CN116032699A - Sparse channel estimation method for ultra-large-scale MIMO system - Google Patents
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Abstract
The invention discloses a sparse channel estimation method for a super-large-scale MIMO system, which comprises the following steps: based on the double-layer block sparse structural characteristics of a channel in a time delay and polarization domain in an uplink ultra-large-scale MIMO communication system, constructing a channel sparse prior model, wherein the channel sparse prior model considers a near field effect when being constructed and is modeled based on spherical wave hypothesis; based on the channel sparse prior model, controlling a user terminal to send pilot symbols to a base station terminal, and receiving sparse channel vectors according to the pilot symbols received by the base station terminal; and based on a non-inversion block sparse variable decibel leaf-based inference algorithm, carrying out iterative updating on parameters to be estimated in the channel sparse prior model according to the sparse channel vector to obtain a channel estimation value. The invention can realize high-precision and low-complexity channel estimation.
Description
Technical Field
The invention relates to the technical field of wireless communication, in particular to a sparse channel estimation method for a super-large-scale MIMO system.
Background
Large-scale Multiple-input Multiple-Output (MIMO) has been widely deployed in current wireless communication networks as one of the key technologies for the new type of air interface of 5G. System gain is obtained by deploying a large antenna array at the Base Station (BS) side, including high spectral efficiency and capacity, high energy efficiency, and low complexity signal processing, etc. In order to meet the capacity and rate requirements of future 6G communication, a super-large-scale MIMO system with a higher dimension antenna array needs to be further deployed and a corresponding new signal processing method needs to be researched, particularly a channel estimation method is used for acquiring accurate channel state information (ChannelStateInformation, CSI), and is a basis for realizing technologies such as channel equalization, precoding, beam forming, resource allocation and the like, and is very important for guaranteeing effective transmission of super-large-scale MIMO signals and acquiring the gain of the super-large-scale MIMO system.
On the one hand, since future 6G super-large-scale MIMO systems configure larger-scale antenna arrays at the base station side, rayleigh distances (commonly defined as 2D 2 /λ c Wherein D represents the dimension of the antenna array, λ c Representing carrier wave wavelength) so that the mobile user and the scatterer are within the Rayleigh distance, and the channel sparse channel estimation method based on the plane wavefront assumption, which is widely adopted at present, is not applicable any more, and a channel sparse estimation model needs to be built under the spherical wave assumption; on the other hand, sparse channel estimation based on compressed sensing (compressed sensing, CS) theory has been widely studied in order to reduce pilot overhead and channel estimation algorithm complexity. Therefore, the invention aims to provide a new channel estimation scheme for a future 6G ultra-large-scale MIMO system.
Accordingly, there is a need for improvement and advancement in the art.
Disclosure of Invention
The invention aims to solve the technical problem of providing a sparse channel estimation method for a super-large-scale MIMO system aiming at the defects of the prior art, and aims to provide a new channel estimation scheme for a future 6G super-large-scale MIMO system.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
in a first aspect, the present invention provides a sparse channel estimation method for a super-large-scale MIMO system, where the method includes:
based on the double-layer block sparse structural characteristics of a channel in a time delay and polarization domain in an uplink ultra-large-scale MIMO communication system, constructing a channel sparse prior model, wherein the channel sparse prior model considers a near field effect when being constructed and is modeled based on spherical wave hypothesis;
based on the channel sparse prior model, controlling a user terminal to send pilot symbols to a base station terminal, and receiving sparse channel vectors according to the pilot symbols received by the base station terminal;
and based on a non-inversion block sparse variable decibel leaf-based inference algorithm, carrying out iterative updating on parameters to be estimated in the channel sparse prior model according to the sparse channel vector to obtain a channel estimation value.
In one implementation, the sparsity of the channel in the time delay and polarization domain is expressed as:
wherein ,
w represents a sparse channel vector to be estimated, q=mxi represents the number of polarization domain samples, I represents the number of spatial distance samples, M represents the dimension, L represents the number of multipaths, B represents a sparse dictionary,
representing the kronecker product.
In one implementation, w has a block-sparse structure similar to h, referred to as outer layer block sparsity; w (w) l The block sparse structure is called inner layer block sparsity;
wherein ,
the sparse representation vector of the channel under the sparse dictionary B is represented.
In one implementation, the polarization domain is an angle-space distance domain, and the constructing a channel sparse prior model based on a double-layer block sparse structure feature of a channel in a time delay and polarization domain in an uplink ultra-large-scale MIMO communication system includes:
sampling in both the angular and spatial dimensions to construct a sparse representation matrix of the channel in near field conditions, where for angular domain sampling, the sampling angle is represented as
For spatial distance sampling, relative to angle θ l，m Is expressed as the sampling distance of (2)
wherein ,
wherein ,βΔ A low coherence threshold that guarantees a sparse channel representation matrix is represented.
In one implementation, the method further comprises:
constructing a system model of ultra-large-scale MIMO, wherein the system model is as follows under near-field channel sparse representation: y=Φw+n,
wherein ,
diagonal matrix representing signal vector x, +.>
A submatrix representing a discrete fourier matrix, K being pilot symbols, wherein the (K, l) th term is
Represents additive Gaussian white noise satisfying complex Gaussian distribution CN (0, sigma) -1 I MK ) Where σ represents the noise accuracy.
In one implementation, the sparse channel vector w to be estimated satisfies the following a priori distribution:
wherein ,
as non-negative parameters, the sparsity of the outer layer blocks of w is controlled, when gamma l When=0, w l The variance of (2) is 0, indicating that the values in the block are all 0, < >>
Λ l Is a diagonal matrix, which controls w l Is represented as
wherein ,∧l，q ＝(α l，q +βα l，q-1 +βα l，q+1 ) -1 ，α l，q For non-negative parameters, beta characterizes parameter alpha l，q And its neighboring parameters { alpha } l，q-1 ，α l，q+1 Correlation between.
In one implementation manner, the estimating method based on the non-inversion block sparse variance dbis inference algorithm iteratively updates parameters to be estimated in the channel sparse prior model according to the sparse channel vector to obtain a channel estimation value, and includes:
presetting the maximum iteration times;
iteratively updating posterior distribution of each parameter to be estimated in the channel sparse prior model based on an inversion-free block sparse variable decibel leaf inference algorithm;
and stopping iteration if the current iteration number meets the maximum iteration number, and obtaining the channel estimation value.
In one implementation manner, the estimating method based on the non-inversion block sparse variance dbis inference algorithm iteratively updates parameters to be estimated in the channel sparse prior model according to the sparse channel vector to obtain a channel estimation value, and includes:
presetting an error value of posterior distribution after updating the parameters to be estimated;
iteratively updating posterior distribution of each parameter to be estimated in the channel sparse prior model based on an inversion-free block sparse variable decibel leaf-based inference algorithm, and determining actual error values of the posterior distribution after updating the parameter to be estimated;
and stopping iteration if the actual error value meets the preset error value, and obtaining the channel estimation value.
In a second aspect, an embodiment of the present invention further provides a sparse channel estimation system for a super-large-scale MIMO system, where the system includes:
the model construction module is used for constructing a channel sparse prior model based on the double-layer block sparse structural characteristics of a channel in a time delay and polarization domain in an uplink ultra-large-scale MIMO communication system, wherein the channel sparse prior model considers a near field effect when being constructed and is modeled based on spherical wave hypothesis;
the channel receiving module is used for controlling a user terminal to send pilot symbols to a base station terminal based on the channel sparse prior model, and receiving sparse channel vectors according to the pilot symbols received by the base station terminal;
and the channel estimation module is used for carrying out iterative updating on parameters to be estimated in the channel sparse prior model according to the sparse channel vector based on an inverse block-free sparse-variance decibel leaf-based inference algorithm to obtain a channel estimation value.
In a third aspect, an embodiment of the present invention further provides a terminal device, where the terminal device is applied to a receiving end or a transmitting end, and includes a memory, a processor, and a sparse channel estimation program for a super-large scale MIMO system stored in the memory and capable of running on the processor, where the processor implements the steps of the sparse channel estimation method for a super-large scale MIMO system of any one of the above schemes when executing the sparse channel estimation program for a super-large scale MIMO system.
In a fourth aspect, an embodiment of the present invention further provides a computer readable storage medium, where a sparse channel estimation program for a super-large scale MIMO system is stored on the computer readable storage medium, and when the sparse channel estimation program for the super-large scale MIMO system is executed by a processor, the steps of the sparse channel estimation method for the super-large scale MIMO system according to any one of the above schemes are implemented.
The beneficial effects are that: compared with the prior art, the invention provides a sparse channel estimation method for a super-large-scale MIMO system, which is characterized in that firstly, a channel sparse prior model is constructed based on the double-layer block sparse structure characteristics of a channel in a time delay and polarization domain in an uplink super-large-scale MIMO communication system, wherein the channel sparse prior model considers near field effect when being established and is modeled based on spherical wave hypothesis. And then, based on the channel sparse prior model, controlling a user terminal to send pilot symbols to a base station terminal, and receiving sparse channel vectors according to the pilot symbols received by the base station terminal. And finally, based on a non-inversion block sparse variable decibel leaf-based inference algorithm, carrying out iterative updating on parameters to be estimated in the channel sparse prior model according to the sparse channel vector to obtain a channel estimation value. The invention can realize high-precision and low-complexity channel estimation.
Drawings
Fig. 1 is a flowchart of a specific implementation of a sparse channel estimation method for a super-large-scale MIMO system according to an embodiment of the present invention.
Fig. 2 is an overall architecture diagram of a sparse channel estimation system for a super-large-scale MIMO system according to an embodiment of the present invention.
Fig. 3 is a schematic block diagram of an internal structure of a terminal device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and effects of the present invention clearer and more specific, the present invention will be described in further detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment provides a sparse channel estimation method for a super-large-scale MIMO system, and when the method is specifically applied, the method is firstly based on the double-layer block sparse structure characteristics of a channel in a time delay and polarization domain in an uplink super-large-scale MIMO communication system to construct a channel sparse prior model, wherein the channel sparse prior model considers a near field effect when being established and is modeled based on spherical wave hypothesis. And then, based on the channel sparse prior model, controlling a user terminal to send pilot symbols to a base station terminal, and receiving sparse channel vectors according to the pilot symbols received by the base station terminal. And finally, based on a non-inversion block sparse variable decibel leaf-based inference algorithm, carrying out iterative updating on parameters to be estimated in the channel sparse prior model according to the sparse channel vector to obtain a channel estimation value. The invention can realize high-precision and low-complexity channel estimation.
Exemplary method
The sparse channel estimation method for the ultra-large-scale MIMO system can be applied to terminal equipment, wherein the terminal equipment is intelligent product equipment such as computers, mobile phones and intelligent televisions. As shown in fig. 1, the sparse channel estimation method for a super-large-scale MIMO system of the present embodiment includes the steps of:
step S100, a channel sparse prior model is built based on double-layer block sparse structural features of a channel in a time delay and polarization domain in an uplink ultra-large-scale MIMO communication system, wherein the channel sparse prior model considers near field effect and is modeled based on spherical wave hypothesis when being built.
Step 200, based on the channel sparse prior model, controlling a user terminal to send pilot symbols to a base station terminal, and receiving sparse channel vectors according to the pilot symbols received by the base station terminal;
and step S300, based on a non-inversion block sparse variance dB-based inference algorithm, carrying out iterative updating on parameters to be estimated in the channel sparse prior model according to the sparse channel vector to obtain a channel estimation value.
In specific application, the embodiment considers the near field effect caused by the deployment of the high-dimensional antenna array in the 6G ultra-large-scale MIMO system, digs the potential structural characteristics of the channel in the time delay-polarization domain, and constructs the structural sparse channel estimation problem; secondly, designing a sparse prior model for describing a double-layer block channel of a channel in a time delay-polarization domain, wherein the sparse prior model describes a double-layer block channel sparse structure of the channel by using a group of super parameters; finally, aiming at the high computational complexity caused by matrix inversion under the current sparse Bayesian framework, a high-efficiency sparse Bayesian learning algorithm without matrix Inversion (IF) is provided, and high-precision and low-complexity channel estimation is realized.
In particular, the present embodiment contemplates an uplink super-large-scale MIMO communication system. The dimension of the allocation at the base station is M (M>>1) Is used to serve single antenna users and employs orthogonal frequency division multiplexing (Orthogonal Frequency Division Multiplexing, OFDM) as a signaling mechanism. In order to estimate the uplink channel, the present embodiment adopts a pilot-assisted channel estimation method, i.e., K (K<<N) subcarriers are used for transmitting pilot symbols, and pilot position identification is defined as
Accordingly, the signal vector of the transmission pilot symbol is x= [ x (p 1 )，x(p 2 )，...，x(p K )] T . Therefore, the received signal containing K pilot symbols received by the antenna array at the base station end is the following formula (1), that is, the system model of the ultra-large-scale MIMO constructed in this embodiment.
in the formula ,
diagonal matrix representing signal vector x, +.>
A submatrix representing a discrete fourier matrix, wherein the (k, l) th term is +.>
Represents the Cronecker product, I M Representing an identity matrix with dimension M +.>
Represents a time domain channel vector, L represents the number of multipaths, < +.>
Represents additive Gaussian white noise satisfying complex Gaussian distribution CN (0, sigma) -1 I MK ) Where σ represents the noise accuracy.
Further, the present embodiment considers the near field effect,
further modeled under spherical wave assumption as
in the formula ,λc Representing carrier wavelength, S l Represents the number g of sub-paths included in the first path l，s Represents the power of the s-th sub-path in the first path, θ l，s Representing the arrival angle from the s-th sub-path to the center of the base station antenna array in the first path, r l，s Representing the distance from the s-th sub-path in the first path to the center of the base station antenna array,
representing the distance from the s-th sub-path to the m-th antenna array at the base station side in the first path, can be further calculated as
in the formula ,δm The identity of the mth antenna element in the antenna array is defined as
d represents the distance between adjacent antenna elements.
From equations (2) and (3), the antenna response vector b (θ) under near field conditions l，s ，r l，s ) Depending on the angle of arrival of the radiation passing through the scatterer and on the spatial position between the base stations. Therefore, this embodiment requires sampling in both the angular and spatial dimensions to construct a channel sparse representation matrix under near field conditions. Notably, the angle-space distance domain is also referred to as a polarization domain, since the angle and space distance can represent coordinates in a polar coordinate system. For angle domain sampling, the sampling angle is expressed as:
for spatial distance sampling, relative to angle θ l.m Is representative of the sampling distance of (2)Is that
in the formula ,
wherein ,βΔ Representing a low coherence threshold that guarantees a sparse channel representation matrix, I represents the number of spatial distance samples. />
Channel(s)
Can be further sparsely represented as
B＝[B 1 ，...，B i ，...，B I ]， (8)
B i ＝[b(θ l，0 ，r l，0 (i))，...，b(θ l，M-1 ，r l，M-1 (i))]， (9)
in the formula ,
representing the sparse representation vector of the channel under sparse dictionary B, q=m×i represents the number of polarization domain samples. Based on equation (4-7), channel h is further denoted as
in the formula ,
for the sparse channel vector w to be estimated, it has a two-layer block sparse structure for the following reasons: in one aspect, a number of channel measurements indicate a number of multipaths with greater power in the time domain channelThe number of eyes is smaller, and therefore,
representing all zero blocks or all non-zero blocks, so that the channel h has a block sparse structure, namely I H I 0，K <<L, wherein I 0，K Representing the number of non-zero blocks. Thus, w has a block-sparse structure similar to h, referred to as outer layer block sparsity. On the other hand, the theory of geometric-based random channel modeling shows that the channel multipath is modeled as a scattering cluster, and the scattering cluster comprises a plurality of sub-clusters, wherein the sub-clusters are distributed near the central arrival angle. w (w) l The position of the non-zero element in (b) is related to the angle of arrival of the sub-cluster, thus w l Has a block sparse structure, called inner layer block sparsity. The two aspects are considered, and the sparse vector w to be estimated has a double-layer block sparse structure.
In addition, this embodiment also constructs a system model of ultra-large-scale MIMO, and rewrites the system model shown in formula (1) into one under near-field channel sparse representation
y＝Φw+n， (1 1)
in the formula ,
further, the channel w has a double-layer block sparse structure in the time delay-polarization domain, and can be used for designing a channel estimation algorithm to improve the performance of large-scale MIMO channel estimation. In order to characterize such a channel sparse structure, a sparse channel vector w to be estimated is defined to satisfy the following a priori distribution:
in the formula ,
as a non-negative parameter, the outer layer block sparsity of w is controlled. When gamma is l When=0, w l The variance of (2) is 0, indicating that the values in the block are all 0. Therefore, the number of non-zeros in γ determines the outer block sparsity. At the same time (I)>
A l Is a diagonal matrix, which controls w l Is represented as
Λ l ＝diag([∧ l，1 ，...，∧ l，q ，...，∧ l，Q ] T )， (13)
in the formula ,∧l，q ＝(α l，q +βα l,q-1 +βα l,q+1 ) -1 ，α l,q For non-negative parameters, beta characterizes parameter alpha l,q And its neighboring parameters { alpha } l,q-1 ，α l,q+1 Correlation between. Meanwhile, in order to further promote the sparsity of the model, γl and α are defined l,q The a priori distribution of (c) satisfies the Gamma distribution, i.e
P(γ l |λ)＝λexp(-λ γl )， (14)
P(α l，q |ξ l )＝ξ l exp(-ξ l α l，q )， (15)
Wherein λ is a number equal to γ l Related parameters, ζ l Is equal to alpha l，q Related parameters.
In the traditional sparse Bayesian learning, the defect of high computational complexity of inversion operation in the variance solving process of posterior distribution exists. This example, by reference to the concept of non-inversion sparse Bayesian learning (Inverse-free sparse Bayesian learning, IF-SBL), proposes a lower relaxation likelihood limit (relaxed evidence lower bound, R-ELBO) for w as:
in the formula ,
wherein, eig (·) represents the value of the feature (·) H The transpose of the representation matrix is inverted. Therefore, the lower limit of the P (y|w, σ) distribution is
Based on the channel prior model to be estimated and the super parameters thereof, the channel to be estimated and the parameter set thereof are Θ
{ w, γ, α, ζ, λ, σ }, wherein ∈>
Because the accurate posterior distribution of the parameters to be estimated in Θ is difficult to obtain, the channel estimation algorithm based on the variable decibel leaf reasoning is provided in the embodiment. The Θ posterior approximation distribution factorizes into q (Θ) =q (w) q (γ) q (α) q (ζ) q (λ) q (σ), where q (x) represents the posterior distribution of factor x. Maximizing R-ELB0 is obtained by alternately updating the approximate posterior distribution of the parameters to be estimated, and the optimized expression of q (Θ) is
in the formula ,
Θ n representing the n-th parameter to be estimated in Θ, < >>
Represents the relation q (Θ)/q (Θ) n ) Q (Θ)/q (Θ) n ) Indicating that q (Θ) n ) Q (Θ) distribution of (a). Based on equation (18), the posterior distribution of each parameter to be estimated is updated step by step while leaving the other parameters unchanged during the iteration. After the iteration converges, the estimated value of each parameter is equal to the expectation of the posterior distribution. Specifically, the present embodiment sets the maximum number of iterations in advance. And then, iteratively updating posterior distribution of each parameter to be estimated in the channel sparse prior model based on an inverse block-free sparse variable decibel leaf inference algorithm. If the current iteration number meets the maximum iteration number, stopping iteration and obtaining the channel estimation value. Or in another implementation manner, the embodiment presets the error value of the posterior distribution after the parameter to be estimated is updated. And then, iteratively updating posterior distribution of each parameter to be estimated in the channel sparse prior model based on an inverse block-free sparse variable decibel leaf-based inference algorithm, and determining actual error values of the posterior distribution after updating the parameters to be estimated. And stopping iteration if the actual error value meets the preset error value, and obtaining the channel estimation value.
The update rules for each parameter to be estimated are given in detail below.
For update q (w):
lnq(w)∝<lnG(y，w，z，σ)+lnP(w|γ，Λ)> q(γ)q(α)q(σ) ∝-<σ) q(σ) w H (Γz+Φ H (y-Φz))-w H (Γ<σ> q(σ) +Ω -1 )w， (19)
in the formula ,
/>
as shown in formula (19), q (w) satisfies complex Gaussian distribution, i.e., q (w) to CN (w|mu, Σ), the mean and variance of which are expressed as
μ＝(σ> q(σ) ∑(Γz+Φ H (y-Φz))， (20)
∑＝(Γ<σ> q(σ) +Ω -1 ) -1 ， (21)
Update q (γ):
in the formula ,<|wl,q | 2 ＞＝|μ l，q | 2 +∑ l，q . Gamma is represented by formula (22) l Satisfy generalized inverse Gaussian (Gene)ralized Inverse Gaussian, GIG). Thus, gamma l Is calculated as the mean value of
in the formula ,
K v (. Cndot.) represents a second order modified Bessel function.
Update q (α):
from formula (24), it can be seen that with alpha l，q The relevant items are
Then it can be seen that alpha l，q The updated expression of (2) is
Wherein χ is E (0, 1). Notably, here use is made of
Approximately q (alpha) as a distribution l，q ) Is a mean value of (c).
Updates q (ζ), q (λ) and q (σ):
the above expression shows that q (ζ) l ) Q (lambda) and q (sigma) satisfy Gamma distribution, so their mean value is expressed as
After obtaining the updated expressions of all the unknown parameters in Θ, the present embodiment also proposes to summarize the entire channel estimation procedure using algorithm 1, which is denoted as a super-large-scale MIMO sparse channel estimation method based on Inverse Block-free Block-sparse Variational Bayesian Inference, IB-VBI. The convergence of the IB-VBI algorithm proposed by the present invention is described below. The IB-VBI algorithm proposed in this embodiment is derived under a variational Bayesian inference framework, so that the convergence of the IB-VBI algorithm can be ensured by maximizing the optimized expression (18) of R-ELBO based on the local convergence of the variational Bayesian inference. Algorithm 1 is as follows:
input:
the maximum iteration number Tmax, tolerates the error epsilon.
Initializing:
and (3) circulation:
Updating according to (4-31)
Updating according to (4-32)
Until: t > T max or 
And (3) outputting: w (W) est ＝μ
It can be seen that, in this embodiment, a channel sparse prior model is constructed based on a two-layer block sparse structure feature of a channel in a time delay and polarization domain in an uplink ultra-large-scale MIMO communication system, where the channel sparse prior model considers a near-field effect when being established and is modeled based on a spherical wave hypothesis. And then, based on the channel sparse prior model, controlling a user terminal to send pilot symbols to a base station terminal, and receiving sparse channel vectors according to the pilot symbols received by the base station terminal. And finally, based on a non-inversion block sparse variable decibel leaf-based inference algorithm, carrying out iterative updating on parameters to be estimated in the channel sparse prior model according to the sparse channel vector to obtain a channel estimation value. In the embodiment, a near field effect in the ultra-large-scale MIMO channel is considered, and the invention provides a novel double-layer block sparse prior model for representing a double-layer block sparse structure of an uplink ultra-large-scale MIMO sparse channel in a time delay-polarization domain; aiming at the high computational complexity caused by matrix inversion under the current sparse Bayesian framework, the embodiment provides a high-efficiency sparse Bayesian learning algorithm without matrix inversion (Inverse), namely IB-VBI, so as to realize high-precision and low-complexity channel estimation.
Exemplary System
Based on the above embodiment, the present invention further provides a sparse channel estimation system for a super-large-scale MIMO system, as shown in fig. 2, the system includes: model building block 10, channel receiving block 20 and channel estimating block 30. Specifically, the model building module 10 is configured to build a channel sparse prior model based on a two-layer block sparse structure feature of a channel in a delay and polarization domain in an uplink ultra-large-scale MIMO communication system, where the channel sparse prior model considers a near field effect when building and performs modeling based on a spherical wave hypothesis. The channel receiving module 20 is configured to control a user terminal to send pilot symbols to a base station terminal based on the channel sparse prior model, and receive sparse channel vectors according to the pilot symbols received by the base station terminal. The channel estimation module 30 is configured to iteratively update parameters to be estimated in the channel sparse prior model according to the sparse channel vector based on an inverse block-free sparse variational bayesian inference algorithm, so as to obtain a channel estimation value.
The working principle of each module in the sparse channel estimation system for the ultra-large-scale MIMO system in this embodiment is the same as the execution process of each step in the above method embodiment, and is not described here in detail.
Based on the above embodiment, the present invention further provides a terminal device, and a schematic block diagram of the terminal device may be shown in fig. 3. The terminal device of the present embodiment may include one or more processors 100 (only one is shown in fig. 3), a memory 101, and a computer program 102 stored in the memory 101 and executable on the one or more processors 100, for example, a program for sparse channel estimation for a very large scale MIMO system. The execution of the computer program 102 by one or more processors 100 may implement various steps in an embodiment of a method for sparse channel estimation for a very large scale MIMO system. Alternatively, the one or more processors 100, when executing computer program 102, may implement the functions of the various modules/units in an apparatus embodiment for sparse channel estimation for a very large scale MIMO system, without limitation.
In one embodiment, the processor 100 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (ApplicationSpecific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
In one embodiment, the memory 101 may be an internal storage unit of the electronic device, such as a hard disk or a memory of the electronic device. The memory 101 may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart card (SMC), a Secure Digital (SD) card, a flash card (flash card) or the like, which are provided on the electronic device. Further, the memory 101 may also include both an internal storage unit and an external storage device of the electronic device. The memory 101 is used to store computer programs and other programs and data required by the terminal device. The memory 101 may also be used to temporarily store data that has been output or is to be output.
It will be appreciated by persons skilled in the art that the functional block diagram shown in fig. 3 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the terminal device to which the present inventive arrangements are applied, and that a particular terminal device may include more or fewer components than shown, or may combine some of the components, or may have a different arrangement of components.
Those skilled in the art will appreciate that implementing all or part of the above-described methods may be accomplished by way of a computer program, which may be stored on a non-transitory computer readable storage medium and which, when executed, may comprise the steps of the above-described embodiments of the methods. Any reference to memory, storage, operational database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual operation data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (11)
1. A sparse channel estimation method for a super-large-scale MIMO system, the method comprising:
based on the double-layer block sparse structural characteristics of a channel in a time delay and polarization domain in an uplink ultra-large-scale MIMO communication system, constructing a channel sparse prior model, wherein the channel sparse prior model considers a near field effect when being constructed and is modeled based on spherical wave hypothesis;
based on the channel sparse prior model, controlling a user terminal to send pilot symbols to a base station terminal, and receiving sparse channel vectors according to the pilot symbols received by the base station terminal;
and based on a non-inversion block sparse variable decibel leaf-based inference algorithm, carrying out iterative updating on parameters to be estimated in the channel sparse prior model according to the sparse channel vector to obtain a channel estimation value.
2. The sparse channel estimation method for a very large scale MIMO system of claim 1, wherein the sparse representation of the channel in the time delay and polarization domain is:
wherein ,
w represents a sparse channel vector to be estimated, q=mxi represents the number of polarization domain samples, I represents the number of spatial distance samples, M represents the dimension, L represents the number of multipaths, B represents a sparse dictionary, < >>
Representing the kronecker product.
3. The sparse channel estimation method for a very large scale MIMO system according to claim 2, wherein w has a block sparse structure similar to h, called outer layer block sparsity; w (w) l The block sparse structure is called inner layer block sparsity;
wherein ,
the sparse representation vector of the channel under the sparse dictionary B is represented.
4. The sparse channel estimation method for a super-large-scale MIMO system according to claim 3, wherein the polarization domain is an angle-space distance domain, the constructing a channel sparse prior model based on a two-layer block sparse structure feature of a channel in a time delay and polarization domain in an uplink super-large-scale MIMO communication system comprises:
sampling in both the angular and spatial dimensions to construct a sparse representation matrix of the channel in near field conditions, where for angular domain sampling, the sampling angle is represented as
For spatial distance sampling, relative to angle θ l,m Is expressed as the sampling distance of (2)
wherein ,
wherein ,βΔ A low coherence threshold that guarantees a sparse channel representation matrix is represented.
5. The sparse channel estimation method for a very large scale MIMO system of claim 4, further comprising:
constructing a system model of ultra-large-scale MIMO, wherein the system model is as follows under near-field channel sparse representation: y=Φw+n,
wherein ,
a diagonal matrix representing the signal vector x,
a submatrix representing a discrete fourier matrix, K being pilot symbols, wherein the (K, l) th term is
Representing additive Gaussian white noise satisfying complex Gaussian distribution CN0, sigma -1 I MK ) Where σ represents the noise accuracy.
6. A sparse channel estimation method for a very large scale MIMO system according to claim 3, wherein the sparse channel vector w to be estimated satisfies the following a priori distribution:
wherein ,
as non-negative parameters, the sparsity of the outer layer blocks of w is controlled, when gamma l When=0, w l The variance of (2) is 0, indicating that the values in the block are all 0, < >>
Λ l Is a diagonal matrix, which controls w l Is represented as
Λ l ＝diag([Λ l,1 ,...,Λ l,q ,...,Λ l,Q ] T ),
wherein ,Λl,q ＝(α l,q +βα l,q-1 +βα l,q+1 ) -1 ，α l,q For non-negative parameters, beta characterizes parameter alpha l,q And its neighboring parameters { alpha } l,q-1 ,α l,q+1 Correlation between.
7. The sparse channel estimation method for the ultra-large-scale MIMO system according to claim 1, wherein the iterative updating of parameters to be estimated in the channel sparse prior model according to the sparse channel vector based on the non-inversion block sparse variational bayesian inference algorithm to obtain a channel estimation value comprises:
presetting the maximum iteration times;
iteratively updating posterior distribution of each parameter to be estimated in the channel sparse prior model based on an inversion-free block sparse variable decibel leaf inference algorithm;
and stopping iteration if the current iteration number meets the maximum iteration number, and obtaining the channel estimation value.
8. The sparse channel estimation method for the ultra-large-scale MIMO system according to claim 1, wherein the iterative updating of parameters to be estimated in the channel sparse prior model according to the sparse channel vector based on the non-inversion block sparse variational bayesian inference algorithm to obtain a channel estimation value comprises:
presetting an error value of posterior distribution after updating the parameters to be estimated;
iteratively updating posterior distribution of each parameter to be estimated in the channel sparse prior model based on an inversion-free block sparse variable decibel leaf-based inference algorithm, and determining actual error values of the posterior distribution after updating the parameter to be estimated;
and stopping iteration if the actual error value meets the preset error value, and obtaining the channel estimation value.
9. A sparse channel estimation system for a super-large-scale MIMO system, the system comprising:
the model construction module is used for constructing a channel sparse prior model based on the double-layer block sparse structural characteristics of a channel in a time delay and polarization domain in an uplink ultra-large-scale MIMO communication system, wherein the channel sparse prior model considers a near field effect when being constructed and is modeled based on spherical wave hypothesis;
the channel receiving module is used for controlling a user terminal to send pilot symbols to a base station terminal based on the channel sparse prior model, and receiving sparse channel vectors according to the pilot symbols received by the base station terminal;
and the channel estimation module is used for carrying out iterative updating on parameters to be estimated in the channel sparse prior model according to the sparse channel vector based on an inverse block-free sparse-variance decibel leaf-based inference algorithm to obtain a channel estimation value.
10. A terminal device, characterized in that the terminal device is applied to a receiving end or a transmitting end, and comprises a memory, a processor and a sparse channel estimation program for a super-large scale MIMO system stored in the memory and capable of running on the processor, wherein the processor implements the steps of the sparse channel estimation method for a super-large scale MIMO system according to any one of claims 1-10 when executing the sparse channel estimation program for a super-large scale MIMO system.
11. A computer readable storage medium, wherein a sparse channel estimation program for a super-MIMO system is stored on the computer readable storage medium, and when the sparse channel estimation program for a super-MIMO system is executed by a processor, the steps of the sparse channel estimation method for a super-MIMO system according to any one of claims 1-10 are implemented.
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