PCT/CN2022/140399 2021-12-22 2022-12-20 预编码矩阵获取方法、装置、电子设备和存储介质 Ceased WO2023116703A1 (zh)

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