CN113328770A - Large-scale MIMO channel state estimation method and device - Google Patents

Large-scale MIMO channel state estimation method and device Download PDF

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CN113328770A
CN113328770A CN202110616813.3A CN202110616813A CN113328770A CN 113328770 A CN113328770 A CN 113328770A CN 202110616813 A CN202110616813 A CN 202110616813A CN 113328770 A CN113328770 A CN 113328770A
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channel state
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罗志勇
朱贝贝
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Sun Yat Sen University
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    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • 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
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Abstract

The invention discloses a large-scale MIMO channel state estimation method and a device, wherein the method comprises the following steps: determining sparsity and low rank of millimeter wave channel transmission according to millimeter wave transmission in a large-scale MIMO system; under a matrix complete model, determining sparsity by adopting a beam domain space representation matrix, and determining low rank by adopting a matrix kernel norm; constructing an objective function according to sparsity and low rank of a millimeter wave channel, an unknown sparse channel gain matrix and an unknown channel state matrix; and optimizing and solving the objective function to determine the state information of the millimeter wave channel, wherein iterative updating variable solving is carried out by adopting an alternative direction multiplier method, and a random singular value decomposition method is introduced to solve a channel state matrix in the alternative direction multiplier method. By means of the low-rank characteristic and the sparse characteristic of the millimeter wave channel, a random singular value decomposition method of the matrix is introduced in the process of iteratively updating the variable, and the efficiency of obtaining channel state information is improved.

Description

Large-scale MIMO channel state estimation method and device
Technical Field
The present invention relates to the field of wireless communication technologies, and in particular, to a method and an apparatus for estimating a large-scale MIMO channel state.
Background
The development of mobile communication technology has promoted the historical progress of human, but the development of communication technology is restricted by the complicated and changeable wireless Channel of mobile communication, and the acquisition of accurate Channel State Information (CSI) is crucial to the acquisition of diversity gain and the utilization of Channel fading. The large-scale multi-input multi-output technology is a key technology in a 5G communication system, the number of antennas in the technology can reach 64 or more than 128, compared with the number of antennas with the number not exceeding 4 of a 4G system terminal, the number is increased by one order of magnitude, the data transmission rate is improved, meanwhile, the calculation complexity is high, so that more antenna units need higher signal frequency than a microwave frequency band, therefore, millimeter waves with frequency bands distributed at 30-300 GHz can meet the requirements of people, researchers combine the millimeter waves with the large-scale multi-input multi-output technology, and the millimeter wave large-scale MIMO technology is provided.
The millimeter wave has the characteristics of large bandwidth, high frequency and short wavelength, so that the scattering of the millimeter wave is weak when the millimeter wave is transmitted in the air, and the characteristic of cluster transmission is presented, so that a millimeter wave channel looks sparse, except the sparse characteristic, the millimeter wave channel can show angular expansion in the arrival angle, the departure angle and the elevation angle domain, the angular expansion can generate a useful low-rank structure, but the millimeter wave generates very large loss when the millimeter wave is transmitted in space, is easy to be absorbed by the atmosphere and is seriously attenuated by rainfall.
In order to compensate path loss of millimeter waves, a millimeter wave communication system realizes a high-directivity antenna through a super-large-scale antenna array and a beam forming technology to obtain gain and improve transmission quality, the beam forming technology is a design that a user carries out precoding according to information such as the direction of a transmitting end or a receiving end, in a millimeter wave large-scale MIMO system, researchers usually design a mixed precoding matrix according to the obtained CSI, and can also carry out fading matching on a transmitting signal by utilizing the CSI so as to achieve efficient transmission, meanwhile, the transmitting end obtains accurate and effective channel state information and is beneficial to assisting in realizing mixed beam forming, but in a large-scale antenna array scene, the environment with low signal-to-noise ratio is usually adopted before beam forming, so that the accurate and effective channel state information is more difficult to obtain.
Disclosure of Invention
The invention aims to provide a large-scale MIMO channel state estimation method to solve the problem that channel state information is difficult to estimate.
In order to achieve the above object, the present invention provides a large-scale MIMO channel state estimation method, including:
determining sparsity and low rank of millimeter wave channel transmission according to millimeter wave transmission in a large-scale MIMO system; wherein, under a matrix-complete model,
determining the sparsity by adopting a preset beam domain space representation matrix, and determining the low rank by adopting a preset matrix kernel norm;
constructing a target function according to the sparsity and low rank of the millimeter wave channel, a preset unknown sparse channel gain matrix and a preset unknown channel state recovery matrix;
and optimizing and solving the objective function to determine the state information of the millimeter wave channel, wherein the optimizing and solving comprises iteratively updating variables by adopting an alternative direction multiplier method, and solving a channel state matrix in the alternative direction multiplier method by introducing a random singular value decomposition method.
Preferably, the first and second electrodes are formed of a metal,
obtaining a beam domain space representation matrix S according to the beam domain space decomposition of the low rank matrix H, as follows:
Figure BDA0003097717530000021
wherein D isRAnd DTEach represents a discrete fourier matrix that is,
Figure BDA0003097717530000022
representing the discrete Fourier matrix DTThe conjugate matrix of (2).
Preferably, the objective function is as follows:
minH,S τH||H||*+τS||S||1；
s.t ΩoH＝HΩand
Figure BDA0003097717530000023
wherein | H | Y*Represents the nuclear norm of the low-rank matrix H, | S | | luminance1Representing the 1 norm of the beam domain space representation matrix S, omega representing the sampling matrix, o representing the Hadamard product operation, HΩRepresenting the result of sampling the channel matrix, τHAnd τSEach representing a weighting factor related to the number of millimeter-wave channel paths.
Preferably, the objective function further comprises:
introducing two auxiliary variables to eliminate the noise of the millimeter wave channel, wherein the two auxiliary variables are as follows:
Figure BDA0003097717530000024
Figure BDA0003097717530000025
updating the objective function as follows:
Figure BDA0003097717530000026
s.t.H＝Y and
Figure BDA0003097717530000027
wherein,
Figure BDA0003097717530000028
representing the square of the F-norm of the computation matrix C.
The present invention also provides a large-scale MIMO channel state estimation apparatus, comprising:
the acquisition module is used for determining the sparsity and low rank of millimeter wave channel transmission according to the transmission of millimeter waves in the large-scale MIMO system; wherein, under a matrix-complete model,
determining the sparsity by adopting a preset beam domain space representation matrix, and determining the low rank by adopting a preset matrix kernel norm;
the constructing module is used for constructing a target function according to the sparsity and the low rank of the millimeter wave channel, a preset unknown sparse channel gain matrix and a preset unknown channel state recovery matrix;
and the optimization module is used for optimizing and solving the objective function to determine the state information of the millimeter wave channel, wherein the optimization and solution comprises the steps of iteratively updating variables by adopting an alternative direction multiplier method and solving a channel state matrix in the alternative direction multiplier method by introducing a random singular value decomposition method.
Preferably, the acquisition module is configured to acquire, from the user,
and is further configured to obtain the beam domain spatial representation matrix S according to a beam domain spatial decomposition of the low rank matrix H, as follows:
Figure BDA0003097717530000031
wherein D isRAnd DTEach represents a discrete fourier matrix that is,
Figure BDA0003097717530000032
representing the discrete Fourier matrix DTThe conjugate matrix of (2).
Preferably, the constructing module is further configured to construct the objective function as follows:
minH,S τH||H||*+τS||S||1；
s.t ΩoH＝HΩ and
Figure BDA0003097717530000033
wherein | H | Y*Represents the nuclear norm of the low-rank matrix H, | S | | luminance1Representing the beam domain spatial representation1 norm of matrix S, Ω denotes sampling matrix, o denotes Hadamard product operation, HΩRepresenting the result of sampling the channel matrix, τHAnd τSEach representing a weighting factor related to the number of millimeter-wave channel paths.
Preferably, the constructing module is further configured to construct the objective function as follows:
introducing two auxiliary variables to eliminate the noise of the millimeter wave channel, wherein the two auxiliary variables are as follows:
Figure BDA0003097717530000034
Figure BDA0003097717530000035
updating the objective function as follows:
Figure BDA0003097717530000036
s.t.H＝Y and
Figure BDA0003097717530000037
wherein,
Figure BDA0003097717530000038
representing the square of the F-norm of the computation matrix C.
The invention also provides a computer terminal device comprising one or more processors and a memory. A memory coupled to the processor for storing one or more programs; when executed by the one or more processors, cause the one or more processors to implement a massive MIMO channel state estimation method as in any one of the above embodiments.
The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the massive MIMO channel state estimation method according to any of the embodiments described above.
In the background of a complete matrix model, the method for introducing the random singular value decomposition of the matrix in the process of iteratively updating the variable improves the efficiency of acquiring the state information of the channel by the low-rank characteristic and the sparse characteristic of the millimeter wave channel.
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In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart of a massive MIMO channel state estimation method according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating normalized mean square error as a function of signal-to-noise ratio according to another embodiment of the present invention;
FIG. 3 is a schematic diagram of achievable spectral efficiency as a function of signal-to-noise ratio according to yet another embodiment of the present invention;
FIG. 4 is a diagram illustrating normalized mean square error as a function of iteration count for an algorithm according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a massive MIMO channel state estimation apparatus according to an embodiment of the present invention.
Detailed Description
The technical solutions in the present invention will be described clearly and completely with reference to the accompanying drawings, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be understood that the step numbers used herein are for convenience of description only and are not used as limitations on the order in which the steps are performed.
It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.
Referring to fig. 1, the present invention provides a large-scale MIMO channel state estimation method, which includes:
s101, determining sparsity and low-rank property of millimeter wave channel transmission according to millimeter wave transmission in a large-scale MIMO system; and under a complete matrix model, determining the sparsity by adopting a preset beam domain space representation matrix, and determining the low rank by adopting a preset matrix kernel norm.
Specifically, under a complete matrix model, channel estimation can be modeled as a recovery problem of a low-rank matrix, and meanwhile, beam space representation of the channel matrix can be introduced to be used as auxiliary information for joint recovery. Single-user multi-antenna time division duplex uplink scene, analog beam forming structure of sampling switch mode of base station end and user end, and user end configuration NTRoot antenna, base station side equipped with NRThe method comprises the following steps of root-fire antennas, wherein the antenna arrangement mode is a uniform linear array, the antenna spacing is d, the antenna departure angle is phi, the antenna arrival angle is theta, the transmitted training sequence is unit power, a high-frequency millimeter wave channel is adopted, and a classical S-V channel model is used for modeling the millimeter wave channel, and the method comprises the following steps:
Figure BDA0003097717530000051
wherein alpha islDenotes the complex gain of the l-th path, aR(θl) And aT(φl) Array response vectors for the ue and the bs respectively,
Figure BDA0003097717530000052
representing a vector of response to an array aT(φl) And (4) making a conjugation device.
The problem of recovery of the low-rank matrix H under the complete matrix model can be modeled as follows:
min||H||*
s.t.ρΩ(H)＝ρΩ(D)
wherein the nuclear norm
Figure BDA0003097717530000053
Representing the sum of the singular values of the matrix, Ω being the set of observation samples, ρΩ():CM×N→CM×NRepresents a projection matrix, defined as:
Figure BDA0003097717530000054
the beam domain spatial decomposition of the low rank matrix H is as follows:
Figure BDA0003097717530000055
wherein D isRAnd DTEach represents a discrete fourier matrix that is,
Figure BDA0003097717530000056
representing the discrete Fourier matrix DTThe beam space representation matrix S may reflect the sparse characteristics of the millimeter wave channel.
Under the background of a complete matrix model, modeling low-rank characteristics generated by millimeter wave channel angle expansion into a recovery problem of a low-rank matrix, and establishing a constrained target function by using a matrix kernel norm as optimal convex approximation of a rank function.
S102, constructing a target function according to the sparsity and low rank of the millimeter wave channel, a preset unknown sparse channel gain matrix and a preset unknown channel state recovery matrix.
In the design of the millimeter wave channel estimation algorithm, because the millimeter wave channel has the sparse characteristic, the millimeter wave channel matrix is expressed by the beam domain space as follows:
Figure BDA0003097717530000057
wherein D isRAnd DTEach represents a discrete fourier matrix that is,
Figure BDA0003097717530000061
representing the discrete Fourier matrix DTThe beam space representation matrix S can reflect the sparse characteristic of a millimeter wave channel and only contains a small number of high channel gains, because the millimeter wave channel angular expansion can represent a useful low-rank structure, the recovery problem of the low-rank matrix can be established under a matrix complete model, the beam domain space representation of the matrix is used as auxiliary information of the recovery problem of the low-rank matrix, an unknown channel state matrix H and the beam space representation thereof are jointly recovered through the unknown sparse channel gain matrix S, weighting factors related to the number of millimeter wave channel paths are introduced, and a target convex optimization problem is formulated as follows:
minH,S τH||H||*+τS||S||1；
s.t ΩoH＝HΩand
Figure BDA0003097717530000062
wherein | H | Y*Represents the nuclear norm of the low-rank matrix H, | S | | luminance1Representing the 1 norm of the beam domain space representation matrix S, omega representing the sampling matrix, o representing the Hadamard product operation, HΩRepresenting the result of sampling the channel matrix, τHAnd τSEach representing a weighting factor related to the number of millimeter-wave channel paths.
The convex optimization problem is a dual-target convex optimization problem, so that the convex optimization problem has a global optimal solution, the solution is carried out by adopting an alternating direction multiplier method, and two auxiliary variables are introduced to eliminate the noise of a millimeter wave channel, wherein the two auxiliary variables are as follows:
Figure BDA0003097717530000063
Figure BDA0003097717530000064
meanwhile, in order to make the target problem separable in variables, the target problem is updated as follows:
Figure BDA0003097717530000065
s.t.H＝Y and
Figure BDA0003097717530000066
wherein,
Figure BDA0003097717530000067
representing the square of the F-norm of the computation matrix C.
S103, optimizing and solving the objective function to determine state information of the millimeter wave channel, wherein the optimizing and solving include iteratively updating variables by adopting an alternative direction multiplier method, and solving a channel state matrix in the alternative direction multiplier method by introducing a random singular value decomposition method.
Specifically, the target problem in step S103 is converted into an augmented lagrange function as follows:
Figure BDA0003097717530000068
wherein,
Figure BDA0003097717530000069
the lagrange multiplier is represented by a number of lagrange multipliers,
Figure BDA00030977175300000610
representation matrix Z1The conjugate transpose of (a) is performed,
Figure BDA00030977175300000611
representation matrix Z2Tr (·) represents the tracing of the matrix, and ρ represents the step size of the lagrange multiplier method.
Splitting an augmented Lagrange function of a target problem into a plurality of independent subproblems according to an alternating direction multiplier method, and solving the problems when the L-th is 0,1, 2.
Figure BDA0003097717530000071
Figure BDA0003097717530000072
Figure BDA0003097717530000073
Figure BDA0003097717530000074
Figure BDA0003097717530000075
Figure BDA0003097717530000076
For the variables H, H(L+1)＝Udiag({sign(ξi)max(ξi,0)}1≤i≤r)VHWherein a matrix of random singular value decompositions is used
Figure BDA0003097717530000077
Obtaining approximate i truncated singular vector matrixes U and V and the first i larger singular values xiiThe specific calculation flow is as follows:
1) a random matrix omega is generated.
2) And taking the matrix to be decomposed as A, projecting the A onto a random matrix omega, calculating a subspace A omega of the matrix A, and generating an orthogonal basis of the subspace.
3) The iterative update uses the orthogonal bases to generate a new subspace of matrix a and a new orthogonal base, and adds together the subspaces of each iteration into a larger subspace.
4) The iteration is completed, and the orthogonal base Q of the maximum subspace is calculated
5) Let final subspace B be QTA, then calculating the singular vector matrix and singular values of the matrix B. For the variables Y, Y(L+1)＝unvec(y(L+1)),
Figure BDA0003097717530000078
"vec.)" means that the columns of a matrix are connected as a vector, and "unwec.)" means the inverse operation thereof.
For the variable S, S(L+1)＝unvec(s(L+1))，
s(L+1)＝sign(Re(υ(L+1)))°max(|Re(υ(L+1))|-τ′s,0)+sign(Im(υ(L+1)))°max(|Im(υ(L+1))|-τ′s0), "Im (. -)" means taking the imaginary part.
With respect to the variable C, it is preferred that,
Figure BDA0003097717530000079
for the lagrange multiplier Z1，Z2，
Figure BDA00030977175300000710
Figure BDA00030977175300000711
Final output
Figure BDA00030977175300000712
According to experimental simulation verification, an environment is configured, the number of antennas is set to be 64 multiplied by 64, the number of channel clusters is set to be 2, the number of paths in each cluster is set to be 1, and path gain a is set to beilThe array response vector is generated as a uniform linear array, subject to a rayleigh distribution with a mean of 0 and a variance of 1.
Normalized mean square error:
Figure BDA0003097717530000081
"ε.)" means the mean value obtained by averaging the results of multiple Monte Carlo iterations in an experiment.
Spectral efficiency:
Figure BDA0003097717530000082
"det.)" represents a determinant of the matrix.
Referring to fig. 2, fig. 3 and fig. 4, compared to other methods for acquiring channel information, the method of the present invention can estimate the state information of the channel more accurately and obtain good spectrum efficiency.
The invention adopts the sparse characteristic of a millimeter wave channel and a low-rank structure generated by angular expansion, establishes a dual-target convex problem represented by an unknown channel state matrix H and a beam space thereof through joint recovery of an unknown sparse channel gain matrix S under the background of a complete matrix model, obtains a global optimal solution by using an alternative direction multiplier method, and uses random singular value decomposition in the process of solving the sub-problem. The invention adopts a random singular value decomposition method to solve variables in the subproblems, avoids directly decomposing a large matrix by solving a subspace with lower dimensionality, effectively reduces the complexity in a large-scale multi-antenna scene, and improves the efficiency of obtaining channel state information by jointly utilizing the low-rank characteristic and the sparse characteristic of a millimeter wave channel and combining a matrix complete model.
Referring to fig. 4, the present invention provides a massive MIMO channel state estimation apparatus, including:
the acquisition module 11 is configured to determine sparsity and low rank of millimeter wave channel transmission according to transmission of millimeter waves in the massive MIMO system; wherein, under a matrix-complete model,
and determining the sparsity by adopting a preset beam domain space representation matrix, and determining the low rank by adopting a preset matrix kernel norm.
And the constructing module 12 is configured to construct an objective function according to the sparsity and low rank of the millimeter wave channel, a preset unknown sparse channel gain matrix, and a preset unknown channel state recovery matrix.
And the optimization module 13 is configured to optimize and solve the objective function to determine the state information of the millimeter wave channel, where the optimizing and solving includes iteratively updating variables by using an alternative direction multiplier method, and solving a channel state matrix in the alternative direction multiplier method by introducing a random singular value decomposition method.
For specific limitations of the massive MIMO channel state estimation apparatus, reference may be made to the above limitations, which are not described herein again. The modules in the massive MIMO channel state estimation apparatus may be wholly or partially implemented by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
The invention provides a computer terminal device comprising one or more processors and a memory. A memory is coupled to the processor for storing one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the massive MIMO channel state estimation method as in any one of the embodiments above.
The processor is used for controlling the overall operation of the computer terminal equipment so as to complete all or part of the steps of the massive MIMO channel state estimation method. The memory is used to store various types of data to support the operation at the computer terminal device, which data may include, for example, instructions for any application or method operating on the computer terminal device, as well as application-related data. The Memory may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.
In an exemplary embodiment, the computer terminal Device may be implemented by one or more Application Specific 1 integrated circuits (AS 1C), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components, for performing the above-mentioned large-scale MIMO channel state estimation method, and achieving technical effects consistent with the above-mentioned methods.
In another exemplary embodiment, a computer readable storage medium comprising program instructions which, when executed by a processor, implement the steps of the massive MIMO channel state estimation method in any one of the above embodiments is also provided. For example, the computer-readable storage medium may be the above-mentioned memory including program instructions executable by a processor of a computer terminal device to perform the above-mentioned massive MIMO channel state estimation method and achieve the technical effects consistent with the above-mentioned method.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.

Claims (10)

1. A massive MIMO channel state estimation method is characterized by comprising the following steps:
determining sparsity and low rank of millimeter wave channel transmission according to millimeter wave transmission in a large-scale MIMO system; wherein, under a matrix-complete model,
determining the sparsity by adopting a preset beam domain space representation matrix, and determining the low rank by adopting a preset matrix kernel norm;
constructing a target function according to the sparsity and low rank of the millimeter wave channel, a preset unknown sparse channel gain matrix and a preset unknown channel state recovery matrix;
and optimizing and solving the objective function to determine the state information of the millimeter wave channel, wherein the optimizing and solving comprises iteratively updating variables by adopting an alternative direction multiplier method, and solving a channel state matrix in the alternative direction multiplier method by introducing a random singular value decomposition method.
2. The massive MIMO channel state estimation method of claim 1, wherein the beam domain spatial representation matrix S is obtained according to a beam domain spatial decomposition of a low rank matrix H, as follows:
Figure FDA0003097717520000014
wherein D isRAnd DTEach represents a discrete fourier matrix that is,
Figure FDA0003097717520000015
representing the discrete Fourier matrix DTThe conjugate matrix of (2).
3. The massive MIMO channel state estimation method of claim 2, wherein the objective function is as follows:
minH,S τH||H||*+τS||S||1；
s.tΩoH＝HΩand
Figure FDA0003097717520000011
wherein | H | Y*Represents the nuclear norm of the low-rank matrix H, | S | | luminance1Representing the 1 norm of the beam domain space representation matrix S, omega representing the sampling matrix, o representing the Hadamard product operation, HΩRepresenting the result of sampling the channel matrix, τHAnd τSEach representing a weighting factor related to the number of millimeter-wave channel paths.
4. The massive MIMO channel state estimation method of claim 3, wherein the objective function further comprises:
introducing two auxiliary variables to eliminate the noise of the millimeter wave channel, wherein the two auxiliary variables are as follows:
Figure FDA0003097717520000012
Figure FDA0003097717520000013
updating the objective function as follows:
Figure FDA0003097717520000021
s.t.H＝Y and
Figure FDA0003097717520000022
wherein,
Figure FDA0003097717520000023
representing the square of the F-norm of the computation matrix C.
5. A massive MIMO channel state estimation apparatus, comprising:
the acquisition module is used for determining the sparsity and low rank of millimeter wave channel transmission according to the transmission of millimeter waves in the large-scale MIMO system; wherein, under a matrix-complete model,
determining the sparsity by adopting a preset beam domain space representation matrix, and determining the low rank by adopting a preset matrix kernel norm;
the constructing module is used for constructing a target function according to the sparsity and the low rank of the millimeter wave channel, a preset unknown sparse channel gain matrix and a preset unknown channel state recovery matrix;
and the optimization module is used for optimizing and solving the objective function to determine the state information of the millimeter wave channel, wherein the optimization and solution comprises the steps of iteratively updating variables by adopting an alternative direction multiplier method and solving a channel state matrix in the alternative direction multiplier method by introducing a random singular value decomposition method.
6. The massive MIMO channel state estimation apparatus of claim 5, wherein the obtaining module is further configured to obtain the beam-domain spatial representation matrix S according to a beam-domain spatial decomposition of a low rank matrix H, as follows:
Figure FDA0003097717520000024
wherein D isRAnd DTEach represents a discrete fourier matrix that is,
Figure FDA0003097717520000025
representing the discrete Fourier matrix DTThe conjugate matrix of (2).
7. The massive MIMO channel state estimation apparatus of claim 6, wherein the constructing module is further configured to construct the objective function as follows:
minH,SτH||H||*+τS||S||1；
s.tΩoH＝HΩand
Figure FDA0003097717520000026
wherein | H | Y*Represents the nuclear norm of the low-rank matrix H, | S | | luminance1Representing the 1 norm of the beam domain space representation matrix S, omega representing the sampling matrix, o representing the Hadamard product operation, HΩRepresenting the result of sampling the channel matrix, τHAnd τSEach representing a weighting factor related to the number of millimeter-wave channel paths.
8. The massive MIMO channel state estimation apparatus of claim 7, wherein the constructing module is further configured to construct the objective function as follows:
introducing two auxiliary variables to eliminate the noise of the millimeter wave channel, wherein the two auxiliary variables are as follows:
Figure FDA0003097717520000031
Figure FDA0003097717520000032
updating the objective function as follows:
Figure FDA0003097717520000033
s.t.H＝Y and
Figure FDA0003097717520000034
wherein,
Figure FDA0003097717520000035
representing the square of the F-norm of the computation matrix C.
9. A computer terminal device, comprising:
one or more processors;
a memory coupled to the processor for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the massive MIMO channel state estimation method of any one of claims 1 to 4.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the massive MIMO channel state estimation method according to any one of claims 1 to 4.
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