WO2024046112A1 - 数据处理方法、电子设备及存储介质 - Google Patents

数据处理方法、电子设备及存储介质 Download PDF

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Publication number
WO2024046112A1
WO2024046112A1 PCT/CN2023/113106 CN2023113106W WO2024046112A1 WO 2024046112 A1 WO2024046112 A1 WO 2024046112A1 CN 2023113106 W CN2023113106 W CN 2023113106W WO 2024046112 A1 WO2024046112 A1 WO 2024046112A1
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branch
data
matrix
target
receiver
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PCT/CN2023/113106
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English (en)
French (fr)
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董展谊
李文斌
林伟
芮华
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中兴通讯股份有限公司
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Publication of WO2024046112A1 publication Critical patent/WO2024046112A1/zh

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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
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks

Definitions

  • This application belongs to the technical field of receivers, and specifically relates to a data processing method, electronic equipment and storage media.
  • Embodiments of the present application provide a data processing method, electronic device, and storage medium to solve the problem of high complexity of traditional receivers when performing large-scale antenna processing.
  • embodiments of the present application provide a data processing method, including: determining a covariance matrix of frequency domain data received by a receiver; and determining the number of branches for parallel processing by the receiver according to the covariance matrix. and a spatial filtering weight corresponding to each branch, where each branch corresponds to an antenna of at least one dimension; for each branch, the data corresponding to the branch is processed according to the spatial filtering weight corresponding to the branch. Perform dimensionality reduction processing to obtain dimensionality reduction data corresponding to the branches; complete the processing operation of the frequency domain data based on the dimensionality reduction data corresponding to each branch.
  • embodiments of the present application further provide a data processing device, including: a first determination module, used to determine the covariance matrix of the frequency domain data received by the receiver; a second determination module, used to determine the covariance matrix according to the covariance matrix, correct Determine the number of branches processed in parallel by the receiver and the spatial filtering weight corresponding to each branch, wherein each branch corresponds to an antenna of at least one dimension; a dimensionality reduction module, configured for each branch, Perform dimensionality reduction processing on the data corresponding to the branch according to the spatial filtering weight corresponding to the branch, and obtain the dimensionally reduced data corresponding to the branch; a processing module, configured to perform dimensionality reduction according to the dimensionality reduction corresponding to each branch. After receiving the data, the processing operation of the frequency domain data is completed.
  • inventions of the present application provide an electronic device.
  • the electronic device includes a processor, a memory, and a program or instructions stored on the memory and executable on the processor.
  • the program or instructions are When executed by the processor, the steps of the method described in the first aspect are implemented.
  • embodiments of the present application provide a readable storage medium.
  • Programs or instructions are stored on the readable storage medium.
  • the steps of the method described in the first aspect are implemented. .
  • Embodiments of the present application include determining the covariance matrix of the frequency domain data received by the receiver; determining the number of branches for parallel processing by the receiver and the spatial domain filtering weight corresponding to each branch according to the covariance matrix; and determining the spatial domain filtering weight corresponding to the branch according to the covariance matrix.
  • the weights perform dimensionality reduction processing on the data corresponding to the branch to obtain the dimensionally reduced data corresponding to the branch; based on the dimensionally reduced data corresponding to each branch, the frequency domain data processing operation is completed.
  • Figure 1 is a schematic flow chart of the data processing method in the embodiment of the present application.
  • Figure 2 is a schematic flowchart of determining the number of branches for parallel processing by the receiver and the spatial filtering weight of each branch in an embodiment of the present application;
  • FIG. 3 is one of the schematic diagrams of the receiver in the embodiment of the present application.
  • Figure 4 is the second schematic diagram of the receiver in the embodiment of the present application.
  • Figure 5 is the third schematic diagram of the receiver in the embodiment of the present application.
  • Figure 6 is a schematic structural diagram of a data processing device in an embodiment of the present application.
  • FIG. 7 is a schematic structural diagram of an electronic device provided by an embodiment of the present application.
  • MIMO multiple-input multiple-output
  • the applicant provides a receiver that can perform covariance matrix calculation on the received frequency domain data, perform eigenvalue decomposition calculation on the covariance matrix to determine the spatial domain energy weight of the received frequency domain data, and determine The number of dimensions in the spatial domain energy direction of the received frequency domain data (that is, the number of parallel processing branches), so that the received frequency domain data can be processed in parallel according to the corresponding spatial domain filtering weights of each branch, thus realizing the data domain agreement based on the received data.
  • the variance matrix extracts the signal spatial energy direction information, avoiding the spatial filtering calculation based on the received signal channel estimate, and the spatial filtering processing of the energy dimensions of each branch can be calculated in parallel, reducing the complexity of the algorithm processing, while ensuring the spatial filtering calculation real-time and effectiveness.
  • Figure 1 shows a data processing method provided by an embodiment of the present application.
  • the method can be executed by a receiver.
  • the method includes the following steps:
  • Step 101 Determine the covariance matrix of the frequency domain data received by the receiver.
  • the receiver can perform Cyclic Prefix (CP) and Fast Fourier Transform (FFT) related processing on the received data to obtain the initial frequency domain data, and rearrange the initial frequency domain data according to the antenna dimensions to obtain the rearranged frequency domain data, so that the covariance matrix calculation can be performed on the rearranged frequency domain data.
  • CP Cyclic Prefix
  • FFT Fast Fourier Transform
  • the dimensions of the initial frequency domain data are [RE, Sym, Ka], RE represents the number of resource elements, Sym represents the sum of the number of data symbols and the number of pilot symbols, Ka represents the antenna dimension, and the rearranged frequency domain data R Dimensions of squ [N, Ka], N represents the product of RE and Sym.
  • the initial frequency domain data is 3276 ⁇ 14 ⁇ 32
  • the rearranged frequency domain data is 45864 ⁇ 32.
  • Nre represents the number of resource particles
  • the dimension of Cov squ is Ka ⁇ Ka
  • Ka represents the dimension of the antenna.
  • Step 102 According to the covariance matrix, determine the number of branches processed in parallel by the receiver and the spatial domain filtering weight corresponding to each branch.
  • Each branch corresponds to an antenna of at least one dimension, which enables selective division of antennas according to dimensions.
  • the first branch can correspond to a 2-dimensional antenna (i.e., two antennas)
  • the second branch can correspond to a 4-dimensional antenna
  • the third branch can correspond to an 8-dimensional antenna.
  • Antenna for another example, assume that the number of branches is 8, and each branch can correspond to an antenna of 1 dimension.
  • the number of parallel processing branches of the receiver and the spatial domain filtering weight corresponding to each branch are determined, thereby realizing the selection of the number of parallel processing branches and the extraction of spatial domain energy direction information, so that each branch can be parallelized
  • Performing spatial filtering processing ensures the real-time and effectiveness of spatial filtering calculations and reduces the complexity of algorithm processing.
  • Step 103 For each branch, perform dimensionality reduction processing on the data corresponding to the branch according to the spatial filtering weight corresponding to the branch, to obtain the dimensionally reduced data corresponding to the branch.
  • the data corresponding to the branch is dimensionally reduced according to the spatial filtering weight corresponding to the branch, so that subsequent processing and calculations can be performed based on the dimensionally reduced data, thereby reducing the processing complexity.
  • R′ squ,i R squ ⁇ G i ;
  • R′ squ, i represents the dimensionally reduced data corresponding to the i-th branch
  • G i represents the spatial domain filtering weight corresponding to the i-th branch
  • R squ represents the frequency domain data.
  • the value of i ranges from 0 to M, where M is the total number of branches.
  • Step 104 Complete the processing operation of the frequency domain data according to the dimensionally reduced data corresponding to each branch.
  • the frequency domain data processing operation is completed.
  • the processing operation can be demodulation, derate matching, low density parity check code (LDPC) Decoding, decoding Cyclic Redundancy Check (CRC) and other operations, thereby completing the entire processing process of frequency domain data by the receiver.
  • LDPC low density parity check code
  • CRC Cyclic Redundancy Check
  • this embodiment determines the number of branches processed in parallel by the receiver and the spatial filtering weight corresponding to each branch according to the covariance matrix, and performs dimensionality reduction processing on the data corresponding to the branch according to the spatial filtering weight corresponding to each branch. , Obtain the dimensionally reduced data corresponding to the branch, and complete the frequency domain data processing operation based on the dimensionally reduced data corresponding to each branch, realizing spatial domain filtering processing and branch parallel calculation, reducing the complexity of algorithm processing. At the same time, the real-time and effectiveness of spatial filtering calculation are ensured.
  • step 2 when determining the number of branches processed in parallel by the receiver and the spatial domain filtering weight corresponding to each branch according to the covariance matrix, the following may be included: step:
  • Step 201 Perform singular value decomposition (SVD) on the covariance matrix to obtain the S matrix and V matrix.
  • SVD singular value decomposition
  • Step 202 Determine the number of branches processed in parallel by the receiver according to the S matrix.
  • the energy threshold can be determined according to the diagonal element values of the S matrix; from the Determine the number of diagonal element values that are greater than the energy threshold among the diagonal elements, where the maximum value of the number is less than the antenna dimension of the antenna; determine the number of branches for parallel processing by the receiver based on the number .
  • the energy threshold when determining the energy threshold according to the diagonal element values of the S matrix, the energy threshold can be determined by the following formula:
  • Thr P represents the energy threshold
  • Ka represents the antenna dimension of the antenna
  • S(,i) represents the S matrix
  • Pthr represents the preset energy threshold
  • the number of diagonal element values greater than the energy threshold can be determined from the diagonal elements of the S matrix. This number needs to satisfy the antenna dimension smaller than the antenna, and then determine the receiver based on this number. The number of branches processed in parallel.
  • the number of antenna dimensions of the antenna is 32, and it is preset that the maximum value of the determined energy dimension (that is, the number of diagonal element values greater than the energy threshold) does not exceed the number of antenna dimensions of a preset proportion. If the ratio is 1/4, then the maximum number of diagonal element values determined is 8. In this way, the selection of the energy dimension is achieved.
  • the number of branches for parallel processing by the receiver when determining the number of branches for parallel processing by the receiver based on the determined number of diagonal element values, it can be limited according to requirements. For example, continuing the above example, assuming that the maximum number of determined diagonal element values is is 8, then the number of branches processed by the receiver in parallel can be 3, 4 or 8, which is not specifically limited here.
  • Step 203 Determine the spatial filtering weight corresponding to each branch according to the V matrix.
  • the target column in the V matrix can be determined for each branch, where the target column includes the 1st to Nth columns.
  • N is the number of antenna dimensions corresponding to the branch; the data located in the target column in all rows of the V matrix is determined as the spatial filtering weight corresponding to the branch.
  • the target column can be determined from column 1 to column 2.
  • the data located in column 1 to column 2 in all rows of the V matrix can be determined as the spatial filtering weight corresponding to branch A;
  • the target column can be determined from column 1 to column 4.
  • the data located in column 1 to column 4 in all rows of the V matrix can be determined as the spatial filtering weight corresponding to branch B;
  • the target column can be determined from column 1 to column 8.
  • the data located in column 1 to column 8 in all rows of the V matrix can be determined as the spatial filtering weight corresponding to branch C.
  • the comma in the formula is used to separate the front and rear parts. The colon before the comma indicates that all rows in the V matrix are selected, and the colon after the comma indicates that The target column ranges from 1 to 2 i .
  • the S matrix and V matrix are obtained.
  • the S matrix is used to determine the number of parallel processing branches of the receiver, and the V matrix is used to determine the spatial domain filtering weight corresponding to each branch, so that The receiver can process spatial filtering of different energy dimensions in parallel and can reduce the dimensionality of each branch, ensuring the real-time and effectiveness of spatial filtering calculations.
  • channel estimation and equalization processing are performed on the dimensionally reduced data corresponding to each branch, and the process of obtaining the equalized data corresponding to each branch can be found in the traditional receiver processing process, which will not be described in detail here.
  • any of the following methods may be included:
  • EVM Error Vector Magnitude
  • the target balancing data can be determined based on the EVM value corresponding to the branch.
  • the balanced data corresponding to the minimum EVM value can be determined as the target balanced data. That is, the branch with the smallest EVM is selected as the optimal branch, and the balanced data corresponding to the branch with the smallest EVM is determined as the target balanced data;
  • [min_index] min(EVM(C' points,i ));
  • (C' points,i ) represents the balanced data corresponding to the i-th branch
  • EVM(C' points,i ) represents the EVM value of the balanced data corresponding to the i-th branch
  • min_index represents the index of the branch with the smallest EVM.
  • C' points (min_index) represent the balanced data corresponding to the index of the smallest branch of EVM, Represents target equilibrium data.
  • At least one target EVM value that meets the preset EVM threshold can be selected from all the EVM values, and the at least one target EVM value corresponding to The sum value of the balanced data is determined as the target balanced data.
  • satisfying the preset EVM threshold may be less than the preset EVM threshold
  • [index] find(EVM(C' points,i ) ⁇ EVM Thr );
  • EVM Thr represents the preset EVM threshold, which can be set according to needs
  • index represents the index corresponding to at least one target EVM value
  • C' points (index) represents the balanced data corresponding to the determined index. Represents target equilibrium data.
  • the second method is to determine the average value of the balanced data corresponding to all the branches, and determine the average value as the target balanced data.
  • the average value of the balanced data corresponding to all the branches is determined as the target balanced data, that is, where M is the number of all branches.
  • CRC Cyclic Redundancy Check
  • the receiver includes a front-end processing module, a data domain spatial filtering function module, a receiver parallel processing module and a bit-level processing module.
  • the specific processing steps are as follows:
  • Step 1 Receiver front-end processing module, specifically execute the following processing flow:
  • R data performs CP and FFT related processing on the received data to obtain the frequency domain received data R data .
  • the dimension of R data is 3276 ⁇ 14 ⁇ 32.
  • the R data is rearranged according to the antenna dimension.
  • the rearranged data is R squ .
  • the dimension of R squ is 45864 ⁇ 32.
  • Step 2 The data domain air domain filtering function module performs data domain air domain filtering processing. This module specifically executes the following processing flow:
  • the data domain covariance matrix calculation process is performed, and the covariance matrix Cov squ of the received data R squ is calculated,
  • the dimension of Cov squ is 32 ⁇ 32;
  • Thr P is the energy threshold value, which can be configured as 3dB according to the empirical value configuration.
  • N is the number of dimensions that meet the threshold requirements, and the maximum value of N is limited. The maximum N does not exceed 1/4 the number of antennas, the N value in this embodiment is determined to be 4.
  • G i represents the spatial filtering weight corresponding to the i-th branch
  • V represents the V matrix
  • the value range of i is 0 to M
  • M is the total number of the branches
  • the comma in the formula is used to separate the front and rear parts.
  • the colon before the comma indicates that all rows in the V matrix are selected, and the colon after the comma indicates that the target column ranges from 1 to 2 i-1 .
  • Step 3 Receiver parallel processing module, specifically execute the following processing flow:
  • Each branch performs spatial filtering and dimensionality reduction processing according to the spatial filtering weight of each branch, and obtains the dimensionally reduced data R' squ,i .
  • the dimension of the reduced data is 45864 ⁇ Mi, where Mi represents the i-th path. Corresponding antenna dimensions.
  • R′ squ,i R squ ⁇ G i , where R′ squ,i represents the dimensionally reduced data corresponding to the i-th branch, G i represents the spatial filtering weight corresponding to the i-th branch, and R squ represents the Frequency domain data.
  • Branch selection function processing perform EVM calculation on the balanced data C' points,i of each branch of parallel processing, select the branch with the smallest EVM as the optimal branch, and determine the balanced data corresponding to the branch with the smallest EVM as the target balanced data.
  • Step 4 Bit-level processing module, specifically execute the following processing flow:
  • the target equalization data is sent to the bit-level processing module to perform subsequent demodulation, derate matching, LDPC decoding, CRC code and other operations to complete the entire receiver processing process.
  • the receiver includes a front-end processing module, a data domain spatial filtering function module, a receiver parallel processing module and a bit-level processing module.
  • the specific processing steps are as follows:
  • Step 1 Receiver front-end processing module, specifically execute the following processing flow:
  • R data performs CP and FFT related processing on the received data to obtain the frequency domain received data R data .
  • the dimension of R data is 3276 ⁇ 14 ⁇ 32.
  • the R data is rearranged according to the antenna dimension.
  • the rearranged data is R squ .
  • the dimension of R squ is 45864 ⁇ 32;
  • Step 2 The data domain air domain filtering function module performs data domain air domain filtering processing. This module specifically executes the following processing flow:
  • the data domain covariance matrix calculation process is performed, and the covariance matrix Cov squ of the received data R squ is calculated,
  • the dimension of Cov squ is 32 ⁇ 32;
  • calculate the number N of energy direction dimensions in the received signal airspace including specifically:
  • Thr P is the energy threshold value, which can be configured as 3dB according to the empirical value configuration.
  • the diagonal elements of the S matrix and the energy threshold are judged and processed, and the energy dimension greater than the energy threshold is selected.
  • N is the number of dimensions that meet the threshold requirements, and the maximum value of N is limited. The maximum N does not exceed 1. /4 the number of antennas, the value of N is determined to be 8 in this embodiment.
  • G i represents the spatial filtering weight corresponding to the i-th branch
  • V represents the V matrix
  • the value range of i is 0 to M
  • M is the total number of the branches
  • the comma in the formula is used to separate the front and rear parts.
  • a colon before the comma indicates that all rows in the V matrix are selected, and a colon after the comma indicates that the value of the target column is i.
  • Step 3 Receiver parallel processing module, specifically execute the following processing flow:
  • Each branch performs spatial filtering and dimensionality reduction processing according to the spatial filtering weight of each branch, and obtains the dimensionally reduced data R' squ,i .
  • the dimension of the reduced data is 45864 ⁇ Mi.
  • R′ squ,i R squ ⁇ G i ; where, R′ squ,i represents the dimensionally reduced data corresponding to the i-th branch, G i represents the spatial filtering weight corresponding to the i-th branch, and R squ represents the above Frequency domain data.
  • Branch selection function processing average the balanced data C' points,i of each branch processed in parallel, and output the final balanced data
  • N the number of all branches.
  • Step 4 Bit-level processing module, specifically execute the following processing flow:
  • the receiver includes a front-end processing module, a data domain spatial filtering function module, a receiver parallel processing module and a bit-level processing module.
  • the specific processing steps are as follows:
  • Step 1 Receiver front-end processing module, specifically execute the following processing flow:
  • R data performs CP and FFT related processing on the received data to obtain the frequency domain received data R data .
  • the dimension of R data is 3276 ⁇ 14 ⁇ 64.
  • the R data is rearranged according to the antenna dimension.
  • the rearranged data is R squ .
  • the dimension of R squ is 45864 ⁇ 64.
  • Step 2 The data domain air domain filtering function module performs data domain air domain filtering processing. This module specifically executes the following processing flow:
  • the data domain covariance matrix calculation process is performed, and the covariance matrix Cov squ of the received data R squ is calculated,
  • the dimension of Cov squ is 64 ⁇ 64;
  • calculate the number N of energy direction dimensions in the received signal airspace including:
  • Thr P is the energy threshold value, which can be configured as 3dB according to the empirical value configuration.
  • N is the number of dimensions that meet the threshold requirements, and the maximum value of N is limited. The maximum N does not exceed 1/4 the number of antennas, the N value is determined to be 8 in this embodiment.
  • G i represents the spatial filtering weight corresponding to the i-th branch
  • V represents the V matrix
  • the value range of i is 0 to M
  • M is the total number of the branches
  • the comma in the formula is used to separate the front and rear parts.
  • the colon before the comma indicates that all rows in the V matrix are selected, and the colon after the comma indicates that the target column ranges from 1 to 2 i .
  • Step 3 Receiver parallel processing module, specifically execute the following processing flow:
  • Each branch performs spatial filtering and dimensionality reduction processing according to the spatial filtering weight of each branch, and obtains the dimensionally reduced data R' squ,i , whose dimension is 45864 ⁇ Mi.
  • R′ squ,i R squ ⁇ G i , where R′ squ,i represents the dimensionally reduced data corresponding to the i-th branch, G i represents the spatial filtering weight corresponding to the i-th branch, and R squ represents the Frequency domain data.
  • Branch selection function processing perform EVM calculation on the balanced data C' points,i of each branch in parallel processing, select the branch whose EVM value meets the threshold requirement, perform merge processing, and obtain the target balanced data.
  • [index] find(EVM(C ' points,i ) ⁇ EVM Thr );
  • EVM Thr represents the preset EVM threshold
  • index represents the index corresponding to at least one target EVM value
  • C' points (index) represents the balanced data corresponding to the determined index. Represents target equilibrium data.
  • Step 4 Bit-level processing module, specifically execute the following processing flow:
  • the target equalization data is sent to the bit-level processing module to perform subsequent demodulation, derate matching, LDPC decoding, CRC code and other operations to complete the entire receiver processing process.
  • this embodiment extracts spatial energy direction information based on the data domain covariance matrix of the received signal, avoids spatial filtering calculations based on reference signal channel estimation, realizes data domain spatial filtering processing functions and parallel receiver processing functions, and can greatly improve Receiver performance under large-scale array antennas while reducing the processing complexity of large-scale array antennas.
  • FIG. 6 shows a schematic structural diagram of a data processing device provided by an embodiment of the present application.
  • a data processing device includes:
  • the first determination module 601 is used to determine the covariance matrix of the frequency domain data received by the receiver;
  • the second determination module 602 is configured to determine, according to the covariance matrix, the number of branches processed in parallel by the receiver and the spatial filtering weight corresponding to each branch, wherein each branch corresponds to at least one dimension. antenna;
  • the dimensionality reduction module 603 is configured to, for each branch, perform dimensionality reduction processing on the data corresponding to the branch according to the spatial filtering weight corresponding to the branch, and obtain the dimensionally reduced data corresponding to the branch;
  • the processing module 604 is configured to complete the processing operation of the frequency domain data according to the dimensionally reduced data corresponding to each branch.
  • the second determination module 602 is used to perform singular value decomposition SVD on the covariance matrix to obtain an S matrix and a V matrix; and determine the branch of the parallel processing branch of the receiver according to the S matrix. Quantity; determine the spatial filtering weight corresponding to each branch according to the V matrix.
  • the second determination module 602 is configured to determine an energy threshold based on the diagonal element values of the S matrix; determine the diagonal elements that are greater than the energy threshold from the diagonal elements of the S matrix.
  • the number of values, wherein the maximum value of the number is less than the number of antenna dimensions of the antenna; the number of branches processed in parallel by the receiver is determined based on the number.
  • the second determination module 602 is used to determine the energy threshold through the following formula:
  • Thr P represents the energy threshold
  • Ka represents the antenna dimension of the antenna
  • S(i, i) represents the S matrix
  • Pthr represents the preset energy threshold
  • the second determination module 602 is configured to determine the target column in the V matrix for each branch, where the target column includes the 1st column to the Nth column, and N is the The number of antenna dimensions corresponding to the branch; determine the data located in the target column in all rows of the V matrix as the spatial filtering weight corresponding to the branch.
  • the dimensionality reduction module 603 is used to perform dimensionality reduction processing on the data corresponding to the branch according to the spatial filtering weight corresponding to the branch and through the following formula to obtain the reduced dimension corresponding to the branch.
  • Post-dimensional data: R′ squ,i R squ ⁇ G i ;
  • R′ squ, i represents the dimensionally reduced data corresponding to the i-th branch
  • G i represents the spatial domain filtering weight corresponding to the i-th branch
  • R squ represents the frequency domain data
  • the processing module 604 is configured to perform channel estimation and equalization processing on the dimensionally reduced data corresponding to each of the branches to obtain the equalized data corresponding to each of the branches; according to each of the Branch the corresponding equalized data to determine the target equalized data; process the target equalized data to complete the processing operation of the frequency domain data.
  • the processing module 604 is configured to perform error vector magnitude EVM calculation on the equalized data corresponding to each branch; and determine the target equalized data according to the EVM value corresponding to each branch.
  • the processing module 604 is configured to determine the equalized data corresponding to the minimum EVM value as the target equalized data; or, select at least one target that satisfies a preset EVM threshold from all the EVM values. EVM value, and the sum of the equalized data respectively corresponding to the at least one target EVM value is determined as the target equalized data.
  • the processing module 604 is configured to determine an average value of the balanced data corresponding to all the branches, and determine the average value as the target balanced data.
  • the data processing device provided by the embodiment of the present application can implement each process implemented by the method embodiments of Figures 1 to 5. To avoid repetition, details will not be described here.
  • the data processing device in the embodiment of the present application may be a device, or may be a component, integrated circuit, or chip in a receiver.
  • the data processing device in the embodiment of the present application may be a device with an operating system.
  • the operating system can be an Android operating system, an ios operating system, or other possible operating systems, which are not specifically limited in the embodiments of this application.
  • FIG. 7 is a schematic structural diagram of an electronic device that implements various embodiments of the present application.
  • the electronic device may vary greatly due to different configurations or performance, and may include a processor (processor) 710, a communications interface (Communications Interface) 720, a memory (memory) 730, and a communication bus 740, where the processor 710, The communication interface 720 and the memory 730 complete communication with each other through the communication bus 740.
  • the processor 710 may call a computer program stored on the memory 730 and executable on the processor 710 to perform the following steps: determine a covariance matrix of frequency domain data received by the receiver; determine based on the covariance matrix The number of branches processed in parallel by the receiver and the spatial filtering weight corresponding to each branch, wherein each branch corresponds to an antenna of at least one dimension; for each branch, according to the spatial domain corresponding to the branch The filter weight performs dimensionality reduction processing on the data corresponding to the branch to obtain the dimensionally reduced data corresponding to the branch; based on the dimensionally reduced data corresponding to each branch, the processing operation of the frequency domain data is completed .
  • the above structure of the electronic device does not constitute a limitation on the electronic device.
  • the electronic device may include more or less components than shown in the figure, or a combination of certain components, or a different arrangement of components, such as an input unit, which may include a graphics processor. (Graphics Processing Unit, GPU) and microphone, the display unit can be configured with a display panel in the form of a liquid crystal display, an organic light-emitting diode, etc.
  • the user input unit includes at least one of a touch panel and other input devices. Touch panels are also called touch screens.
  • Other input devices may include but are not limited to physical keyboards, function keys (such as volume control keys, switch keys, etc.), trackballs, mice, and joysticks, which will not be described again here.
  • Memory can be used to store software programs as well as various data.
  • the memory may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instructions required for at least one function (such as a sound playback function, an image play function, etc.) etc.
  • memory may include volatile memory or non-volatile memory, or memory may include both volatile and non-volatile memory.
  • non-volatile memory can be read-only memory (Read-Only Memory, ROM), programmable read-only memory (Programmable ROM, PROM), erasable programmable read-only memory (Erasable PROM, EPROM), electrically removable memory.
  • Volatile memory can be random access memory (Random Access Memory, RAM), static random access memory (Static RAM, SRAM), dynamic random access memory (Dynamic RAM, DRAM), synchronous dynamic random access memory (Synchronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (Double Data Rate SDRAM, DDRSDRAM), enhanced synchronous dynamic random access memory (Enhanced SDRAM, ESDRAM), synchronous link dynamic random access memory (Synch link DRAM) , SLDRAM) and direct memory bus random access memory (Direct Rambus RAM, DRRAM).
  • the processor may include one or more processing units; optionally, the processor integrates an application processor and a modem processor, where the application processor mainly handles operations involving the operating system, user interface, application programs, etc., and the modem processor
  • the modulation processor mainly processes wireless communication signals, such as baseband processors. It will be appreciated that the modem processor described above may also to not be integrated into the processor.
  • Embodiments of the present application also provide a readable storage medium.
  • Programs or instructions are stored on the readable storage medium.
  • the program or instructions are executed by a processor, each process of the above-mentioned data processing method embodiment is implemented, and the same can be achieved. The technical effects will not be repeated here to avoid repetition.
  • the processor is the processor in the electronic device described in the above embodiment.
  • the readable storage media includes computer-readable storage media, such as computer read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disks or optical disks, etc.
  • An embodiment of the present application further provides a chip.
  • the chip includes a processor and a communication interface.
  • the communication interface is coupled to the processor.
  • the processor is used to run programs or instructions to implement various processes of the above method embodiments. , and can achieve the same technical effect, so to avoid repetition, they will not be described again here.
  • chips mentioned in the embodiments of this application may also be called system-on-chip, system-on-a-chip, system-on-a-chip or system-on-chip, etc.
  • the methods of the above embodiments can be implemented by means of software plus the necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is better. implementation.
  • the technical solution of the present application can be embodied in the form of a software product in essence or that contributes to the existing technology.
  • the computer software product is stored in a storage medium (such as ROM/RAM, disk, CD), including several instructions to cause a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of this application.

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Abstract

本申请公开了一种数据处理方法、电子设备及存储介质，方法包括：确定接收机所接收的频域数据的协方差矩阵；根据所述协方差矩阵，确定所述接收机并行处理的分支的数量和每个所述分支对应的空域滤波权值，其中每个所述分支对应至少一个维度的天线；针对每个所述分支，根据所述分支对应的空域滤波权值对所述分支所对应数据进行降维处理，得到所述分支所对应的降维后数据；根据每个所述分支所对应的降维后数据，完成所述频域数据的处理操作。

Description

数据处理方法、电子设备及存储介质
交叉引用
本申请要求在2022年09月02日提交中国专利局、申请号为202211071213.4、名称为“数据处理方法、电子设备及存储介质”的中国专利申请的优先权，该申请的全部内容通过引用结合在本申请中。
技术领域
本申请属于接收机技术领域，具体涉及一种数据处理方法、电子设备及存储介质。
背景技术
传统的接收机在进行大规模天线处理时，需要用到复杂的矩阵求逆运算，处理复杂度较高；此外，在使用通用处理器进行接收机处理时，复杂的接收机处理往往会造成通用处理器占用过多处理时间，从而影响整个系统的处理时序。
因此，在不降低接收机性能的前提下，尽可能降低接收机在进行大规模天线处理时的复杂度，是当前待解决的问题。
发明内容
本申请实施例提供一种数据处理方法、电子设备及存储介质，以解决传统的接收机在进行大规模天线处理时复杂度较高的问题。
第一方面，本申请实施例提供了一种数据处理方法，包括：确定接收机所接收的频域数据的协方差矩阵；根据所述协方差矩阵，确定所述接收机并行处理的分支的数量和每个所述分支对应的空域滤波权值，其中每个所述分支对应至少一个维度的天线；针对每个所述分支，根据所述分支对应的空域滤波权值对所述分支所对应数据进行降维处理，得到所述分支所对应的降维后数据；根据每个所述分支所对应的降维后数据，完成所述频域数据的处理操作。
第二方面，本申请实施例另提供了一种数据处理装置，包括：第一确定模块，用于确定接收机所接收的频域数据的协方差矩阵；第二确定模块，用于根据所述协方差矩阵，确 定所述接收机并行处理的分支的数量和每个所述分支对应的空域滤波权值，其中每个所述分支对应至少一个维度的天线；降维模块，用于针对每个所述分支，根据所述分支对应的空域滤波权值对所述分支所对应数据进行降维处理，得到所述分支所对应的降维后数据；处理模块，用于根据每个所述分支所对应的降维后数据，完成所述频域数据的处理操作。
第三方面，本申请实施例提供了一种电子设备，该电子设备包括处理器、存储器及存储在所述存储器上并可在所述处理器上运行的程序或指令，所述程序或指令被所述处理器执行时实现如第一方面所述的方法的步骤。
第四方面，本申请实施例提供了一种可读存储介质，所述可读存储介质上存储程序或指令，所述程序或指令被处理器执行时实现如第一方面所述的方法的步骤。
本申请实施例包括确定接收机所接收的频域数据的协方差矩阵；根据协方差矩阵，确定接收机并行处理的分支的数量和每个分支对应的空域滤波权值；根据分支对应的空域滤波权值对分支所对应数据进行降维处理，得到分支所对应的降维后数据；根据每个分支所对应的降维后数据，完成频域数据的处理操作。
附图说明
图1是本申请实施例中数据处理方法的流程示意图；
图2是本申请实施例中确定接收机并行处理的分支的数量和每个分支的空域滤波权值的流程示意图；
图3是本申请实施例中接收机的示意图之一；
图4是本申请实施例中接收机的示意图之二；
图5是本申请实施例中接收机的示意图之三；
图6是本申请实施例中数据处理装置的结构示意图；
图7是本申请实施例提供的电子设备的结构示意图。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
具体的，传统的接收机，例如多输入多输出(multiple-input multiple-output，MIMO)接收机是近年来数字通信领域重大的技术突破之一，它对提高无线通信系统的频谱利用率和信道容量有显著作用。MIMO系统实现了多个信号流的并行传输，与传统的单输入单输出系统相比较，在系统的接收端实现了每根接收天线的接收信号是多路发送天线信号的叠加。但是由于多天线的存在，消除空间干扰的空时合并器和信号检测的设计变得异常复杂。MIMO接收机与单天线相比，复杂性明显增加，例如MIMO信道估计会导致复杂性的增加，因为整个信道矩阵的每一条路径延时都需要技术跟踪和更新，而不是只跟踪和更新单个系数。因此，需要研究设计出一种复杂度低的大规模天线接收机。
针对此，申请人提供了一种接收机，能够对所接收的频域数据进行协方差矩阵计算，并对协方差矩阵进行特征值分解计算确定所接收频域数据的空域能量权值，并确定所接收频域数据空域能量方向的维度数目(即并行处理的分支数目)，从而能够根据各分支对应空域滤波权值对所接收频域数据进行并行处理，这样实现了根据接收数据的数据域协方差矩阵提取信号空域能量方向信息，避免了根据所接收信号信道估计进行空域滤波计算，且各个分支的能量维度的空域滤波处理可以并行计算，降低了算法处理的复杂度，同时保证了空域滤波计算的实时性与有效性。
下面结合附图，通过具体的实施例及其应用场景对本申请实施例提供的道具的产出方法进行详细地说明。
图1示出了本申请的一个实施例提供的数据处理方法，该方法可以由接收机执行，该方法包括如下步骤：
步骤101：确定接收机所接收的频域数据的协方差矩阵。
具体的，接收机在确定频域数据的协方差矩阵之前，可以对接收数据进行去循环前缀(Cyclic Prefix，CP)和快速傅里叶变换(Fast Fourier Transform，FFT)相关处理，获得初始频域数据，并对初始频域数据按照天线维度进行数据重排，得到重排后的频域数据，使得可以对重排后的频域数据进行协方差矩阵计算。
例如，初始频域数据的维度为[RE，Sym，Ka]，RE表示资源粒数目，Sym表示数据符号数和导频符号数的和值，Ka表示天线维度，重排后的频域数据Rsqu的维度[N,Ka]，N表示RE与Sym的乘积。作为一个示例，假设初始频域数据为3276×14×32，则重排后的频域数据为45864×32。
频域数据的协方差矩阵的公式可以表示为：
Nre表示资源粒的数目，表示频域数据Rsqu,i的共轭矩阵，Covsqu的维度为Ka×Ka，Ka表示天线的维度。
步骤102：根据所述协方差矩阵，确定所述接收机并行处理的分支的数量和每个所述分支对应的空域滤波权值。
其中，每个所述分支对应至少一个维度的天线，这实现了对天线按照维度进行选择划分。例如，假设分支的数量为3个，第一个分支可以对应2个维度的天线(即两根天线)，第二个分支可以对应4个维度的天线，第三个分支可以对应8个维度的天线；再例如，假设分支的数量为8个，每个分支均可以对应1个维度的天线。
根据所确定的协方差矩阵，确定接收机并行处理的分支的数量和每个分支对应的空域滤波权值，实现了对并行处理的分支数量的选择以及提取空域能量方向信息，使得各个分支能够并行进行空域滤波处理，保证了空域滤波计算的实时性和有效性，降低了算法处理的复杂度。
步骤103：针对每个所述分支，根据所述分支对应的空域滤波权值对所述分支所对应数据进行降维处理，得到所述分支所对应的降维后数据。
具体的，针对每个分支，根据分支对应的空域滤波权值对分支所对应数据进行降维处理，使得后续能够根据降维后的数据进行处理计算，从而降低了处理复杂度。
具体的，在一个实施例中，根据所述分支对应的空域滤波权值对所述分支所对应数据进行降维处理时，可以包括：根据所述分支对应的空域滤波权值，通过下述公式，对所述分支所对应数据进行降维处理，得到所述分支所对应的降维后数据：
R′squ，i＝Rsqu×Gi；
其中，R′squ，i表示第i路分支对应的降维后数据，Gi表示第i路分支对应的空域滤波权值，Rsqu表示所述频域数据。当然i的取值范围为0至M，M为分支的总数量。
这样通过上述公式实现了频域数据的降维。
步骤104：根据每个所述分支所对应的降维后数据，完成所述频域数据的处理操作。
具体的，根据每个分支所对应的降维后数据，完成频域数据的处理操作，该处理操作可以为解调、解速率匹配、低密度奇偶校验码(Low Density Parity Check Code，LDPC)译码、解循环冗余校验(Cyclic Redundancy Check，CRC)等操作，从而完成接收机对频域数据的整个处理过程。
这样，本实施例通过根据协方差矩阵确定接收机并行处理的分支的数量和每个分支对应的空域滤波权值，根据每个分支对应的空域滤波权值对该分支所对应数据进行降维处理， 得到该分支所对应的降维后数据，并根据每个分支所对应的降维后数据，完成频域数据的处理操作，实现了空域滤波处理和分支并行计算，降低了算法处理的复杂度，同时保证了空域滤波计算的实时性与有效性。
此外，在一种实现方式中，如图2所示，根据所述协方差矩阵，确定所述接收机并行处理的分支的数量和每个所述分支对应的空域滤波权值时，可以包括如下步骤：
步骤201：对协方差矩阵进行奇异值分解(Singular Value Decomposition，SVD)，得到S矩阵和V矩阵。
具体的，在该步骤中，可以对协方差矩阵进行特征值分解计算，例如进行SVD分解：[U,S,V]＝svd(Covsqu)，从而得到S矩阵和V矩阵。
步骤202：根据所述S矩阵，确定所述接收机并行处理的分支的数量。
具体的，在一个实施例中，根据所述S矩阵，确定所述接收机并行处理的分支的数量时，可以根据所述S矩阵的对角元素值，确定能量阈值；从所述S矩阵的对角元素中确定大于所述能量阈值的对角元素值的个数，其中所述个数的最大值小于天线的天线维度数；根据所述个数确定所述接收机并行处理的分支的数量。
其中，根据所述S矩阵的对角元素值，确定能量阈值时，可以通过下述公式确定所述能量阈值：
其中，ThrP表示所述能量阈值，Ka表示所述天线的天线维度，S(，i)表示所述S矩阵，Pthr表示预设的能量门限值。
此外，在确定能量阈值后，可以从S矩阵的对角元素中确定大于能量阈值的对角元素值的个数，该个数需要满足小于天线的天线维度数，然后根据该个数确定接收机并行处理的分支的数量。
例如，假设天线的天线维度数为32，且预先设定所确定的能量维度(即大于能量阈值的对角元素值的个数)的最大值不超过预设比例的天线维度数，该预设比例为1/4，则所确定的对角元素值的个数的最大值为8，这样通过该种方式实现了对能量维度的选择。
此外，在根据所确定的对角元素值的个数确定接收机并行处理的分支的数量时，可以根据需求进行限定，例如接续上述示例，假设所确定的对角元素值的个数的最大值为8，则接收机并行处理的分支的数量可以为3、4或8，在此不对此进行具体限定。
步骤203：根据所述V矩阵，确定每个所述分支对应的空域滤波权值。
具体的，在根据V矩阵，确定每个分支对应的空域滤波权值时，可以针对每个所述分支，确定所述V矩阵中的目标列，其中所述目标列包括第1列至第N列，N为所述分支对应的天线维度数；将所述V矩阵的所有行中位于所述目标列的数据确定为所述分支所对应的空域滤波权值。
例如，作为一个示例，假设分支的数量为4，包括A、B和C三路分支，分支A对应的天线维度数为2，分支B对应的天线维度数为4，分支C对应的天线维度数为8。针对分支A，则可以确定目标列为第1列至第2列，此时可以将V矩阵的所有行中位于第1列至第2列的数据确定为分支A所对应的空域滤波权值；针对分支B，则可以确定目标列为第1列至第4列，此时可以将V矩阵的所有行中位于第1列至第4列的数据确定为分支B所对应的空域滤波权值；针对分支C，则可以确定目标列为第1列至第8列，此时可以将V矩阵的所有行中位于第1列至第8列的数据确定为分支C所对应的空域滤波权值。
此时，每个分支对应的空域滤波权值可以通过下述公式表示：Gi＝V(:,1:2i)；其中，Gi表示第i路分支对应的空域滤波权值，V表示所述V矩阵，i的取值范围为0至M，M为所述分支的总数量，公式中逗号用于隔开前后部分，逗号前冒号表示选中V矩阵中的所有行，逗号后冒号表示目标列的范围为从1至2i。
这样，通过对协方差矩阵进行奇异值分解SVD，得到S矩阵和V矩阵，实现了通过S矩阵确定接收机并行处理的分支的数量，通过V矩阵确定每个分支对应的空域滤波权值，使得接收机能够并行处理不同能量维度的空域滤波，并能够对每个分支进行降维，保证了空域滤波计算的实时性与有效性。
此外，在另一种实现方式中，根据每个所述分支所对应的降维后数据，完成所述频域数据的处理操作时，可以包括如下步骤：
对每个所述分支所对应的降维后数据进行信道估计与均衡处理，得到每个所述分支对应的均衡后数据；根据每个所述分支对应的均衡后数据，确定目标均衡数据；对所述目标均衡数据进行处理，以完成对所述频域数据的处理操作。
具体的，对每个分支所对应的降维后数据进行信道估计与均衡处理，得到每个分支对应的均衡后数据的过程可以参见传统接收机处理过程，在此不再进行具体赘述。
此外，根据每个所述分支对应的均衡后数据，确定目标均衡数据时，可以包括下述任一种方式：
其一方式：对每个所述分支所应的均衡后数据进行误差向量幅度(Error Vector Magnitude，EVM)计算，根据每个所述分支对应的EVM值，确定所述目标均衡数据。
在该方式中，可以根据分支对应的EVM值，确定目标均衡数据。
具体的，根据每个分支对应的EVM值，确定目标均衡数据时，可以将最小EVM值所对应的均衡后数据确定为所述目标均衡数据。即在此选择EVM最小的分支为最优分支，并将EVM最小的分支所对应的均衡后数据确定为所述目标均衡数据；
即[min_index]＝min(EVM(C‘points,i))；
其中，(C‘points,i)表示第i个分支对应的均衡后数据，EVM(C‘points,i)表示第i个分支所对应均衡后数据的EVM值，min_index表示EVM最小的分支的索引。
C‘points(min_index)表示EVM最小的分支的索引所对应的均衡后数据，表示目标均衡数据。
或者，根据每个分支对应的EVM值，确定目标均衡数据时，可以从所有所述EVM值中选择满足预设EVM阈值的至少一个目标EVM值，并将所述至少一个目标EVM值分别对应的均衡后数据的和值确定为所述目标均衡数据。
具体的，满足预设EVM阈值可以为小于预设EVM阈值；
即[index]＝find(EVM(C‘points,i)<EVMThr)；
其中，EVMThr表示预设EVM阈值，该EVM阈值可以根据需求进行设定，index表示至少一个目标EVM值所对应的索引，C‘points(index)表示所确定的index分别对应的均衡后数据，表示目标均衡数据。
这样，通过对每个所述分支所应的均衡后数据进行误差向量幅度EVM计算，根据每个分支对应的EVM值确定目标均衡数据，实现了对分支的选择操作，增强了算法灵活性。
其二方式：确定所有所述分支对应的均衡后数据的平均值，并将所述平均值确定为所述目标均衡数据。
在该方式中，将所有所述分支对应的均衡后数据的平均值确定为目标均衡数据，即其中M为所有分支的数量。
这样，通过上述任一方式均实现了目标均衡数据的确定过程。
此外，需要说明的是，在确定目标均衡数据后，可以进行行后续的解调、解速率匹配、低密度奇偶校验码(Low Density Parity Check Code，LDPC)译码、解循环冗余校验(Cyclic Redundancy Check，CRC)等操作，从而完成整个接收机处理过程。
下面通过具体实施例对本申请进行详细说明。
实施例一：
假设应用场景为单终端单流，参数如表1所示。
表1
参见图3所示的接收机结构示意图，接收机包括前端处理模块、数据域空域滤波功能模块、接收机并行处理模块和比特级处理模块。其中，具体处理步骤如下：
步骤一：接收机前端处理模块，具体执行如下处理流程：
对接收数据进行去CP和FFT相关处理，获得频域接收数据Rdata，Rdata的维度为3276×14×32；将Rdata按照天线维度进行数据重排，重排后的数据为Rsqu，Rsqu的维度为45864×32。
步骤二：数据域空域滤波功能模块进行数据域空域滤波处理，该模块具体执行如下处理流程：
根据所接收频域数据，进行数据域协方差矩阵计算处理，计算接收数据Rsqu的协方差矩阵Covsqu，Covsqu的维度为32×32；
然后，根据数据域协方差矩阵，进行特征值分解计算，这里对协方差矩阵Covsqu进行奇异值分解SVD分解：
[U,S,V]＝svd(Covsqu)
再然后，计算接收的频域数据空域能量方向维度数目N，具体包括：
根据S矩阵对角元素能量判决，计算能量阈值：
其中ThrP为能量门限值，根据经验值配置，可以配置为3dB。
然后根据能量阈值ThrP，对S矩阵对角元素与能量阈值进行判决处理，选择大于能量阈值的能量维度，N为满足阈值要求的维度数目，并对N值进行最大值限定，N最大不超过1/4天线数目，本实施例N值确定为4。
再然后，根据一定的判决准则选择并行处理分支维度与对应空域滤波处理权值，例如可以根据N值，确定本实施例的并行处理分支数为3路，每个分支对应的维度Mi分别为1/2/4(即一路对应1根天线，一路对应2根天线，一路对应4根天线)，并分别获取对应的分支空域滤波权值，其中每个分支对应的空域滤波权值为：
Gi＝V(:,1:2i-1)；
其中，Gi表示第i路分支对应的空域滤波权值，V表示V矩阵，i的取值范围为0至M，M为所述分支的总数量，公式中逗号用于隔开前后部分，逗号前冒号表示选中V矩阵中的所有行，逗号后冒号表示目标列的范围为从1至2i-1。
步骤三：接收机并行处理模块，具体执行如下处理流程：
空域滤波处理：各个分支根据各分支的空域滤波权值，分别进行空域滤波降维处理，获取降维后数据R’squ,i，降维后数据的维度为45864×Mi，Mi表示第i路对应的天线维度数。
R′squ，i＝Rsqu×Gi，其中，R′squ，i表示第i路分支对应的降维后数据，Gi表示第i路分支对应的空域滤波权值，Rsqu表示所述频域数据。
传统接收机处理：对R’squ数据进行传统接收机信道估计与均衡处理，获取均衡后数据C’points,i。
分支选择功能处理：对并行处理的各个分支均衡后数据C’points,i进行EVM计算，选择EVM最小的分支最为最优分支，并将EVM最小的分支所对应均衡后数据确定为目标均衡数据。
步骤四：比特级处理模块，具体执行如下处理流程：
将目标均衡数据送入比特级处理模块，进行后续的解调、解速率匹配、LDPC译码、CRC码等操作，从而完成整个接收机处理过程。
实施例二：
假设应用场景为单终端单流，参数如表2所示。
表2
参见图4所示的接收机结构示意图，接收机包括前端处理模块、数据域空域滤波功能模块、接收机并行处理模块和比特级处理模块。其中，具体处理步骤如下:
步骤一：接收机前端处理模块，具体执行如下处理流程：
对接收数据进行去CP和FFT相关处理，获得频域接收数据Rdata，Rdata的维度为3276×14×32；将Rdata按照天线维度进行数据重排，重排后的数据为Rsqu，Rsqu的维度为45864×32；
步骤二：数据域空域滤波功能模块进行数据域空域滤波处理，该模块具体执行如下处理流程：
根据所接收频域数据，进行数据域协方差矩阵计算处理，计算接收数据Rsqu的协方差矩阵Covsqu，Covsqu的维度为32×32；
然后，根据数据域协方差矩阵，进行特征值分解计算，这里对协方差矩阵Covsqu进行奇异值分解SVD分解：
[U,S,V]＝svd(Covsqu)
再然后，计算接收信号空域能量方向维度数目N，具体包括；
根据S矩阵对角元素能量判决，计算能量阈值：
其中ThrP为能量门限值，根据经验值配置，可以配置为3dB。
根据能量阈值Thrp，对S矩阵对角元素与能量阈值进行判决处理，选择大于能量阈值的能量维度，N为满足阈值要求的维度数目，并对N值进行最大值限定，N最大不超过1/4天线数目，本实施例N值确定为8。
再然后，根据一定的判决准则选择并行处理分支维度与对应空域滤波处理权值，例如可以根据N值，确定本实施例的并行处理分支数为8路，每个分支对应的维度Mi分别为1/1/1/1/1/1/1/1，并分别获取对应的分支空域滤波权值，其中每个分支对应的空域滤波权值为：
Gi＝V(:,i)。
其中，Gi表示第i路分支对应的空域滤波权值，V表示V矩阵，i的取值范围为0至M，M为所述分支的总数量，公式中逗号用于隔开前后部分，逗号前冒号表示选中V矩阵中的所有行，逗号后冒号表示目标列的取值为i。
步骤三：接收机并行处理模块，具体执行如下处理流程：
空域滤波处理：各个分支根据各分支的空域滤波权值，分别进行空域滤波降维处理，获取降维后数据R’squ,i，降维后数据的维度为45864×Mi。
R′squ，i＝Rsqu×Gi；其中，R′squ，i表示第i路分支对应的降维后数据，Gi表示第i路分支对应的空域滤波权值，Rsqu表示所述频域数据。
传统接收机处理：对R’squ数据进行传统接收机信道估计与均衡处理，获取均衡后数据C’points,i。
分支选择功能处理：对并行处理的各个分支均衡后数据C’points,i进行平均计算，输出最终的均衡数据
N表示所有分支的数量。
步骤四：比特级处理模块，具体执行如下处理流程：
将送入比特级处理模块，进行后续的解调、解速率匹配、LDPC译码、 解CRC等操作，从而完成整个接收机处理。
实施例三：
假设应用场景为单终端两流，参数如表3所示。
表3
参见图5所示的接收机结构示意图，接收机包括前端处理模块、数据域空域滤波功能模块、接收机并行处理模块和比特级处理模块。其中，具体处理步骤如下：
步骤一：接收机前端处理模块，具体执行如下处理流程：
对接收数据进行去CP和FFT相关处理，获得频域接收数据Rdata，Rdata的维度为3276×14×64；将Rdata按照天线维度进行数据重排，重排后的数据为Rsqu，Rsqu的维度为45864×64。
步骤二：数据域空域滤波功能模块进行数据域空域滤波处理，该模块具体执行如下处理流程：
根据所接收频域数据，进行数据域协方差矩阵计算处理，计算接收数据Rsqu的协方差矩阵Covsqu，Covsqu的维度为64×64；
然后，根据数据域协方差矩阵，进行特征值分解计算，这里对协方差矩阵Covsqu进行奇异值分解SVD分解：
[U,S,V]＝svd(Covsqu)
再然后，计算接收信号空域能量方向维度数目N，具体包括：
根据S矩阵对角元素能量判决，计算能量阈值：
其中ThrP为能量门限值，根据经验值配置，可以配置为3dB。
然后根据能量阈值ThrP，对S矩阵对角元素与能量阈值进行判决处理，选择大于能量阈值的能量维度，N为满足阈值要求的维度数目，并对N值进行最大值限定，N最大不超过1/4天线数目，本实施例N值确定为8。
再然后，根据一定的判决准则选择并行处理分支维度与对应空域滤波处理权值，例如可以根据N值，确定本实施例的并行处理分支数为3路，每个分支对应的维度Mi分别为2/4/8(即一路对应2根天线，一路对应4根天线，一路对应8根天线)，并分别获取对应的分支空域滤波权值，其中每个分支对应的空域滤波权值为：
Gi＝V(:,1:2i)；
其中，Gi表示第i路分支对应的空域滤波权值，V表示V矩阵，i的取值范围为0至M，M为所述分支的总数量，公式中逗号用于隔开前后部分，逗号前冒号表示选中V矩阵中的所有行，逗号后冒号表示目标列的范围为从1至2i。
步骤三：接收机并行处理模块，具体执行如下处理流程：
空域滤波处理：各个分支根据各分支的空域滤波权值，分别进行空域滤波降维处理，获取降维后数据R’squ,i，其维度为45864×Mi。
R′squ，i＝Rsqu×Gi，其中，R′squ，i表示第i路分支对应的降维后数据，Gi表示第i路分支对应的空域滤波权值，Rsqu表示所述频域数据。
传统接收机处理：对R’squ数据进行传统接收机信道估计与均衡处理，获取均衡后数据C’points,i。
分支选择功能处理：对并行处理的各个分支均衡后数据C’points,i进行EVM计算，择EVM值满足阈值要求的分支，进行合并处理，得到目标均衡数据。
即[index]＝find(EVM(C‘ points,i)<EVMThr)；
其中，EVMThr表示预设EVM阈值，index表示至少一个目标EVM值所对应的索引，C‘points(index)表示所确定的index分别对应的均衡后数据，表示目标均衡数据。
步骤四：比特级处理模块，具体执行如下处理流程：
将目标均衡数据送入比特级处理模块，进行后续的解调、解速率匹配、LDPC译码、CRC码等操作，从而完成整个接收机处理过程。
这样，本实施例根据接收信号的数据域协方差矩阵提取空域能量方向信息，避免了根据参考信号信道估计进行空域滤波计算，实现了数据域空域滤波处理功能与并行接收机处理功能，能够提升大规模阵列天线下的接收机性能，同时降低大规模阵列天线的处理复杂度。
图6示出本申请的一个实施例提供的一种数据处理装置的结构示意图。如图6所示，一种数据处理装置包括：
第一确定模块601，用于确定接收机所接收的频域数据的协方差矩阵；
第二确定模块602，用于根据所述协方差矩阵，确定所述接收机并行处理的分支的数量和每个所述分支对应的空域滤波权值，其中每个所述分支对应至少一个维度的天线；
降维模块603，用于针对每个所述分支，根据所述分支对应的空域滤波权值对所述分支所对应数据进行降维处理，得到所述分支所对应的降维后数据；
处理模块604，用于根据每个所述分支所对应的降维后数据，完成所述频域数据的处理操作。
在一种实现方式中，第二确定模块602，用于对所述协方差矩阵进行奇异值分解SVD，得到S矩阵和V矩阵；根据所述S矩阵，确定所述接收机并行处理的分支的数量；根据所述V矩阵，确定每个所述分支对应的空域滤波权值。
在一种实现方式中，第二确定模块602，用于根据所述S矩阵的对角元素值，确定能量阈值；从所述S矩阵的对角元素中确定大于所述能量阈值的对角元素值的个数，其中所述个数的最大值小于天线的天线维度数；根据所述个数确定所述接收机并行处理的分支的数量。
在一种实现方式中，第二确定模块602，用于通过下述公式确定所述能量阈值：
其中，ThrP表示所述能量阈值，Ka表示天线的天线维度，S(i，i)表示所述S矩阵，Pthr表示预设的能量门限值。
在一种实现方式中，第二确定模块602，用于针对每个所述分支，确定所述V矩阵中的目标列，其中所述目标列包括第1列至第N列，N为所述分支对应的天线维度数；将所述V矩阵的所有行中位于所述目标列的数据确定为所述分支所对应的空域滤波权值。
在一种实现方式中，降维模块603用于，根据所述分支对应的空域滤波权值，通过下述公式，对所述分支所对应数据进行降维处理，得到所述分支所对应的降维后数据：
R′squ，i＝Rsqu×Gi；
其中，R′squ，i表示第i路分支对应的降维后数据，Gi表示第i路分支对应的空域滤波权值，Rsqu表示所述频域数据。
在一种实现方式中，处理模块604用于，对每个所述分支所对应的降维后数据进行信道估计与均衡处理，得到每个所述分支对应的均衡后数据；根据每个所述分支对应的均衡后数据，确定目标均衡数据；对所述目标均衡数据进行处理，以完成对所述频域数据的处理操作。
在一种实现方式中，处理模块604用于，对每个所述分支所应的均衡后数据进行误差向量幅度EVM计算；根据每个所述分支对应的EVM值，确定所述目标均衡数据。
在一种实现方式中，处理模块604用于，将最小EVM值所对应的均衡后数据确定为所述目标均衡数据；或者，从所有所述EVM值中选择满足预设EVM阈值的至少一个目标EVM值，并将所述至少一个目标EVM值分别对应的均衡后数据的和值确定为所述目标均衡数据。
在一种实现方式中，处理模块604用于，确定所有所述分支对应的均衡后数据的平均值，并将所述平均值确定为所述目标均衡数据。
本申请实施例提供的数据处理装置能够实现图1至图5的方法实施例实现的各个过程，为避免重复，这里不再赘述。
需要说明的是，本说明书中关于数据处理装置的实施例与本说明书中关于数据处理方法的实施例基于同一发明构思，因此关于数据处理装置实施例的具体实施可以参见前述对应的关于数据处理方法实施例的实施，重复之处不再赘述。
本申请实施例中的数据处理装置可以是装置，也可以是接收机中的部件、集成电路、或芯片。本申请实施例中的数据处理装置可以为具有操作系统的装置。该操作系统可以为安卓(Android)操作系统，可以为ios操作系统，还可以为其他可能的操作系统，本申请实施例不作具体限定。
基于相同的技术构思，本申请实施例还提供了一种电子设备，该电子设备用于执行上述的数据处理方法，图7为实现本申请各个实施例的一种电子设备的结构示意图。电子设备可因配置或性能不同而产生比较大的差异，可以包括处理器(processor)710、通信接口(Communications Interface)720、存储器(memory)730和通信总线740，其中，处理器710， 通信接口720，存储器730通过通信总线740完成相互间的通信。处理器710可以调用存储在存储器730上并可在处理器710上运行的计算机程序，以执行下述步骤：确定接收机所接收的频域数据的协方差矩阵；根据所述协方差矩阵，确定所述接收机并行处理的分支的数量和每个所述分支对应的空域滤波权值，其中每个所述分支对应至少一个维度的天线；针对每个所述分支，根据所述分支对应的空域滤波权值对所述分支所对应数据进行降维处理，得到所述分支所对应的降维后数据；根据每个所述分支所对应的降维后数据，完成所述频域数据的处理操作。
具体执行步骤可以参见上述数据处理方法实施例的各个步骤，且能达到相同的技术效果，为避免重复，这里不再赘述。
以上电子设备结构并不构成对电子设备的限定，电子设备可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置，例如，输入单元，可以包括图形处理器(Graphics Processing Unit，GPU)和麦克风，显示单元可以采用液晶显示器、有机发光二极管等形式来配置显示面板。用户输入单元包括触控面板以及其他输入设备中的至少一种。触控面板也称为触摸屏。其他输入设备可以包括但不限于物理键盘、功能键(比如音量控制按键、开关按键等)、轨迹球、鼠标、操作杆，在此不再赘述。
存储器可用于存储软件程序以及各种数据。存储器可主要包括存储程序或指令的第一存储区和存储数据的第二存储区，其中，第一存储区可存储操作系统、至少一个功能所需的应用程序或指令(比如声音播放功能、图像播放功能等)等。此外，存储器可以包括易失性存储器或非易失性存储器，或者，存储器可以包括易失性和非易失性存储器两者。其中，非易失性存储器可以是只读存储器(Read-Only Memory，ROM)、可编程只读存储器(Programmable ROM，PROM)、可擦除可编程只读存储器(Erasable PROM，EPROM)、电可擦除可编程只读存储器(Electrically EPROM，EEPROM)或闪存。易失性存储器可以是随机存取存储器(Random Access Memory，RAM)，静态随机存取存储器(Static RAM，SRAM)、动态随机存取存储器(Dynamic RAM，DRAM)、同步动态随机存取存储器(Synchronous DRAM，SDRAM)、双倍数据速率同步动态随机存取存储器(Double Data Rate SDRAM，DDRSDRAM)、增强型同步动态随机存取存储器(Enhanced SDRAM，ESDRAM)、同步连接动态随机存取存储器(Synch link DRAM，SLDRAM)和直接内存总线随机存取存储器(Direct Rambus RAM，DRRAM)。
处理器可包括一个或多个处理单元；可选的，处理器集成应用处理器和调制解调处理器，其中，应用处理器主要处理涉及操作系统、用户界面和应用程序等的操作，调制解调处理器主要处理无线通信信号，如基带处理器。可以理解的是，上述调制解调处理器也可 以不集成到处理器中。
本申请实施例还提供一种可读存储介质，所述可读存储介质上存储有程序或指令，该程序或指令被处理器执行时实现上述数据处理方法实施例的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。
其中，所述处理器为上述实施例中所述的电子设备中的处理器。所述可读存储介质，包括计算机可读存储介质，如计算机只读存储器(Read-Only Memory，ROM)、随机存取存储器(Random Access Memory，RAM)、磁碟或者光盘等。
本申请实施例另提供了一种芯片，所述芯片包括处理器和通信接口，所述通信接口和所述处理器耦合，所述处理器用于运行程序或指令，实现上述方法实施例的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。
应理解，本申请实施例提到的芯片还可以称为系统级芯片、系统芯片、芯片系统或片上系统芯片等。
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者装置不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者装置所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者装置中还存在另外的相同要素。此外，需要指出的是，本申请实施方式中的方法和装置的范围不限按示出或讨论的顺序来执行功能，还可包括根据所涉及的功能按基本同时的方式或按相反的顺序来执行功能，例如，可以按不同于所描述的次序来执行所描述的方法，并且还可以添加、省去、或组合各种步骤。另外，参照某些示例所描述的特征可在其他示例中被组合。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端(可以是手机，计算机，服务器，空调器，或者网络设备等)执行本申请各个实施例所述的方法。
上面结合附图对本申请的实施例进行了描述，但是本申请并不局限于上述的具体实施方式，上述的具体实施方式仅仅是示意性的，而不是限制性的，本领域的普通技术人员在本申请的启示下，在不脱离本申请宗旨和权利要求所保护的范围情况下，还可做出很多形式，均属于本申请的保护之内。

Claims (12)

  1. 一种数据处理方法，其中，包括：
    确定接收机所接收的频域数据的协方差矩阵；
    根据所述协方差矩阵，确定所述接收机并行处理的分支的数量和每个所述分支对应的空域滤波权值，其中每个所述分支对应至少一个维度的天线；
    针对每个所述分支，根据所述分支对应的空域滤波权值对所述分支所对应数据进行降维处理，得到所述分支所对应的降维后数据；
    根据每个所述分支所对应的降维后数据，完成所述频域数据的处理操作。
  2. 根据权利要求1所述的数据处理方法，其中，所述根据所述协方差矩阵，确定所述接收机并行处理的分支的数量和每个所述分支对应的空域滤波权值，包括：
    对所述协方差矩阵进行奇异值分解SVD，得到S矩阵和V矩阵；
    根据所述S矩阵，确定所述接收机并行处理的分支的数量；
    根据所述V矩阵，确定每个所述分支对应的空域滤波权值。
  3. 根据权利要求2所述的数据处理方法，其中，所述根据所述S矩阵，确定所述接收机并行处理的分支的数量，包括：
    根据所述S矩阵的对角元素值，确定能量阈值；
    从所述S矩阵的对角元素中确定大于所述能量阈值的对角元素值的个数，其中所述个数的最大值小于天线的天线维度数；
    根据所述个数确定所述接收机并行处理的分支的数量。
  4. 根据权利要求3所述的数据处理方法，其中，所述根据所述S矩阵的对角元素值，确定能量阈值，包括：
    通过下述公式确定所述能量阈值：
    其中，ThrP表示所述能量阈值，Ka表示所述天线的天线维度，S(i，i)表示所述S矩阵，Pthr表示预设的能量门限值。
  5. 根据权利要求2所述的数据处理方法，其中，所述根据所述V矩阵，确定每个所述分支对应的空域滤波权值，包括：
    针对每个所述分支，确定所述V矩阵中的目标列，其中所述目标列包括第1列至第N列，N为所述分支对应的天线维度数；
    将所述V矩阵的所有行中位于所述目标列的数据确定为所述分支所对应的空域滤波权值。
  6. 根据权利要求1所述的数据处理方法，其中，所述针对每个所述分支，根据所述分支对应的空域滤波权值对所述分支所对应数据进行降维处理，得到所述分支所对应的降维后数据，包括：
    根据所述分支对应的空域滤波权值，通过下述公式，对所述分支所对应数据进行降维处理，得到所述分支所对应的降维后数据：
    R′squ，i＝Rsqu×Gi；
    其中，R′squ，i表示第i路分支对应的降维后数据，Gi表示第i路分支对应的空域滤波权值，Rsqu表示所述频域数据。
  7. 根据权利要求1所述的数据处理方法，其中，所述根据每个所述分支所对应的降维后数据，完成所述频域数据的处理操作，包括：
    对每个所述分支所对应的降维后数据进行信道估计与均衡处理，得到每个所述分支对应的均衡后数据；
    根据每个所述分支对应的均衡后数据，确定目标均衡数据；
    对所述目标均衡数据进行处理，以完成对所述频域数据的处理操作。
  8. 根据权利要求7所述的数据处理方法，其中，所述根据每个所述分支对应的均衡后数据，确定目标均衡数据，包括：
    对每个所述分支所应的均衡后数据进行误差向量幅度EVM计算；
    根据每个所述分支对应的EVM值，确定所述目标均衡数据。
  9. 根据权利要求8所述的数据处理方法，其中，所述根据每个所述分支对应的EVM值，确定所述目标均衡数据，包括：
    将最小EVM值所对应的均衡后数据确定为所述目标均衡数据；或者，
    从所有所述EVM值中选择满足预设EVM阈值的至少一个目标EVM值，并将所述至少一个目标EVM值分别对应的均衡后数据的和值确定为所述目标均衡数据。
  10. 根据权利要求7所述的数据处理方法，其中，所述根据每个所述分支对应的均衡后数据，确定目标均衡数据，包括：
    确定所有所述分支对应的均衡后数据的平均值，并将所述平均值确定为所述目标均衡数据。
  11. 一种电子设备，其中，包括处理器，存储器及存储在所述存储器上并可在所述处理器上运行的程序或指令，所述程序或指令被所述处理器执行时实现如权利要求1-10任一项所述的数据处理方法的步骤。
  12. 一种可读存储介质，其中，所述可读存储介质上存储程序或指令，所述程序或指令被处理器执行时实现如权利要求1-10任一项所述的数据处理方法的步骤。
PCT/CN2023/113106 2022-09-02 2023-08-15 数据处理方法、电子设备及存储介质 WO2024046112A1 (zh)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101556328A (zh) * 2009-05-08 2009-10-14 西安电子科技大学 基于杂波协方差矩阵的机载雷达空时二维滤波器构建方法
CN113866796A (zh) * 2021-09-10 2021-12-31 西安电子科技大学 基于空域滤波与波束成形的导航接收机抗干扰方法
CN114403903A (zh) * 2022-01-14 2022-04-29 杭州电子科技大学 面向跨被试rsvp的多特征低维子空间erp检测方法

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101556328A (zh) * 2009-05-08 2009-10-14 西安电子科技大学 基于杂波协方差矩阵的机载雷达空时二维滤波器构建方法
CN113866796A (zh) * 2021-09-10 2021-12-31 西安电子科技大学 基于空域滤波与波束成形的导航接收机抗干扰方法
CN114403903A (zh) * 2022-01-14 2022-04-29 杭州电子科技大学 面向跨被试rsvp的多特征低维子空间erp检测方法

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