Intelligent reflector-assisted communication system channel estimation method, device and storage medium

Abstract

The present invention discloses a channel estimation method, device and storage medium for an intelligent reflector-assisted communication system, belonging to the field of communication systems. The method comprises: step 1: obtaining channel estimation parameters, including: a received signal matrix Y, a pilot matrix #imgabs0# and a phase shift matrix #imgabs1# and setting a maximum number of iterations I t , termination threshold ξ t , distribution parameters {a, b, c, d, e, f}; Step 2: Initialize the grouping coefficient ω k , iteration count i t , and the channel estimate q m ; Step 3: Update parameters β, μ in turn through the variational Bayesian expectation maximization algorithm m s, #imgabs2#lnπ k s,ln(1‑π k )s,γ,φ m,k s, let i t =i t +1, q m (i t )=μ m ; Step 4: Iteration termination condition judgment, if i t <I t and #imgabs3# then return to step 4; if i t ≥I t or #imgabs4# then the iteration is terminated; Step 5: Output the estimated parameters, including the channel vector estimation result q m And the grouping result z m This method can improve the accuracy of channel estimation and group channels.

Description

Technical Field

[0001] The invention relates to the field of channel estimation of communication systems, and in particular to a channel estimation method of an OFDM communication system assisted by an intelligent reflecting surface. Background Art

[0002] Intelligent Reflecting Surface (IRS) assisted technology is a new research field that has attracted widespread attention from researchers at home and abroad since 2019. IRS assisted technology can achieve the purpose of configuring the wireless propagation environment, thereby greatly improving the wireless spectrum utilization and energy efficiency, which will have a profound impact on the network design and optimization methods of the next generation of communication systems. Channel estimation is the basis for the operation of IRS assisted communication systems and plays a vital role in the performance of communication systems. However, there are a large number of passive reflective elements in IRS and lack of signal processing capabilities, so traditional pilot-assisted channel estimation methods cannot be directly applied. The IRS-assisted orthogonal frequency division multiplexing (OFDM) communication system channel exhibits sparse characteristics in the delay domain, but few existing estimation methods can fully exploit the sparse characteristics of the IRS-assisted OFDM communication system channel. When dealing with complex wireless communication scenarios, there is inevitably a loss in channel estimation performance. Therefore, how to provide an IRS-assisted OFDM communication system for accurate channel estimation is a problem that needs to be solved.

[0003] In view of this, the present invention is proposed. Summary of the invention

[0004] The purpose of the present invention is to provide a channel estimation method, device and storage medium for an intelligent reflector-assisted communication system, which can fully exploit the sparse characteristics of the channel of the IRS-assisted OFDM communication system, and make full use of the potential grouping information between channels corresponding to the sub-planes, further exploit the common sparsity between channels, and improve the channel estimation accuracy.

[0005] The objective of the present invention is achieved through the following technical solutions:

[0006] The embodiment of the present invention provides a channel estimation method for an intelligent reflector-assisted communication system, which is used in an intelligent reflector-assisted communication system to estimate a channel between any base station antenna of the communication system and any user mobile terminal, and includes the following steps:

[0007] Step 1: Obtain channel estimation parameters from the communication system. The channel estimation parameters include: received signal matrix Y, pilot matrix IRS reflection coefficient matrix And set the channel estimation control parameters, including: maximum number of iterations I t , termination threshold ξ t The initial values ​​of a, b, c, d, e, and f are all 10. -10 ;

[0008] Step 2: Initialize the channel grouping coefficient ω k , iteration count i t and the channel vector q m ;

[0009] Step 3: Use the variational Bayesian expectation maximization algorithm to iteratively update the parameters β and μ m s, lnπ k s, ln(1-π k )s, γ and φ m,k s; let iteration count i t =i t +1, channel vector q m (i t )=μ m ;in,

[0010] Channel vector q m The updated posterior distribution is μ m Represents the mean vector matrix of the posterior distribution of the channel vector after update; Σ m Represents the covariance matrix of the posterior distribution after the channel vector is updated; the channel vector q m Obey complex Gaussian distribution α m represents the precision vector of the channel vector, which is distributed as follows: If the mth subplane is divided into the kth group, then The precision vector representing the common sparsity within the kth group; β = σ -2 Represents the noise accuracy, which obeys the gamma distribution, that is, β~Gamma(a,b); represents the transmitted pilot vector, where N p represents the number of pilots, is randomly generated from [0,2π); express Updated posterior mean; <lnπ k > and <ln(1-π k )> respectively represent π k After the update, lnπ k and ln(1-π k ), π k represents the scaling factor of channel grouping; represents the updated posterior mean of γ, and γ represents π k The parameters of the Beta distribution are obeyed, and γ obeys the Gamma distribution, that is, γ|e,f~Gamma(e,f); express <z m,k =1>, is the updated z m,k =1, which means the probability that the m-th sub-plane is assigned to the k-th group after this iteration.

[0011] Step 4: If the iteration count i t <Maximum number of iterations I t and Then return to step 3; if the iteration count i t ≥ Maximum number of iterations I t or Then the iteration is terminated and step 5 is executed; the Err(i t ) represents the i-th t The error of the channel vector estimate at the iteration;

[0012] Step 5: output estimated parameters, the estimated parameters include: channel vector q m The estimated value and channel grouping value z m .

[0013] A processing device, comprising:

[0014] at least one memory for storing one or more programs;

[0015] At least one processor can execute one or more programs stored in the memory. When the one or more programs are executed by the processor, the processor can implement the method described in the present invention.

[0016] A readable storage medium stores a computer program, which can implement the method described in the present invention when the computer program is executed by a processor.

[0017] Compared with the prior art, the intelligent reflector-assisted communication system channel estimation method, device and storage medium provided by the present invention have the following beneficial effects:

[0018] The present invention fully exploits the sparse characteristics of the channels of the IRS-assisted OFDM communication system in a sparse Bayesian learning manner, can utilize the potential grouping information between channels corresponding to the sub-planes of the smart reflection surface, further exploit the common sparsity between channels, improve the channel estimation accuracy and group the channels. In the actual IRS-assisted OFDM communication system, the present invention has practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0020] Figure 1 A schematic flow chart of a channel estimation method for an intelligent reflecting surface assisted communication system provided in an embodiment of the present invention.

[0021] Figure 2 A hierarchical probability model diagram of the channel estimation method provided in an embodiment of the present invention.

[0022] Figure 3 The channel estimation method provided in the embodiment of the present invention is in N p =64, M=15, I0=20, the actual number of groups K * =3Results of the change with the number of iterations under different signal-to-noise ratios.

[0023] Figure 4 The channel estimation method provided in the embodiment of the present invention is when M=15, I0=20, and the actual number of packets K * =3, with the number of pilots N at a signal-to-noise ratio of 10 dB p The results of the changes compared with other algorithms.

[0024] Figure 5 The channel estimation method provided in the embodiment of the present invention is in N p =64,M=15, the actual number of groups K * =3, the results of comparison with other algorithms when the number of OFDM symbols I0 changes at a signal-to-noise ratio of 10dB.

[0025] Figure 6 The channel estimation method provided in the embodiment of the present invention is in N p =64, M=15, I0=20, the actual number of groups K * =3 and compared with other algorithms as the signal-to-noise ratio changes.

[0026] Figure 7 The channel estimation method provided in the embodiment of the present invention is in N p =64, M=15, I0=20, the actual number of groups K * =15 and compared with other algorithms as the signal-to-noise ratio changes.

[0027] Figure 8 The channel estimation method provided in the embodiment of the present invention is in N p =64, M=15, I0=20, the actual number of groups K *=3 grouping results. DETAILED DESCRIPTION

[0028] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in combination with the specific content of the present invention; it is obvious that the described embodiments are only part of the embodiments of the present invention, not all of the embodiments, which does not constitute a limitation of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the protection scope of the present invention.

[0029] First, the terms that may be used in this article are explained as follows:

[0030] The term “and / or” means that either or both of them can be realized at the same time. For example, X and / or Y means both “X” or “Y” and “X and Y”.

[0031] The terms "include", "comprises", "contains", "has" or other descriptions with similar semantics should be interpreted as non-exclusive inclusion. For example, including certain technical feature elements (such as raw materials, components, ingredients, carriers, dosage forms, materials, dimensions, parts, components, mechanisms, devices, steps, procedures, methods, reaction conditions, processing conditions, parameters, algorithms, signals, data, products or products, etc.) should be interpreted as including not only certain technical feature elements explicitly listed, but also other technical feature elements known in the art that are not explicitly listed.

[0032] The term "consisting of..." means excluding any technical feature elements not explicitly listed. If this term is used in a claim, it will make the claim closed, so that it does not contain technical feature elements other than the technical feature elements explicitly listed, except for the conventional impurities related to them. If this term only appears in a clause of a claim, it only limits the elements explicitly listed in the clause, and the elements recorded in other clauses are not excluded from the overall claim.

[0033] Unless otherwise specified or limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this article can be understood according to specific circumstances.

[0034] When concentration, temperature, pressure, size or other parameters are expressed in the form of a numerical range, the numerical range should be understood to specifically disclose all ranges formed by the pairing of any upper limit, lower limit, and preferred value in the numerical range, regardless of whether the range is explicitly stated; for example, if a numerical range of "2 to 8" is stated, the numerical range should be interpreted as including ranges such as "2 to 7", "2 to 6", "5 to 7", "3 to 4 and 6 to 7", "3 to 5 and 7", "2 and 5 to 7", etc. Unless otherwise specified, the numerical ranges stated herein include both their end values ​​and all integers and fractions within the numerical range.

[0035] The orientation or position relationship indicated by terms such as "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", etc. are based on the orientation or position relationship shown in the drawings and are only for the convenience and simplification of description, and do not explicitly or implicitly indicate that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation of this document.

[0036] The channel estimation method, device and storage medium of the intelligent reflector-assisted communication system provided by the present invention are described in detail below. The contents not described in detail in the embodiments of the present invention belong to the prior art known to professionals in the field. If no specific conditions are specified in the embodiments of the present invention, the conditions are carried out according to the conventional conditions in the field or the conditions recommended by the manufacturer. If the manufacturer is not specified in the reagents or instruments used in the embodiments of the present invention, they are all conventional products that can be purchased commercially.

[0037] like Figure 1 As shown, an embodiment of the present invention provides a channel estimation method for an intelligent reflective surface assisted communication system, which is used in an intelligent reflective surface assisted communication system to estimate a channel between any base station antenna of the communication system and any user mobile terminal, and is characterized in that it includes the following steps:

[0038] Step 1: Obtain channel estimation parameters from the communication system. The channel estimation parameters include: received signal matrix Y, pilot matrix IRS reflection coefficient matrix And set the channel estimation control parameters, including: maximum number of iterations I t , termination threshold ξ t The initial values ​​of a, b, c, d, e, and f are all 10. -10 ;

[0039] Step 2: Initialize the channel grouping coefficient ω k, iteration count i t and the channel vector q m ;

[0040] Step 3: Use the variational Bayesian expectation maximization algorithm to iteratively update the parameters β and μ m s, lnπ k s, ln(1-π k )s, γ and φ m,k s; let iteration count i t =i t +1, channel vector q m (i t )=μ m ;in,

[0041] Channel vector q m The updated posterior distribution is μ m Represents the mean vector matrix of the posterior distribution of the channel vector after update; Σ m Represents the covariance matrix of the posterior distribution after the channel vector is updated; the channel vector q m Obey complex Gaussian distribution α m represents the precision vector of the channel vector, which is distributed as follows: If the mth subplane is divided into the kth group, then The precision vector representing the common sparsity within the kth group; β = σ -2 Represents the noise accuracy, which obeys the gamma distribution, that is, β~Gamma(a,b); represents the transmitted pilot vector, where N p represents the number of pilots, is randomly generated from [0,2π); express Updated posterior mean; <lnπ k > and <ln(1-π k )> respectively represent π k After the update, lnπ k and ln(1-π k ), π k represents the scaling factor of channel grouping; represents the updated posterior mean of γ, and γ represents π k The parameters of the Beta distribution are obeyed, and γ obeys the Gamma distribution, that is, γ|e,f~Gamma(e,f); express <z m,k =1>, is the updated z m,k =1, which means the probability that the m-th sub-plane is assigned to the k-th group after this iteration.

[0042] Step 4: If the iteration count i t <Maximum number of iterations I t and Then return to step 3; if the iteration count i t ≥ Maximum number of iterations I t or Then the iteration is terminated and step 5 is executed; the Err(i t ) represents the i-th t The error of the channel vector estimation value at the iteration; the Err(i t ) represents the i-th t The error of the channel vector estimate at the iteration;

[0043] Step 5: output estimated parameters, the estimated parameters include: channel vector q m The estimated value and channel grouping value z m .

[0044] In step 1 of the above method, the received signal matrix Y is calculated by the following formula, including:

[0045]

[0046] in, For the joint collection The signal receiving matrix, y i represents the received signal of the i-th OFDM symbol, and I0 represents the number of OFDM symbols; represents the pilot matrix; represents the channel matrix; Represents a joint set The IRS reflection coefficient matrix, represents the reflection coefficient of the received i-th OFDM symbol, θ i =[θ 1,i ,…,θ M,i ] T ,θ m,i represents the reflection coefficient of the mth sub-plane of the i-th OFDM; represents the additive white Gaussian noise matrix, where σ 2 represents the square of the noise, Indicates dimension N p ×N p The identity matrix, N p Indicates the number of pilots transmitted by the transmitting antenna;

[0047] In step 1 of the above method, the pilot matrix Calculated by the following formula:

[0048]

[0049] Wherein, S=diag(s) represents the frequency domain OFDM symbol; is a submatrix of the DFT matrix F P The first L columns, F P represents the set of rows in F corresponding to the index set P;

[0050] In the step 1, the IRS reflection coefficient matrix Calculated by the following formula:

[0051]

[0052] in, represents the reflection coefficient of the received i-th OFDM symbol, θ i =[θ 1,i ,…,θ M,i ] T ,θ m,i Represents the reflection coefficient of the mth sub-plane when transmitting the i-th OFDM, i = 1, 2, …, I0.

[0053] In step 2 of the above method, the initial channel grouping coefficient ω k Calculated by the following formula:

[0054]

[0055]

[0056] Among them, π k and π l are all random variables that obey the Beta distribution with parameters 1 and γ, that is, π k ~Beta(1,γ),π l ~Beta(1,γ); initialize iteration count i t =0, initialize the channel vector to q m =0 L×1 .

[0057] In step 3 of the above method, the parameters β and μ are updated in sequence by using the variational Bayesian expectation maximization algorithm in the following way: m s, lnπ k s, ln(1-π k )s, γ and φ m,k s, let iteration count i t =i t +1, channel vector q m (i t )=μm ,include:

[0058] (1)

[0059] Where U = [μ1,μ2,...,μ M+1 ], express The mth row of ;

[0060] (2) in

[0061] (3) as well as in express The logarithmic expectation of , Ψ represents the digamma function,

[0062] (4) <lnπ k > = Ψ(τ 1,k )-Ψ(τ 1,k +τ 2,k ), <ln(1-π k )>=Ψ(τ 2,k )-Ψ(τ 1,k +τ 2,k ),in,

[0063] (5)

[0064] (6) in,

[0065]

[0066] In step 4 of the above method, the Err(i t ) is determined as follows, including:

[0067] Among them, q m (i t ) represents the i-th t At the iteration, the estimated value of the channel vector, ||·||2 represents the L2 norm of the vector.

[0068] In step 5 of the above method, when the iteration ends, the estimated value q of the channel vector m =μ m , grouping results

[0069] The embodiment of the present invention further provides a processing device, including:

[0070] at least one memory for storing one or more programs;

[0071] At least one processor can execute one or more programs stored in the memory, and when the one or more programs are executed by the processor, the processor can implement the above method.

[0072] An embodiment of the present invention further provides a readable storage medium storing a computer program, wherein the computer program can implement the above method when executed by a processor.

[0073] In summary, compared with the existing channel estimation method, the channel estimation method of the embodiment of the present invention fully exploits the sparse characteristics of the channel of the IRS-assisted OFDM communication system by using the sparse Bayesian learning method, and can further exploit the common sparsity between channels by using the potential grouping information between channels corresponding to the sub-planes of the smart reflection surface, thereby improving the channel estimation accuracy and grouping the channels. In the actual IRS-assisted OFDM communication system, it has practical application value.

[0074] In order to more clearly demonstrate the technical solution and technical effects provided by the present invention, the channel estimation method of the intelligent reflecting surface assisted communication system provided by the embodiment of the present invention is described in detail with reference to a specific embodiment below.

[0075] Example 1

[0076] like Figure 1 As shown, this embodiment provides a channel estimation method for an intelligent reflector-assisted communication system, which can improve the accuracy of channel estimation and group channels. To clearly illustrate the method, the channel estimation method of the present invention is described below according to the basic IRS-assisted OFDM communication system model, the hierarchical prior model, the SBL framework for channel estimation and sub-plane grouping, and the simulation performance analysis:

[0077] (1) Basic IRS-assisted OFDM communication system model:

[0078] The channel estimation method of the present invention is used in an IRS-assisted OFDM communication system, where the IRS is deployed between a base station and multiple mobile users, and the IRS consists of M sub-planes. Each antenna of the base station transmits a pilot signal to sample different sub-carriers, so the channels between different antennas and different mobile user terminals are independent of each other and can be estimated in parallel. Therefore, only the channel between one antenna and one user mobile terminal is considered. The channel between the base station antenna and the user mobile terminal is composed of a direct channel and a cascade channel superimposed on each other:

[0079] h=d+Qθ (0.1)

[0080] Where d represents the direct channel, represents the cascade channel matrix, q m represents the cascade channel corresponding to the mth sub-plane between the base station antenna and the user terminal, L represents the maximum delay spread of the cascade channel and the direct channel, θ m Represents the reflection coefficient of the mth sub-plane, with an amplitude of 1 and a phase randomly distributed on [0,2π).

[0081] The receiving end receives the received signal y of the i-th OFDM symbol i It can be written as:

[0082]

[0083] Represents the frequency domain OFDM symbol, S is a diagonal matrix with N diagonal elements. p pilot subcarriers; is the first L columns of a DFT matrix; v i represents additive white Gaussian noise; represents the channel matrix to be estimated;

[0084] Combine I0 OFDM symbols together to get the received signal matrix Y:

[0085]

[0086] in

[0087] (2) Hierarchical prior model:

[0088] The likelihood function of the received signal matrix Y can be written as:

[0089]

[0090] Where β = σ -2 It represents the noise accuracy of the received signal V. Since the gamma distribution is conjugate with the complex Gaussian distribution, in order to perform subsequent expected calculations, their multiplication still results in a complex Gaussian distribution. The specific distribution value can be obtained by the coefficient after multiplication, and β is further modeled as a gamma distribution, that is:

[0091] β~Gamma(a,b) (1.2)

[0092] Where a = b = 10 -10 , a and b are set to such a small positive number in order to make their initial values ​​randomly generated in a larger range, and β is modeled as a gamma distribution because it is conjugate with the Gaussian distribution.

[0093] The cascaded channel q mModeled as a Gaussian distribution:

[0094]

[0095] Among them, Λ m =diag(α m ) represents the precision matrix; α m =[α m,1 …α m,L ] T Assume α m Gamma distribution:

[0096]

[0097] Similarly, c = d = 10 -10 .

[0098] Since the distances between the sub-planes of IRS are very close, it is assumed that the sparse patterns of the cascaded channels corresponding to different sub-planes are the same, and the channel vectors with the same sparse pattern are considered as a group. The Dirichlet process (DP) has the property of automatically clustering according to the given information. Therefore, in order to achieve adaptive grouping of channel vectors, the precision vector α of the channel vector is given m Apply a DP prior, that is, Among them, G is a probability distribution randomly sampled from the DP process, represents the Dirac function, π k s is a random value from Beta(1,γ). Assume that q in group k m share a precision vector, then

[0099]

[0100] in, represents the shared precision vector, the basis measurement G0 is chosen to be gamma distributed, and Sampling from G0:

[0101]

[0102] To further infer γ, assume that γ follows a gamma distribution:

[0103] γ|e,f~Gamma(e,f) (1.6)

[0104] Similarly, e = f = 10 -10 .

[0105] The channel vector can be written as:

[0106]

[0107] Among them, z m is an indicator vector used to indicate grouping; 1[z m = k] represents z m The mth element of is 0, and the other elements are 1.

[0108] (3) SBL framework for channel estimation and sub-plane grouping

[0109] Assume that the latent variable parameter set to be estimated is Ω = {β, γ, π, Q, Z, γ}, where π = [π1, ..., π K ] T , These variables can be obtained by calculating the maximum a posteriori estimate p(Ω|Y), and the posterior probability can be written as:

[0110]

[0111] Among them, according to the prior hierarchical model in the previous section, the joint probability can be written as:

[0112]

[0113] However, formula (3.1) involves multiple integrals, and it is difficult to obtain a closed-form expression of p(Ω|Y). Therefore, the present invention uses the VBEM algorithm to calculate the optimal approximate posterior q(Ω) of Ω, which can be obtained by minimizing its KL divergence with the true posterior p(Ω|Y):

[0114]

[0115] According to the mean field theory, q(Ω) can be approximately decomposed into:

[0116]

[0117] Under the constraints of formula (2.4), the best approximate estimate of q(Ω) can be written as q * (Ω k )

[0118]

[0119] Among them, ∝ means that the two sides of the equation are equal when they differ by a constant; Ω k represents the kth element of Ω, Indicates about expectations, Indicates that Ω is removed from Ω k The posterior set.

[0120] According to formula (2.5), at each iteration, only one variable is updated and the other variables are kept constant. This process is repeated until these variables converge, and then the posterior mean of each variable is regarded as their estimate. The specific steps are as follows:

[0121] Step 1: Obtain channel estimation parameters from the communication system, including: received signal matrix Y, pilot matrix and the IRS reflection coefficient matrix And set the maximum number of iterations I t , termination threshold ξ t and the parameters of the distribution {a,b,c,d,e,f};

[0122] Step 2: Initialize channel grouping coefficient ω k , iteration count i t and the channel vector q m ;

[0123] Furthermore, the initial channel grouping coefficient ω in the above step 2 is k Satisfy the following formula:

[0124]

[0125]

[0126] Among them, π k ,π l Obeys a Beta distribution with parameters 1 and γ, that is, π k , π l ~Beta(l,γ); initialize iteration count i t =0, channel vector q m =0 L×1 ;

[0127] Step 3: Update the parameters β, μ in turn through the variational Bayesian expectation maximization (VBEM) algorithm m s, s, lnπ k s, ln(1-π k )s, γ and φ m,k s, let iteration count i t =i t +1, channel vector q m (i t )=μ m :

[0128] (1)

[0129] Where U = [μ1,μ2,...,μ M+1 ], express The mth row of ;

[0130] (2) in

[0131] (3) as well as in express The logarithmic expectation of , Ψ represents the digamma function,

[0132] (4) <lnπ k > = Ψ(τ 1,k )-Ψ(τ 1,k +τ 2,k ), <ln(1-π k )>=Ψ(τ 2,k )-Ψ(τ 1,k +τ 2,k ),

[0133] in

[0134] (5)

[0135] (6) in,

[0136] μ m,l is μ m The lth element of is Σ m The lth diagonal element of ;

[0137] Step 4: Iteration termination condition judgment, if the iteration count i t <I t and Then return to step 4; if the iteration count i t ≥I t or Then terminate the iteration;

[0138] in, Where ||·||2 represents the L2 norm of the vector;

[0139] Step 5: Output estimated parameters, which include: channel vector q m =μ m The estimated values ​​and grouping results of

[0140] (4) Simulation performance analysis:

[0141] Figure 3The normalized mean square error performance diagram of the channel estimation method of this embodiment as the number of iterations changes under different signal-to-noise ratios is given. It can be seen that under all signal-to-noise ratio conditions, the performance tends to be stable, verifying the convergence of the above channel estimation method.

[0142] Figure 4 and Figure 5 The result diagrams of the normalized mean square error of the channel estimation method of this embodiment and other comparison algorithms as the number of pilots and the number of OFDM symbols change under the same parameter conditions are respectively given. It can be seen that the performance of the above channel estimation method is significantly improved compared with other algorithms.

[0143] Figure 6 and Figure 7 The results of the normalized mean square error of the channel estimation method of this embodiment and other comparison algorithms changing with the signal-to-noise ratio under the same parameter conditions are respectively given. Figure 6 The actual number of groups K * =3, Figure 7 The actual number of groups K * =15 (i.e. no grouping information), we can see that Figure 6 The above channel estimation method has significantly improved performance compared with other comparison algorithms; Figure 7 Even if there is no grouping information in the channel, the performance of the above channel estimation method will not suffer any performance loss compared to the ordinary SBL method.

[0144] Figure 8 The channel estimation method of this embodiment is given in the real group number K * = 3. It can be seen that a total of 4 sparse modes (4 groups) are obtained, including three groups of cascade channels and one group of direct channels.

[0145] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above embodiments can be implemented by software, or by means of software plus necessary general hardware platforms. Based on such understanding, the technical solutions of the above embodiments can be embodied in the form of software products, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0146] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed in the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims. The information disclosed in the background technology section of this article is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as an admission or in any form that the information constitutes prior art known to those skilled in the art.

Claims

1. A channel estimation method for an intelligent reflector-assisted communication system, used in an intelligent reflector-assisted communication system, for estimating a channel between any base station antenna and any user mobile terminal of the communication system, characterized in that: The following steps are involved: Step 1: Obtain channel estimation parameters from the communication system. The channel estimation parameters include: received signal matrix Y, pilot matrix IRS reflection coefficient matrix And set the channel estimation control parameters, including: maximum number of iterations I t , termination threshold ξ t The initial values ​​of a, b, c, d, e, and f are all 10. -10 ; Step 2: Initialize the channel grouping coefficient ω k , iteration count i t and the channel vector q m ; The initial channel grouping coefficient ω k Calculated by the following formula: Among them, π k and π l are all random variables that obey the Beta distribution with parameters 1 and γ, that is, π k ~Beta(1,γ),π l ~Beta(1,γ); Initialize iteration count i t =0, initialize the channel vector to q m =0 L×1 ; Step 3: Use the variational Bayesian expectation maximization algorithm to iteratively update the parameters β and μ m s, lnπ k s, ln(1-π k )s, γ and φ m,k s; let iteration count i t =i t +1, channel vector q m (i t )=μ m ;in, Channel vector q m The updated posterior distribution is μ m Represents the mean vector matrix of the posterior distribution of the channel vector after update; Σ m Represents the covariance matrix of the posterior distribution after the channel vector is updated; the channel vector q m Obey complex Gaussian distribution α m represents the precision vector of the channel vector, which is distributed as follows: If the mth subplane is divided into the kth group, then The precision vector representing the common sparsity within the kth group; β = σ -2 Represents the noise accuracy, which obeys the gamma distribution, that is, β~Gamma(a,b); represents the transmitted pilot vector, where N p represents the number of pilots, is randomly generated from [0,2π); express Updated posterior mean; <lnπ k > and <ln(1-π k )> respectively represent π k After the update, lnπ k and ln(1-π k ), π k represents the scaling factor of channel grouping; represents the updated posterior mean of γ, and γ represents π k The parameters of the Beta distribution are obeyed, and γ obeys the Gamma distribution, that is, γ|e,f~Gamma(e,f); express <z m,k =1>, is the updated z m,k =1, which means the probability that the m-th sub-plane is assigned to the k-th group after this iteration. Step 4: If the iteration count i t <Maximum number of iterations I t and Return to step 3; if the iteration count i t ≥ Maximum number of iterations I t or Then the iteration is terminated and step 5 is executed; the Err(i t ) represents the i-th t The error of the channel vector estimation value at the iteration; the Err(i t ) is determined as follows, including: Among them, q m (i t ) represents the i-th t At the iteration, the estimated value of the channel vector, ||·||2 represents the L2 norm of the vector; Step 5: output estimated parameters, the estimated parameters include: channel vector q m The estimated value and channel grouping value z m .

2. The channel estimation method for a smart reflector-assisted communication system according to claim 1, characterized in that: In the step 1, the received signal matrix Y is calculated by the following formula, including: in, For the joint collection The signal receiving matrix, y i represents the received signal of the i-th OFDM symbol, and I0 represents the number of OFDM symbols; represents the pilot matrix; represents the channel matrix to be estimated; Represents a joint set The IRS reflection coefficient matrix, represents the reflection coefficient of the received i-th OFDM symbol, θ i =[θ 1,i ,…,θ M,i ],θ m,i represents the reflection coefficient of the mth sub-plane of the i-th OFDM; represents the additive white Gaussian noise matrix, where i=1,2,…,I0,σ 2 represents the square of the noise, Indicates dimension N P ×N P The identity matrix of In the step 1, the pilot matrix Calculated by the following formula: Wherein, S=diag(s) represents the frequency domain OFDM symbol; is a submatrix of the DFT matrix F P The first L columns, F P represents the set of rows in F corresponding to the index set P; In the step 1, the IRS reflection coefficient matrix Calculated by the following formula: in, represents the reflection coefficient of the received i-th OFDM symbol, θ i =[θ 1,i ,…,θ M,i ] T ,θ m,i Represents the reflection coefficient of the mth sub-plane when transmitting the i-th OFDM, i = 1, 2, …, I0.

3. The channel estimation method for a smart reflector-assisted communication system according to claim 1 or 2, characterized in that: In step 3, the parameters β, μ are updated in sequence by using the variational Bayesian expectation maximization algorithm in the following manner: m s, s, lnπ k s, ln(1-π k )s, γ and φ m,k s, let iteration count i t =i t +1, channel vector q m (i t )=μ m ,include: (1) Where U = [μ1,μ2,…,μ M+1 ], express The mth row of ; (2) in (3) as well as in express The logarithmic expectation of , Ψ represents the digamma function, (4) <lnπ k >=Ψ(τ 1,k )-Ψ(τ 1,k +t 2,k ), <ln(1-π k )>=Ψ(τ 2,k )-Ψ(τ 1,k +t 2,k ), among them, (5) (6) in, 4. The channel estimation method for a smart reflector-assisted communication system according to claim 1 or 2, characterized in that: In step 5, when the iteration ends, the estimated value of the channel vector q m =μ m , grouping results 5. A processing device, characterized in that: include: at least one memory for storing one or more programs; At least one processor is capable of executing one or more programs stored in the memory, and when the one or more programs are executed by the processor, the processor is capable of implementing the method described in any one of claims 1 to 4.

6. A readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 can be implemented.

Images