CN114584430B - Method, base station and storage medium for dimension reduction optimization of uplink receiver - Google Patents
Method, base station and storage medium for dimension reduction optimization of uplink receiver Download PDFInfo
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Abstract
The invention discloses a method, a base station and a storage medium for dimension reduction optimization of an uplink receiver, which are used for solving the technical problems of low working efficiency and high power consumption of the uplink receiver in the prior art, and the method comprises the following steps: when determining that a channel Sounding Reference Signal (SRS) exists, estimating a corresponding SRS channel matrix according to the SRS; according to SRS channel matrix, performing eigenvalue decomposition on conjugate symmetric matrix formed by uplink channel matrix to obtain eigenvector matrix with eigenvalue arranged in descending order, wherein eigenvector matrix is unitary matrix of R×R, R is antenna number of uplink receiver; taking the elements of the first M rows in the feature vector matrix to form a dimension reduction matrix; wherein M is determined according to the data stream number received by the uplink receiver and the characteristic value corresponding to the characteristic vector matrix; and performing dimension reduction processing on the data received by the uplink receiver by using the dimension reduction matrix.
Description
Technical Field
The present invention relates to the field of communications, and in particular, to a method, a base station, and a storage medium for dimension reduction optimization of an uplink receiver.
Background
In the field of communications, after an antenna receives a signal, the received signal needs to be decoded to learn what is transmitted therein.
The uplink receiver usually needs to perform correlation processing on signals received by the antenna before decoding, such as channel estimation, minimum Mean square error (Minimum Mean SquareerrorEstimation, MMSE) multiple input multiple output (Multi Input Multi Output, MIMO) equalization, etc., and as the antenna scale increases, the uplink receiver will face a large amount of high-dimensional matrix operations when performing correlation processing, and as the operand is increased in geometric multiple, the working efficiency and power consumption of the uplink receiver will be reduced.
Disclosure of Invention
The invention provides a method, a base station and a storage medium for dimension reduction optimization of an uplink receiver, which are used for solving the technical problems of low working efficiency and high power consumption of the uplink receiver in the prior art.
In order to solve the above technical problems, a first aspect of the present invention provides a method for dimension reduction optimization of an uplink receiver, which includes the following steps:
estimating a corresponding SRS channel matrix according to the SRS when the existence of a channel sounding reference signal SRS is determined;
According to the SRS channel matrix, performing eigenvalue decomposition on a conjugate symmetric matrix formed by an uplink channel matrix to obtain an eigenvector matrix with eigenvalues arranged in descending order, wherein the eigenvector matrix is a unitary matrix of R multiplied by R, and R is the antenna number of an uplink receiver;
taking the elements of the first M rows in the feature vector matrix to form a dimension reduction matrix; wherein, the M is determined according to the data stream number received by the uplink receiver and the eigenvalue corresponding to the eigenvector matrix;
and performing dimension reduction processing on the data received by the uplink receiver by using the dimension reduction matrix.
The feature vector matrix is obtained by carrying out feature value decomposition on a conjugate symmetric matrix formed by an uplink channel matrix according to an SRS channel matrix, and feature values corresponding to the feature vector matrix are arranged in descending order, so that feature vectors in the feature vector matrix are arranged from high to low according to signal quality, elements of the first M rows in the feature vector matrix are taken to form a dimension reduction matrix, M rows of feature vectors with the best signal quality are obtained, a codebook in the dimension reduction matrix is matched with a signal environment corresponding to an actual signal, the signal transmission almost same as that of the original uplink channel matrix can be completed by using fewer branches, and the number of elements in the dimension reduction matrix is smaller than that of the original uplink channel matrix, so that the operation amount required in processing data received by an uplink receiver is less, the operation efficiency can be effectively improved, and the power consumption of the uplink receiver can be reduced.
In a possible implementation manner, according to the SRS channel matrix, eigenvalue decomposition is performed on a conjugate symmetric matrix formed by an uplink channel matrix to obtain an eigenvector matrix with eigenvalues arranged in descending order, including:
determining the conjugate symmetry matrix based on the SRS channel matrix and a precoding matrix of an uplink channel;
and carrying out eigenvalue decomposition on the conjugate symmetric matrix by using an exponentiation method to obtain the eigenvector matrix.
In a possible implementation manner, the determining the conjugate symmetric matrix based on the SRS channel matrix and a precoding matrix of an uplink channel includes:
if the precoding matrix is a known codebook sent to the terminal by the base station, calculating the uplink channel matrix according to the SRS channel matrix and the precoding matrix;
multiplying the uplink channel matrix with the corresponding transposed matrix to obtain a first matrix;
and weighting diagonal elements of the first matrix to obtain the conjugate symmetric matrix.
In a possible implementation manner, the determining the conjugate symmetric matrix based on the SRS channel matrix and a precoding matrix of an uplink channel includes:
if the precoding matrix is not transmitted by the base station and the precoding matrix is a unitary matrix, calculating the conjugate symmetric matrix according to the SRS channel matrix.
In a possible implementation manner, calculating the conjugate symmetry matrix according to the SRS channel matrix includes:
multiplying the SRS channel matrix with the corresponding transposed matrix to obtain a second matrix;
and weighting diagonal elements of the second matrix to obtain the conjugate symmetric matrix.
In a possible implementation manner, the minimum value of M is the number of data streams, and the maximum value of M is a serial number corresponding to the maximum feature value selected from the feature values smaller than the set value.
In a possible implementation manner, when it is determined that the channel sounding reference signal SRS does not exist, the data received by the uplink receiver is subjected to dimension reduction processing by using a preset fixed dimension reduction matrix.
In a possible implementation manner, the uplink channel includes:
any one of an uplink physical shared channel PUSCH, an uplink physical control channel PUCCH and a physical random access channel PRACH.
In a second aspect, an embodiment of the present invention further provides a base station, including:
comprising a memory, a transceiver, and a processor:
a memory for storing a computer program; a transceiver for transceiving data under control of the processor; a processor for reading the computer program in the memory and performing the following operations:
Estimating a corresponding SRS channel matrix according to the SRS when the existence of a channel sounding reference signal SRS is determined;
according to the SRS channel matrix, performing eigenvalue decomposition on a conjugate symmetric matrix formed by an uplink channel matrix to obtain an eigenvector matrix with eigenvalues arranged in descending order, wherein the eigenvector matrix is a unitary matrix of R multiplied by R, and R is the antenna number of an uplink receiver;
taking the elements of the first M rows in the feature vector matrix to form a dimension reduction matrix; wherein, the M is determined according to the data stream number received by the uplink receiver and the eigenvalue corresponding to the eigenvector matrix;
and performing dimension reduction processing on the data received by the uplink receiver by using the dimension reduction matrix.
In one possible embodiment, the processor is further configured to:
determining the conjugate symmetry matrix based on the SRS channel matrix and a precoding matrix of an uplink channel;
and carrying out eigenvalue decomposition on the conjugate symmetric matrix by using an exponentiation method to obtain the eigenvector matrix.
In one possible embodiment, the processor is further configured to:
if the precoding matrix is a known codebook sent to the terminal by the base station, calculating the uplink channel matrix according to the SRS channel matrix and the precoding matrix;
Multiplying the uplink channel matrix with the corresponding transposed matrix to obtain a first matrix;
and weighting diagonal elements of the first matrix to obtain the conjugate symmetric matrix.
In one possible embodiment, the processor is further configured to:
if the precoding matrix is not transmitted by the base station and the precoding matrix is a unitary matrix, calculating the conjugate symmetric matrix according to the SRS channel matrix.
In one possible embodiment, the processor is further configured to:
multiplying the SRS channel matrix with the corresponding transposed matrix to obtain a second matrix;
and weighting diagonal elements of the second matrix to obtain the conjugate symmetric matrix.
In one possible implementation manner, the minimum value of M is the number of data streams, and the maximum value of M is a serial number corresponding to the maximum feature value selected from the feature values smaller than the set value.
In a possible implementation manner, when it is determined that the channel sounding reference signal SRS does not exist, the data received by the uplink receiver is subjected to dimension reduction processing by using a preset fixed dimension reduction matrix.
A possible implementation manner, the uplink channel includes:
any one of an uplink physical shared channel PUSCH, an uplink physical control channel PUCCH and a physical random access channel PRACH.
In a third aspect, an embodiment of the present invention provides a base station, including:
an estimation unit, configured to estimate a corresponding SRS channel matrix according to an SRS when it is determined that a channel sounding reference signal SRS exists;
the decomposition unit is used for decomposing the eigenvalues of the conjugate symmetric matrix formed by the uplink channel matrix according to the SRS channel matrix to obtain an eigenvector matrix with eigenvalues arranged in a descending order, wherein the eigenvector matrix is a unitary matrix of R multiplied by R, and R is the antenna number of the uplink receiver;
the dimension reduction unit is used for taking the elements of the first M rows in the characteristic vector matrix to form a dimension reduction matrix; wherein, the M is determined according to the data stream number received by the uplink receiver and the eigenvalue corresponding to the eigenvector matrix;
and the processing unit is used for performing dimension reduction processing on the data received by the uplink receiver by using the dimension reduction matrix.
In a possible embodiment, the decomposition unit is further configured to:
determining the conjugate symmetry matrix based on the SRS channel matrix and a precoding matrix of an uplink channel;
and carrying out eigenvalue decomposition on the conjugate symmetric matrix by using an exponentiation method to obtain the eigenvector matrix.
In a possible embodiment, the decomposition unit is further configured to:
if the precoding matrix is a known codebook sent to the terminal by the base station, calculating the uplink channel matrix according to the SRS channel matrix and the precoding matrix;
multiplying the uplink channel matrix with the corresponding transposed matrix to obtain a first matrix;
and weighting diagonal elements of the first matrix to obtain the conjugate symmetric matrix.
In a possible embodiment, the decomposition unit is further configured to:
if the precoding matrix is not transmitted by the base station and the precoding matrix is a unitary matrix, calculating the conjugate symmetric matrix according to the SRS channel matrix.
In a possible embodiment, the decomposition unit is further configured to:
multiplying the SRS channel matrix with the corresponding transposed matrix to obtain a second matrix;
and weighting diagonal elements of the second matrix to obtain the conjugate symmetric matrix.
In one possible implementation manner, the minimum value of M is the number of data streams, and the maximum value of M is a serial number corresponding to the maximum feature value selected from the feature values smaller than the set value.
In a possible implementation manner, when it is determined that the channel sounding reference signal SRS does not exist, the data received by the uplink receiver is subjected to dimension reduction processing by using a preset fixed dimension reduction matrix.
A possible implementation manner, the uplink channel includes:
any one of an uplink physical shared channel PUSCH, an uplink physical control channel PUCCH and a physical random access channel PRACH.
In a fourth aspect, embodiments of the present invention also provide a processor-readable storage medium storing a computer program for causing the processor to perform the method according to the first aspect.
Through the technical scheme in the one or more embodiments of the present invention, the embodiments of the present invention have at least the following technical effects:
in the embodiment provided by the invention, when the existence of the SRS is determined, estimating a corresponding SRS channel matrix according to the SRS; performing eigenvalue decomposition on a conjugate symmetric matrix formed by an uplink channel matrix to obtain an eigenvector matrix with eigenvalues arranged in descending order, wherein the eigenvector matrix is a unitary matrix of R multiplied by R, and R is the antenna number of an uplink receiver; taking the elements of the first M rows in the feature vector matrix to form a dimension reduction matrix; wherein M is determined according to the data stream number received by the uplink receiver and the characteristic value corresponding to the characteristic vector matrix; and performing dimension reduction processing on the data received by the uplink receiver by using the dimension reduction matrix. Because the eigenvalues corresponding to the eigenvector matrix are arranged in descending order, the eigenvectors in the eigenvector matrix are arranged from high to low according to the signal quality, therefore, the elements of the first M rows in the eigenvector matrix are taken to form a dimension reduction matrix, the M rows of eigenvectors with the best signal quality are obtained, the codebook in the dimension reduction matrix is matched with the signal environment corresponding to the actual signal, the signal transmission which is nearly the same as that of the original uplink channel matrix can be completed by using fewer branches, and the number of the elements in the dimension reduction matrix is less than that of the original uplink channel matrix, so that the operation amount required when the data received by the uplink receiver is processed is less, and the operation efficiency and the power consumption of the uplink receiver can be effectively improved.
Drawings
FIG. 1 is a flow chart of a process of an upstream receiver without dimension reduction;
fig. 2 is a process flow diagram of an upstream receiver using a fixed dimension-reduction matrix;
fig. 3 is a flowchart of an uplink receiver dimension reduction optimization method according to an embodiment of the present invention;
FIG. 4 is a graph showing a comparison of throughput curves provided by an embodiment of the present invention;
FIG. 5 is a graph showing a throughput curve versus a second graph provided by an embodiment of the present invention;
FIG. 6 is a graph comparing throughput curves provided by embodiments of the present invention;
FIG. 7 is a graph comparing throughput curves provided by embodiments of the present invention;
FIG. 8 is a graph comparing throughput curves provided by embodiments of the present invention;
FIG. 9 is a graph comparing throughput curves provided by embodiments of the present invention;
fig. 10 is a schematic structural diagram of a base station according to an embodiment of the present invention;
fig. 11 is a schematic structural diagram of a base station according to an embodiment of the present invention.
Detailed Description
In the embodiment of the invention, the term "and/or" describes the association relation of the association objects, which means that three relations can exist, for example, a and/or B can be expressed as follows: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.
The term "plurality" in the embodiments of the present application means two or more, and other adjectives are similar thereto.
The following description of the technical solutions in the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The embodiment of the application provides a method, a base station and a storage medium for dimension reduction optimization of an uplink receiver, which are used for solving the technical problems of low working efficiency and high power consumption of the uplink receiver in the prior art.
The technical scheme provided by the embodiment of the application can be suitable for various systems, in particular to a 5G system. For example, suitable systems may be global system for mobile communications (global system of mobile communication, GSM), code division multiple access (code division multiple access, CDMA), wideband code division multiple access (Wideband Code Division Multiple Access, WCDMA) universal packet Radio service (general packet Radio service, GPRS), long term evolution (long term evolution, LTE), LTE frequency division duplex (frequency division duplex, FDD), LTE time division duplex (time division duplex, TDD), long term evolution-advanced (long term evolution advanced, LTE-a), universal mobile system (universal mobile telecommunication system, UMTS), worldwide interoperability for microwave access (worldwide interoperability for microwave access, wiMAX), 5G New air interface (New Radio, NR), and the like. Terminal devices and network devices are included in these various systems. Core network parts such as evolved packet system (Evloved Packet System, EPS), 5G system (5 GS) etc. may also be included in the system.
The network device according to the embodiment of the present application may be a base station, where the base station may include a plurality of cells for providing services for a terminal. A base station may also be called an access point or may be a device in an access network that communicates over the air-interface, through one or more sectors, with wireless terminal devices, or other names, depending on the particular application. The network device may be operable to exchange received air frames with internet protocol (Internet Protocol, IP) packets as a router between the wireless terminal device and the rest of the access network, which may include an Internet Protocol (IP) communication network. The network device may also coordinate attribute management for the air interface. For example, the network device according to the embodiments of the present application may be a network device (Base Transceiver Station, BTS) in a global system for mobile communications (Global System for Mobile communications, GSM) or code division multiple access (Code Division Multiple Access, CDMA), a network device (NodeB) in a wideband code division multiple access (Wide-band Code Division Multiple Access, WCDMA), an evolved network device (evolutional Node B, eNB or e-NodeB) in a long term evolution (long term evolution, LTE) system, a 5G base station (gNB) in a 5G network architecture (next generation system), a home evolved base station (Home evolved Node B, heNB), a relay node (relay node), a home base station (femto), a pico base station (pico), and the like. In some network structures, the network device may include a Centralized Unit (CU) node and a Distributed Unit (DU) node, which may also be geographically separated.
Multiple-input Multiple-output (Multi Input Multi Output, MIMO) transmissions may each be made between a network device and a terminal device using one or more antennas, and the MIMO transmissions may be Single User MIMO (SU-MIMO) or Multiple User MIMO (MU-MIMO). The MIMO transmission may be 2D-MIMO, 3D-MIMO, FD-MIMO, or massive-MIMO, or may be diversity transmission, precoding transmission, beamforming transmission, or the like, depending on the form and number of antenna combinations.
Fig. 1 is a process flow diagram of an uplink receiver without dimension reduction.
Assuming a receiver in the base station, obtaining a received signal (denoted as Y) after performing fast fourier transform (fast Fourier transform, FFT) on the received signal, wherein the dimension of Y is r×1, where R is the number of receiving antennas; the channel estimation matrix obtained after channel estimation is carried out on R receiving antennas according to the receiving signals Y is H, and the dimension is R multiplied by V, wherein V is the data stream; the interference noise matrix is R uu The dimension is R x R.
At the time of MMSE MIMO equalization, according toFormula x= (H' ·r) of minimum mean square error (Minimum Mean Squareerror Estimation, MMSE) criterion uu ·H) -1 ·H’·R uu Y, MMSE MIMO equalization requires processing a large number of high-dimensional matrix operations when the number of receive antennas R is relatively large. After the MMSE MIMO equalization is completed and the operation output X is outputted, the X can be decoded.
Without dimension reduction, the corresponding matrix computation complexity such as channel estimation and MMSE MIMO equalization increases sharply with an increase in the number of receiving antennas R.
Fig. 2 is a process flow diagram of an uplink receiver using a fixed dimension-reduction matrix.
Fig. 2 is different from fig. 1 in that a fixed dimension-reducing matrix is added between the front-end FFT and the channel estimation, and a matrix F is designed in the "fixed matrix dimension-reducing" scheme, where the dimension is nxr, and N is the total dimension-reducing codebook number. The number of the large-scale antennas is R, assuming that the large-scale antennas have three dimensions of horizontal, vertical and polarization. Taking r=64 as an example, the dimension-reducing matrix F is fixed, and n=64 dimension-reducing codebooks can be obtained according to 8 equally-divided horizontal directions, 4 equally-divided vertical directions and 2 polarization directions.
The fixed dimension reduction matrix F is multiplied by a signal output by the front-end FFT to obtain Z=F.Y, the dimension is N multiplied by 1, and N is the dimension reduced data. Then, the power of the data after N paths of dimension reduction is calculated, and M paths with the strongest data power are selected as dimension reduction output results and marked as Y'. Thus, the matrix operation complexity corresponding to the channel estimation and MMSE MIMO equalization is reduced from R paths to M paths.
From the above processing procedure, it is known that the fixed dimension-reduction matrix is equivalent to selecting the M-way codebook with the strongest data power from the N-way codebook, and because the number of codebooks is limited, it is difficult to achieve very accurate matching with the actual channel environment.
In addition, the codebook is selected based on the strength of the data power, and the data power after the dimension reduction of the fixed matrix comprises signal power, interference power and noise power. This makes the data power strong not indicative of the signal power strong. Therefore, the signal environment corresponding to the selected M-way codebook and the actual signal may be mismatched.
In view of this, the embodiments of the present invention provide the following schemes:
referring to fig. 3, an embodiment of the present invention provides a method for dimension reduction optimization of an uplink receiver, and the processing procedure of the method is as follows.
Step 301: and when determining that the SRS exists, estimating a corresponding SRS channel matrix according to the SRS.
For example, taking the uplink channel as the PUSCH channel, when the ue is in a normal connection state, the ue transmits a channel sounding reference signal (Sounding Reference Signal, SRS) for channel quality sounding, and the base station estimates a corresponding SRS channel matrix (denoted as H) according to the SRS SRS )，H SRS Is R x P SRS Wherein P is SRS The number of ports of the SRS is the number of antennas.
After the SRS channel matrix is obtained, step 302 may be performed.
Step 302: and according to the SRS channel matrix, performing eigenvalue decomposition on a conjugate symmetric matrix formed by an uplink channel matrix to obtain an eigenvector matrix with eigenvalues arranged in descending order, wherein the eigenvector matrix is a unitary matrix of R multiplied by R, and R is the antenna number of an uplink receiver.
The feature vector matrix may be obtained by:
determining a conjugate symmetric matrix based on the SRS channel matrix and the precoding matrix of the uplink channel; and carrying out eigenvalue decomposition on the conjugate symmetric matrix by using an exponentiation method to obtain an eigenvector matrix.
Based on the SRS channel matrix and the precoding matrix of the uplink channel, determining the conjugate symmetry matrix may include two ways:
the first way is: if the precoding matrix is a known codebook sent to the terminal by the base station, calculating an uplink channel matrix according to the SRS channel matrix and the precoding matrix; multiplying the uplink channel matrix with the corresponding transposed matrix to obtain a first matrix; and weighting diagonal elements of the first matrix to obtain a conjugate symmetric matrix.
For example, the uplink channel is still taken as the PUSCH channel as an exampleThe precoding matrix is W PUSCH The base station generates and transmits the data stream on the PUSCH by the base station, the user terminal uses the precoding matrix to transmit the data stream on the PUSCH, and the base station SRS channel matrix (H SRS ) And a precoding matrix (W PUSCH ) The uplink channel matrix of the PUSCH can be calculated as H PUSCH ＝H SRS ·W PUSCH The uplink channel matrix and the corresponding transposed matrix (denoted as (H PUSCH ) ') to obtain a first matrix H PUSCH ·(H PUSCH )'. And weighting diagonal elements of the first matrix to obtain a conjugate symmetric matrix. For example, the diagonal elements of the first matrix are multiplied by 1.001, so that the values of the diagonal elements are slightly increased on the basis of the original values, the situation that the conjugate symmetric matrix is ill-conditioned can be prevented, the subsequent characteristic value decomposition of the conjugate symmetric matrix by the exponentiation method is ensured, and the characteristic vector matrix is obtained.
Let the conjugate symmetric matrix be H PUSCH ·(H PUSCH ) And (i.e. assuming that the matrix is a first matrix, performing feature decomposition on the weighted first matrix, and the like), performing feature decomposition on the first matrix to obtain U.delta.U, wherein U is a feature vector matrix and is a unitary matrix, delta is a diagonal matrix, diagonal elements of the diagonal matrix are feature values of U, the feature values on the diagonal are arranged in descending order, the dimensions of U and delta are R multiplied by R, and R is the number of antennas of an uplink receiver. Therefore, the eigenvectors in the eigenvector matrix are arranged from high to low according to the signal quality, and the part with the best signal quality is conveniently selected from the eigenvectors as the dimension-reducing matrix.
The second way is: if the precoding matrix is not transmitted by the base station and the precoding matrix is a unitary matrix, calculating a conjugate symmetric matrix according to the SRS channel matrix.
For example, taking the uplink channel as the PUSCH channel as an example, the precoding matrix is W PUSCH And is unitary, but the precoding matrix is calculated by the terminal and is not transmitted to the terminal by the base station, so the base station does not know what codebook the terminal uses, and can then be determined based on the SRS channel matrix (H SRS ) A conjugate symmetry matrix is calculated.
According to the SRS channel matrix, a conjugate symmetry matrix is calculated, which can be realized in the following way:
multiplying the SRS channel matrix with the corresponding transposed matrix to obtain a second matrix; and weighting diagonal elements of the second matrix to obtain the conjugate symmetric matrix.
For example, SRS channel matrix (H SRS ) The transposed matrix of (2) is (H SRS ) ' the second matrix is H SRS ·(H SRS ) And the diagonal elements of the second matrix are emphasized, for example, the diagonal elements of the second matrix are multiplied by 1.002, so that the values of the diagonal elements are slightly increased on the basis of the original values, the condition that the conjugate symmetric matrix is ill-conditioned can be prevented, the subsequent characteristic value decomposition of the conjugate symmetric matrix by a power method is ensured, and the characteristic vector matrix is obtained.
The decomposition of the eigenvectors for the conjugate symmetric matrix is similar to the first approach and is not described in detail herein.
After the feature vector matrix is obtained, steps 303 and 304 may be performed.
Step 303: taking the elements of the first M rows in the feature vector matrix to form a dimension reduction matrix; wherein M is determined according to the data stream number received by the uplink receiver and the eigenvalue corresponding to the eigenvector matrix.
Step 304: and performing dimension reduction processing on the data received by the uplink receiver by using the dimension reduction matrix.
Because the eigenvectors in the eigenvector matrix are arranged in descending order according to the corresponding eigenvalues, and M is determined according to the number of data streams received by the uplink receiver and the eigenvalues corresponding to the eigenvector matrix, the elements of the first M rows in the eigenvector matrix are taken to form a dimension reduction matrix, the M rows eigenvectors with the best signal quality are obtained, and the codebook in the dimension reduction matrix is matched with the signal environment corresponding to the actual signal, so that the codebook can complete the method with the original uplink channel matrix (H PUSCH ) Almost the same signal transmission, because the number of elements in the dimension-reduction matrix is less than that of the original uplink channel matrix, the operation amount required when processing the data received by the uplink receiver is less,therefore, the operation efficiency can be effectively improved, and the power consumption of the uplink receiver can be reduced.
In one possible implementation manner, the minimum value of M is a number of data streams, and the maximum value of M is a serial number corresponding to a maximum feature value selected from feature values smaller than the set value.
For example, if the number of data streams received by the uplink receiver is V, the minimum value of M is V.
For another example, the feature values corresponding to the feature vector matrix U are arranged in descending order to form a set { λ } 0 ，λ 1 ，…，λ M-1 Comparing the characteristic values with the set values, and selecting the maximum characteristic value (assumed as lambda) from the characteristic values smaller than the set values i I is lambda i Serial numbers in descending order), i is taken as the maximum value of M, and any value from V to i can be selected as M according to actual needs.
In order to enable the skilled person to fully understand the technical effect achieved by the scheme, two models commonly used in laboratory tests are used for comparing the throughput of the scheme using the application, and the throughput of dimension reduction without dimension reduction and using a fixed matrix.
Taking the 5G NR system, LOS (Line of Sight) channel+adaptive modulation+single stream as an example, please refer to fig. 4, which is a comparison chart of throughput curves provided in an embodiment of the present invention. When the scheme without dimension reduction is used, the number of branches is 64 branches, the number of branches reserved after the dimension reduction matrix is fixed is 16 branches, and the maximum reserved 3 branches after the scheme provided by the embodiment of the invention is used, obviously, the number of branches selected by the scheme provided by the implementation of the invention is far smaller than that of the prior art (i.e. the operation complexity is low), and the throughput performance is basically unchanged and even slightly better than that of the prior art.
The LOS channel refers to a channel when there is no shielding between two base stations or between a mobile phone and a base station.
Taking the 5G NR system, CDL-C channel+adaptive modulation+single stream as an example, please refer to fig. 5, which is a comparison chart of throughput curves provided in an embodiment of the present invention. When the scheme without dimension reduction is used, the number of branches is 64, the number of branches reserved after the dimension reduction matrix is fixed is 16, and after the scheme provided by the embodiment of the invention is used, the performance is poor under the condition of reserving 1 branch at maximum, but when 3 or 5 branches are reserved at maximum, the number of branches is far smaller than that of the prior art (i.e. the operation complexity is low), and the throughput performance is basically unchanged and even better than that of the prior art.
It should be noted that in laboratory tests, a new channel model needs to be applied to simulate the wireless environment in the existing network, such as the CDL (ClusteredDelay Line) model. The CDL model may be adapted to communication systems having frequencies ranging from 0.5GHz to 100GHz, with a maximum supported bandwidth of 2GHz, thus covering all bands and maximum bandwidths in 5G. The CDL model is further divided into five types, namely CDL-A, CDL-B, CDL-C, CDL-D, CDL-E, according to the simulated current network environment, wherein the first three types (CDL-A, CDL-B, CDL-C) are used for simulating three types of channels for non-line-of-sight transmission, and the second two types (CDL-D, CDL-E) are used for simulating channels for line-of-sight transmission.
Taking the 5G NR system, LOS channel+adaptive modulation+dual stream as an example, please refer to fig. 6, which is a comparison chart of throughput curves provided in an embodiment of the present invention. When the scheme without dimension reduction is used, the number of branches is 64 branches, the number of branches reserved after the dimension reduction matrix is fixed is 16 branches, and when 4 or 6 branches are reserved at maximum after the scheme provided by the embodiment of the invention is used, the reserved number of branches is far smaller than that of the prior art (i.e. the operation complexity is low), and the throughput performance is basically unchanged and even better than that of the prior art.
Taking the 5G NR system, CDL-C channel+adaptive modulation+dual stream as an example, please refer to fig. 7, which is a comparison chart of throughput curves provided in an embodiment of the present invention. When the scheme without dimension reduction is used, the number of branches is 64, the number of branches reserved after the dimension reduction matrix is fixed is 16, and after the scheme provided by the embodiment of the invention is used, the performance is poor under the condition of reserving 2 branches at maximum, but when 4 or 6 branches are reserved at maximum, the number of branches is far smaller than that of the prior art (i.e. the operation complexity is low), and the throughput performance is basically unchanged and even better than that of the prior art.
Taking a 5G NR system as an example, LOS channel+adaptive modulation+mu MIMO 2 single-stream users, please refer to fig. 8, which is a comparison chart of throughput curves provided by the embodiment of the present invention. When the scheme without dimension reduction is used, the number of branches is 64 branches, the number of branch flows reserved after the dimension reduction matrix is fixed is 16 branches, and when the scheme provided by the embodiment of the invention is used, 2 branches at maximum, 4 sub-branches at maximum and 6 branches at maximum are reserved, the number of branches reserved by the scheme provided by the embodiment of the invention is far smaller than that of the prior art (i.e. the operation complexity is low), and the throughput performance is basically unchanged and even slightly better than that of the prior art.
Taking a 5G NR system, CDL-C channel+adaptive modulation+mu MIMO 2 single-stream users as an example, please refer to fig. 9 for a comparison chart of throughput curves provided by an embodiment of the present invention. When the scheme without dimension reduction is used, the number of branches is 64 branches, the number of branches reserved after the dimension reduction matrix is fixed is 16 branches, and after the scheme provided by the embodiment of the invention is used, the reserved number of branches is far smaller than that of the prior art (i.e. the operation complexity is low), and the throughput performance is basically unchanged.
In fig. 4 to 9, throughput is expressed in Mbps and signal-to-noise ratio is expressed in dB.
In a possible implementation manner, when it is determined that the channel sounding reference signal SRS does not exist, the data received by the uplink receiver is subjected to dimension reduction processing by using a preset fixed dimension reduction matrix. For example, in the initial state, when the ue has not measured signal quality, the fixed dimension-reduction matrix may be used to perform dimension-reduction processing on the data received by the uplink receiver.
A possible implementation, an uplink channel, includes: any one of an uplink physical shared channel PUSCH, an uplink physical control channel PUCCH and a physical random access channel PRACH.
As shown in fig. 10, a base station provided in an embodiment of the present invention includes a memory 1001, a transceiver 1002, and a processor 1003:
A memory 1001 for storing a computer program; a transceiver 1002 for transceiving data under the control of the processor 1003; a processor 1003 for reading the computer program in the memory 1001 and performing the following operations:
estimating a corresponding SRS channel matrix according to the SRS when the existence of a channel sounding reference signal SRS is determined;
according to the SRS channel matrix, performing eigenvalue decomposition on a conjugate symmetric matrix formed by an uplink channel matrix to obtain an eigenvector matrix with eigenvalues arranged in descending order, wherein the eigenvector matrix is a unitary matrix of R multiplied by R, and R is the antenna number of an uplink receiver;
taking the elements of the first M rows in the feature vector matrix to form a dimension reduction matrix; wherein, the M is determined according to the data stream number received by the uplink receiver and the eigenvalue corresponding to the eigenvector matrix;
and performing dimension reduction processing on the data received by the uplink receiver by using the dimension reduction matrix.
In a possible implementation, the processor 1003 is further configured to:
determining the conjugate symmetry matrix based on the SRS channel matrix and a precoding matrix of an uplink channel;
and carrying out eigenvalue decomposition on the conjugate symmetric matrix by using an exponentiation method to obtain the eigenvector matrix.
In a possible implementation, the processor 1003 is further configured to:
if the precoding matrix is a known codebook sent to the terminal by the base station, calculating the uplink channel matrix according to the SRS channel matrix and the precoding matrix;
multiplying the uplink channel matrix with the corresponding transposed matrix to obtain a first matrix;
and weighting diagonal elements of the first matrix to obtain the conjugate symmetric matrix.
In a possible implementation, the processor 1003 is further configured to:
if the precoding matrix is not transmitted by the base station and the precoding matrix is a unitary matrix, calculating the conjugate symmetric matrix according to the SRS channel matrix.
In a possible implementation, the processor 1003 is further configured to:
multiplying the SRS channel matrix with the corresponding transposed matrix to obtain a second matrix;
and weighting diagonal elements of the second matrix to obtain the conjugate symmetric matrix.
In one possible implementation manner, the minimum value of M is the number of data streams, and the maximum value of M is a serial number corresponding to the maximum feature value selected from the feature values smaller than the set value.
In a possible implementation manner, when it is determined that the channel sounding reference signal SRS does not exist, the data received by the uplink receiver is subjected to dimension reduction processing by using a preset fixed dimension reduction matrix.
A possible implementation manner, the uplink channel includes:
any one of an uplink physical shared channel PUSCH, an uplink physical control channel PUCCH and a physical random access channel PRACH.
A transceiver 1002 for receiving and transmitting data under the control of the processor 1003.
Where in FIG. 10, a bus architecture may be comprised of any number of interconnected buses and bridges, one or more processors, represented in particular by processor 1003, and various circuits of the memory, represented by memory 1001. The bus architecture may also link together various other circuits such as peripheral devices, voltage regulators, power management circuits, etc., which are well known in the art and, therefore, will not be described further herein. The bus interface provides an interface. The transceiver 1002 may be a number of elements, i.e., including a transmitter and a receiver, providing a means for communicating with various other apparatus over a transmission medium, including wireless channels, wired channels, optical cables, etc. The processor 1003 is responsible for managing the bus architecture and general processing, and the memory 1001 may store data used by the processor 1003 in performing operations.
The processor 1003 may be a Central Processing Unit (CPU), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), field programmable gate array (Field-Programmable Gate Array, FPGA) or complex programmable logic device (Complex Programmable Logic Device, CPLD), and the processor may also employ a multi-core architecture.
It should be noted that, the above device provided in the embodiment of the present invention can implement all the method steps implemented in the method embodiment and achieve the same technical effects, and detailed descriptions of the same parts and beneficial effects as those in the method embodiment in this embodiment are omitted.
Based on the same inventive concept, in an embodiment of the present invention, a base station is provided, and a specific implementation of a method for optimizing an uplink receiver of the base station may refer to a description of an embodiment part of the method, and a repetition is omitted, and please refer to fig. 11, where the base station includes:
an estimation unit 1101, configured to estimate, when determining that a channel sounding reference signal SRS exists, a corresponding SRS channel matrix according to the SRS;
a decomposition unit 1102, configured to decompose a eigenvalue of a conjugate symmetric matrix formed by an uplink channel matrix according to the SRS channel matrix, to obtain an eigenvector matrix with eigenvalues arranged in a descending order, where the eigenvector matrix is a unitary matrix with r×r, and R is the number of antennas of the uplink receiver;
A dimension reduction unit 1103, configured to take the elements of the first M rows in the feature vector matrix to form a dimension reduction matrix; wherein, the M is determined according to the data stream number received by the uplink receiver and the eigenvalue corresponding to the eigenvector matrix;
and a processing unit 1104, configured to perform a dimension reduction process on the data received by the uplink receiver by using the dimension reduction matrix.
In a possible implementation manner, the decomposing unit 1102 is further configured to:
determining the conjugate symmetry matrix based on the SRS channel matrix and a precoding matrix of an uplink channel;
and carrying out eigenvalue decomposition on the conjugate symmetric matrix by using an exponentiation method to obtain the eigenvector matrix.
In a possible implementation manner, the decomposing unit 1102 is further configured to:
if the precoding matrix is a known codebook sent to the terminal by the base station, calculating the uplink channel matrix according to the SRS channel matrix and the precoding matrix;
multiplying the uplink channel matrix with the corresponding transposed matrix to obtain a first matrix;
and weighting diagonal elements of the first matrix to obtain the conjugate symmetric matrix.
In a possible implementation manner, the decomposing unit 1102 is further configured to:
If the precoding matrix is not transmitted by the base station and the precoding matrix is a unitary matrix, calculating the conjugate symmetric matrix according to the SRS channel matrix.
In a possible implementation manner, the decomposing unit 1102 is further configured to:
multiplying the SRS channel matrix with the corresponding transposed matrix to obtain a second matrix;
and weighting diagonal elements of the second matrix to obtain the conjugate symmetric matrix.
In one possible implementation manner, the minimum value of M is the number of data streams, and the maximum value of M is a serial number corresponding to the maximum feature value selected from the feature values smaller than the set value.
In a possible implementation manner, when it is determined that the channel sounding reference signal SRS does not exist, the data received by the uplink receiver is subjected to dimension reduction processing by using a preset fixed dimension reduction matrix.
A possible implementation manner, the uplink channel includes:
any one of an uplink physical shared channel PUSCH, an uplink physical control channel PUCCH and a physical random access channel PRACH.
It should be noted that, in the embodiment of the present application, the division of the units is schematic, which is merely a logic function division, and other division manners may be implemented in actual practice. In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a processor-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution, in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
It should be noted that, the above device provided in the embodiment of the present invention can implement all the method steps implemented in the method embodiment and achieve the same technical effects, and detailed descriptions of the same parts and beneficial effects as those in the method embodiment in this embodiment are omitted.
Based on the same inventive concept, the embodiments of the present invention also provide a processor readable storage medium storing a computer program for causing the processor to execute the method as described in the above-mentioned dimensionality reduction optimization of the upstream receiver.
The processor-readable storage medium may be any available medium or data storage device that can be accessed by a processor, including, but not limited to, magnetic storage (e.g., floppy disks, hard disks, magnetic tape, magneto-optical disks (MOs), etc.), optical storage (e.g., CD, DVD, BD, HVD, etc.), semiconductor storage (e.g., ROM, EPROM, EEPROM, nonvolatile storage (NAND FLASH), solid State Disk (SSD)), and the like.
The method and the device are based on the same application, and because the principles of solving the problems by the method and the device are similar, the implementation of the device and the method can be referred to each other, and the repetition is not repeated.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-executable instructions. These computer-executable instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These processor-executable instructions may also be stored in a processor-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the processor-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These processor-executable instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (16)
1. A method for dimension reduction optimization of an uplink receiver, the method comprising:
estimating a corresponding SRS channel matrix according to the SRS when the existence of a channel sounding reference signal SRS is determined;
according to the SRS channel matrix, performing eigenvalue decomposition on a conjugate symmetric matrix formed by an uplink channel matrix to obtain an eigenvector matrix with eigenvalues arranged in descending order, wherein the eigenvector matrix is a unitary matrix of R multiplied by R, and R is the antenna number of an uplink receiver;
Taking the elements of the first M rows in the feature vector matrix to form a dimension reduction matrix; wherein, the M is determined according to the data stream number received by the uplink receiver and the eigenvalue corresponding to the eigenvector matrix;
and performing dimension reduction processing on the data received by the uplink receiver by using the dimension reduction matrix.
2. The method of claim 1, wherein performing eigenvalue decomposition on a conjugate symmetric matrix composed of an uplink channel matrix according to the SRS channel matrix to obtain an eigenvector matrix with eigenvalues arranged in descending order, comprises:
determining the conjugate symmetry matrix based on the SRS channel matrix and a precoding matrix of an uplink channel;
and carrying out eigenvalue decomposition on the conjugate symmetric matrix by using an exponentiation method to obtain the eigenvector matrix.
3. The method of claim 2, wherein determining the conjugate symmetry matrix based on the SRS channel matrix and a precoding matrix for an uplink channel comprises:
if the precoding matrix is a known codebook sent to the terminal by the base station, calculating the uplink channel matrix according to the SRS channel matrix and the precoding matrix;
multiplying the uplink channel matrix with the corresponding transposed matrix to obtain a first matrix;
And weighting diagonal elements of the first matrix to obtain the conjugate symmetric matrix.
4. The method of claim 2, wherein determining the conjugate symmetry matrix based on the SRS channel matrix and a precoding matrix for an uplink channel comprises:
if the precoding matrix is not transmitted by the base station and the precoding matrix is a unitary matrix, calculating the conjugate symmetric matrix according to the SRS channel matrix.
5. The method of claim 4, wherein calculating the conjugate symmetry matrix from the SRS channel matrix comprises:
multiplying the SRS channel matrix with the corresponding transposed matrix to obtain a second matrix;
and weighting diagonal elements of the second matrix to obtain the conjugate symmetric matrix.
6. The method according to any one of claims 1 to 5, wherein the minimum value of M is the number of data streams, and the maximum value of M is a serial number corresponding to a maximum feature value selected from feature values smaller than a set value.
7. The method according to any one of claims 1-5, wherein when it is determined that there is no channel sounding reference signal SRS, performing a dimension reduction process on data received by the uplink receiver with a preset fixed dimension reduction matrix.
8. The method according to any of claims 2-5, wherein the uplink channel comprises:
any one of an uplink physical shared channel PUSCH, an uplink physical control channel PUCCH and a physical random access channel PRACH.
9. A base station comprising a memory, a transceiver, and a processor:
a memory for storing a computer program; a transceiver for transceiving data under control of the processor; a processor for reading the computer program in the memory and performing the following operations:
estimating a corresponding SRS channel matrix according to the SRS when the existence of a channel sounding reference signal SRS is determined;
according to the SRS channel matrix, performing eigenvalue decomposition on a conjugate symmetric matrix formed by an uplink channel matrix to obtain an eigenvector matrix with eigenvalues arranged in descending order, wherein the eigenvector matrix is a unitary matrix of R multiplied by R, and R is the antenna number of an uplink receiver;
taking the elements of the first M rows in the feature vector matrix to form a dimension reduction matrix; wherein, the M is determined according to the data stream number received by the uplink receiver and the eigenvalue corresponding to the eigenvector matrix;
and performing dimension reduction processing on the data received by the uplink receiver by using the dimension reduction matrix.
10. The base station of claim 9, wherein the processor is further configured to:
determining the conjugate symmetry matrix based on the SRS channel matrix and a precoding matrix of an uplink channel;
and carrying out eigenvalue decomposition on the conjugate symmetric matrix by using an exponentiation method to obtain the eigenvector matrix.
11. The base station of claim 10, wherein the processor is further configured to:
if the precoding matrix is a known codebook sent to the terminal by the base station, calculating the uplink channel matrix according to the SRS channel matrix and the precoding matrix;
multiplying the uplink channel matrix with the corresponding transposed matrix to obtain a first matrix;
and weighting diagonal elements of the first matrix to obtain the conjugate symmetric matrix.
12. The base station of claim 10, wherein the processor is further configured to:
if the precoding matrix is not transmitted by the base station and the precoding matrix is a unitary matrix, calculating the conjugate symmetric matrix according to the SRS channel matrix.
13. The base station of claim 12, wherein the processor is further configured to:
multiplying the SRS channel matrix with the corresponding transposed matrix to obtain a second matrix;
And weighting diagonal elements of the second matrix to obtain the conjugate symmetric matrix.
14. The base station according to any of claims 9-13, wherein the minimum value of M is the number of data streams, and the maximum value of M is a serial number corresponding to a maximum eigenvalue selected from eigenvalues smaller than a set value.
15. A base station, comprising:
an estimation unit, configured to estimate a corresponding SRS channel matrix according to an SRS when it is determined that a channel sounding reference signal SRS exists;
the decomposition unit is used for decomposing the eigenvalues of the conjugate symmetric matrix formed by the uplink channel matrix according to the SRS channel matrix to obtain an eigenvector matrix with eigenvalues arranged in a descending order, wherein the eigenvector matrix is a unitary matrix of R multiplied by R, and R is the antenna number of the uplink receiver;
the dimension reduction unit is used for taking the elements of the first M rows in the characteristic vector matrix to form a dimension reduction matrix; wherein, the M is determined according to the data stream number received by the uplink receiver and the eigenvalue corresponding to the eigenvector matrix;
and the processing unit is used for performing dimension reduction processing on the data received by the uplink receiver by using the dimension reduction matrix.
16. A processor-readable storage medium, characterized in that the processor-readable storage medium stores a computer program for causing the processor to perform the method of any one of claims 1 to 8.
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