A hybrid field channel estimation method for very large-scale MIMO systems

Abstract

The present invention discloses a mixed-field channel estimation method for ultra-large-scale MIMO systems. The sparse transformation matrices of two channels are merged into one sparse transformation matrix and the mixed-field channel is sparsely represented; the mixed-field channel estimation problem is converted into a sparse estimation problem, avoiding the problem of mutual interference between the two channels during the two sparse estimation processes. During the estimation process, each time the support set of the mixed-field sparse channel is updated, the far-field sparse channel and the near-field sparse channel are jointly estimated based on the current support set, and the residual is calculated based on the current estimated value for the next update of the support set. This overcomes the problem that existing solutions cannot solve the mutual interference between the far-field sparse channel and the near-field sparse channel during the support set selection process. Simulation results show that the scheme has higher estimation accuracy.

Description

Technical Field

[0001] The present invention belongs to the field of information and communication engineering technology, and relates to ultra-large-scale MIMO technology in wireless communication systems, and specifically to a mixed-field channel estimation scheme based on compressed sensing theory in ultra-large-scale MIMO systems. Background Art

[0002] Ultra-large-scale MIMO systems, a potential key technology for next-generation communications systems, offer dozens of times higher spectral efficiency and communication rates than massive MIMO. High-precision channel estimation algorithms are essential for realizing the full potential of ultra-large-scale MIMO. In existing massive MIMO systems, due to their massive antenna arrays, the use of traditional least squares and minimum mean square error algorithms incurs significant pilot overhead and increased computational complexity. Channel estimation schemes based on compressed sensing theory address these shortcomings. They leverage the sparsity of the channel to achieve channel estimation with lower computational complexity and shorter pilot lengths than traditional schemes, effectively reducing pilot overhead. Due to the small aperture of the antenna array at the base station end of a massive MIMO system, the near-field region is negligible. Communication between users and the base station occurs in the base station's far-field region. Leveraging the angular sparsity of the far-field channel, sparse channel estimation based on compressed sensing achieves both low overhead and high accuracy. Because XL-MIMO is equipped with an ultra-large-scale antenna array at the base station, the number of antennas increases dramatically compared to massive MIMO, and the channel dimensionality also increases with the number of antennas. Channel estimation faces higher computational complexity and pilot overhead. Therefore, channel estimation based on compressed sensing theory has greater practical value in ultra-large-scale MIMO systems. The near-field region of electromagnetic radiation from ultra-large-scale antenna arrays cannot be ignored, and channel estimation must take into account the near-field channel and its sparsity. In actual communication environments, scatterers distributed in the far-field and near-field regions cause multipath channels to contain both far-field and near-field channels. If channel estimation is performed on these channels as a single type, the channel estimation accuracy is low and the estimation result does not adequately reflect the channel situation. Existing mixed-field channel estimation methods based on compressed sensing estimate the far-field and near-field channels in the mixed-field channel separately, and the estimation is sequential. This can interfere with the selection of support sets for the far-field sparse and near-field sparse channels during the estimation process. Summary of the Invention

[0003] In response to the deficiencies in the prior art, the present invention provides a mixed field channel estimation method for ultra-large-scale MIMO systems. The sparse transformation matrices of the two channels are merged into a sparse transformation matrix and the mixed field channel is sparsely represented; the mixed field channel estimation problem is converted into a sparse estimation problem, avoiding the problem of mutual interference between the two channels during the two sparse estimation processes. During the estimation process, each time the support set of the mixed field sparse channel is updated, the far-field sparse channel and the near-field sparse channel are jointly estimated based on the current support set, and the residual is calculated based on the current estimated value for the next update of the support set. This overcomes the problem that the existing scheme cannot solve the mutual interference between the far-field sparse channel and the near-field sparse channel during the support set selection process. Simulation results show that this scheme has higher estimation accuracy. The specific steps of the technical scheme adopted by the present invention to solve its technical problems are as follows:

[0004] A mixed-field channel estimation method for a very large-scale MIMO system comprises the following steps:

[0005] Step 1: Determine the signal transmission model and mixed field channel model of the ultra-large-scale MIMO system and describe the problem;

[0006] Model the downlink communication process between users and base stations in a very large-scale MIMO system. Determine the near-field channel model, the far-field channel model, and the mixed-field channel model consisting of the two channels.

[0007] Step 2: Establish a compressed sensing model for mixed field channel estimation.

[0008] The near-field sparse transformation matrix and the far-field sparse transformation matrix are combined so that the mixed-field channel can be sparsely represented as a single sparse signal, and the mixed-field channel estimation is transformed into a sparse signal estimation problem.

[0009] Step 3: Estimate the mixed field sparse channel based on the compressed sensing model of the mixed field channel estimation, and use the mixed field sparse channel to sparsely reconstruct the mixed field channel.

[0010] Furthermore, the signal transmission model of the ultra-large-scale MIMO system is as follows:

[0011] Consider the communication process between a base station equipped with N uniform linear antenna arrays and a single antenna user in a very large-scale MIMO system, and perform downlink channel estimation. Assume that the base station sends a pilot symbol p in the mth time slot. m ∈C 1×N , then in the mth time slot, the pilot signal received by the user is in The mean is 0 and the variance is σ 2 The channel is represented by a column vector h with a dimension of N.m ∈C N×1 Denote. Define M as the pilot length, m∈1,2,…M. Assuming that the channel remains unchanged during the pilot transmission phase, the signal received by the user end is:

[0012]

[0013] definition is the pilot matrix, the above formula can be further expressed as:

[0014] y=Ph+n (2)

[0015] where y=[y1,y2,…,y M ] H , is the signal received by the receiving end during the entire pilot transmission phase; n=[n1,n2,…,n M ] H , is the noise at the receiving end. The present invention estimates the mixed field channel h based on the known y and pilot matrix P.

[0016] Furthermore, the near-field channel model, the far-field channel model, and the mixed-field channel model composed of the two channels are determined as follows:

[0017] Line-of-sight channel between the nth antenna at the base station and the user [h] n Can be expressed as:

[0018]

[0019] Where α is the path attenuation, and it is assumed that the path attenuation is the same for all antennas; f c is the carrier frequency; r (n) is the distance between the nth antenna of the base station and the user; c is the speed of light. Therefore, the line-of-sight channel h between the user and the ULA with N antennas LOS Can be expressed as:

[0020]

[0021] In wireless communication systems, electromagnetic radiation is measured by the Rayleigh Distance It is divided into far field area and near field area, where D is the aperture of the antenna array and λ is the wavelength of the electromagnetic wave. When , the user is in the far field. The electromagnetic wave front is modeled using a plane wave, with an arrival angle / departure angle of θ. The path difference between the electromagnetic propagation of two adjacent antennas is dcosθ, and the path difference between the nth antenna and the first antenna is (n-1)dcosθ. (1) is the distance between the user and the reference antenna, far-field line-of-sight channel It can be further expressed as:

[0022]

[0023] in:

[0024]

[0025] Far-field multipath channel h far-field can be expressed as:

[0026]

[0027] Where L is the number of multipaths, α l is the gain of the lth path. The far-field channel has sparseness in the angle domain, which can be sparsely represented in the angle domain, that is:

[0028] h far-field =Fh a (8)

[0029] Where F is the discrete Fourier matrix, h a is the angle domain sparse channel.

[0030] When the distance between the user and the base station When , the user is in the near field area. The plane wave model cannot accurately describe the near field channel, and spherical wave modeling should be used. The distance between the user end and the first antenna of the array is r, the arrival angle / departure angle is θ, and the near field line-of-sight channel Can be expressed as:

[0031]

[0032] in is the array response vector of the near-field channel. The distance between the user and the nth antenna of the base station is:

[0033]

[0034] Near-field multipath channel h near-field Can be expressed as:

[0035]

[0036] The near-field channel is sparse in the polar region, that is:

[0037] h near-field =Wh p (12)

[0038] where W∈C N×S is the polar domain transformation matrix, S nis the number of distance samples in different directions. p is the near-field multipath channel h near-field Sparse representation in the polar domain. W is composed of the near-field array response vectors with different angle and distance parameters, as follows:

[0039]

[0040] In ultra-large-scale MIMO systems, considering the multipath effects caused by scatterers distributed in both the far field and the near field, the actual mixed-field multipath channel can be modeled as:

[0041]

[0042] Where L = L f +L n , L is the total number of multipaths in the mixed field channel, L f and L n Represent the multipath numbers of the far-field channel and the near-field channel respectively. At the same time, the l-th far-field path gain and near-field path gain are and

[0043] Furthermore, the specific method of step 2 is as follows:

[0044] The compressed sensing model for mixed field channel estimation is established by equations (2), (8), (12), and (14):

[0045] y=PΨ h h+n (15)

[0046] Where Ψ=[W,F], which is the sparse transformation matrix of the mixed field channel. Therefore, Ψ is the sparse transformation matrix of the far field channel h f and the near-field channel h n The sparse representation of is sufficient. That is, h n =Ψh' p ,h f =Ψh' a , where h' a ,h' p ∈C (N+S)×1 ,h' a is the sparse representation of the far-field channel under the sparse transformation matrix of the mixed-field channel, h' p is the sparse representation of the near-field channel under the sparse transformation matrix of the hybrid channel,

[0047] Furthermore, the specific method of step 3 is as follows:

[0048] Under the compressed sensing model of formula (15), for the mixed field sparse channel h hThe sparse reconstruction of is equivalent to the mixed-field channel h. In the mixed-field channel estimation, the column with the greatest correlation between the residual and the perception matrix column is selected based on the orthogonal matching pursuit algorithm and added to the index set. The support set of the mixed-field sparse channel is determined from the index set, and the coefficients are calculated. The non-zero coefficients of the near-field sparse channel and the non-zero coefficients of the far-field coefficient channel are jointly determined, and the residual is updated simultaneously.

[0049] Furthermore, the specific mixed field channel estimation algorithm is as follows:

[0050] Step 1: Construct the mixed field sparse channel perception matrix A = P[W,F] = PΨ; initialize the residual r (0) =y; initialize the index set Calculate the near-field channel perception matrix A n =PW; far-field channel sensing matrix A f =PF; Initialize the number of loops i=0.

[0051] Step 2: Calculate the residual r (i) The inner product of each column of the perception matrix A. And select the column corresponding to the column vector in A that makes the inner product the largest as the index, recorded as n * .

[0052] Step 3: Set index n * Add to the index set Λ.

[0053] Step 4: Classify the index set Λ: If the value of the element in the index set Λ is less than or equal to S, it is classified into the index set Λ n , if the value of the element in the index set Λ is greater than S, it is divided into the index set Λ f .

[0054] Step 5: Utilize A n and index set Λ n Construct index matrix A n (:,Λ n ), by Λ n Select h p The support set sup(h p ), the support set coefficient is calculated using the least squares method

[0055] Step 6: Update intermediate residuals

[0056] Step 7: Utilize A f and index set Λ f Construct index matrix A f (:,Λ f ), by Λ f Select ha The support set sup(h a ), the support set coefficient is calculated using the least squares method

[0057] Step 8: Update the residuals

[0058] Step 9: Repeat steps 2 to 8 based on the updated residual until the set number of cycles L is reached, i.e., i = L-1.

[0059] Step 10: Using the last loop calculation and Sparse reconstruction of mixed field channels:

[0060]

[0061] Since in actual mixed field communication scenarios, it is easier to obtain the total number of multipath channels than to obtain the number of multipath channels of far-field channels and near-field channels respectively, the solution proposed in this invention avoids using the a priori condition of the number of multipath channels of near-field channels and far-field channels, and instead uses the total number of multipath channels as the number of algorithm iterations to indirectly reconstruct the mixed field channel h, which is more practical. When the algorithm determines the mixed field sparse channel h h When the support set of W is determined, it is equivalent to determining the sparse channel h in the extreme domain under W. p and the sparse channel h in the angular domain under F a The support set of .

[0062] The beneficial effects of the present invention are as follows:

[0063] This paper addresses the problem that existing methods cannot accurately determine the sparsity of sparse channels due to double reconstruction. By improving the mixed-field channel sparse recovery model and designing a mixed-field channel joint estimation scheme, this method jointly estimates the far-field sparse channel and the near-field sparse channel in the current iteration in each iteration and updates the residual based on the current far-field and near-field channel estimates. This improves estimation accuracy without significantly increasing complexity. It also avoids the need for prior knowledge of the number of far-field and near-field paths in the mixed-field channel, making it more practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 is the far-field channel model under uniform linear array.

[0065] Figure 2 It is the near-field channel model under uniform linear array.

[0066] Figure 3 This is a flow chart of the mixed-field channel estimation algorithm proposed by the present invention.

[0067] Figure 4 N = 512, M = 256, and the number of far-field multipaths is equal to the number of near-field multipaths (L f =L n =5), the normalized mean square error simulation diagram under different signal-to-noise ratios.

[0068] Figure 5 N = 256, M = 128, and the number of far-field multipaths is equal to the number of near-field multipaths (L f =L n =5), the normalized mean square error simulation diagram under different signal-to-noise ratios.

[0069] Figure 6 The normalized mean square error simulation diagram under different far-field and near-field multipath numbers when N=512, M=256, and signal-to-noise ratio SNR=5. f =kL, the number of multipath paths in the near-field channel L n =(1-k)L.

[0070] Figure 7 The normalized mean square error simulation diagram under different far-field and near-field multipath numbers when N=512, M=128, and signal-to-noise ratio SNR=5. f =kL, the number of multipath paths in the near-field channel L n =(1-k)L. DETAILED DESCRIPTION

[0071] The technical solution of the present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0072] A mixed-field channel estimation method for a very large-scale MIMO system comprises the following steps:

[0073] Step 1: Determine the signal transmission model and mixed field channel model of the ultra-large-scale MIMO system and describe the problem;

[0074] The downlink communication process between users and base stations in a very large-scale MIMO system is modeled. Consider the communication process between a base station equipped with N uniform linear antenna arrays and a single-antenna user in a very large-scale MIMO system, and perform downlink channel estimation. Assume that the base station sends a pilot symbol p in the mth time slot. m ∈C 1×N , then in the mth time slot, the pilot signal received by the user is in The mean is 0 and the variance is σ 2 The channel is represented by a column vector h with a dimension of N. m ∈C N×1Denote. Define M as the pilot length, m∈1,2,…M. Assuming that the channel remains unchanged during the pilot transmission phase, the signal received by the user end is:

[0075]

[0076] definition is the pilot matrix, the above formula can be further expressed as:

[0077] y=Ph+n (2)

[0078] where y=[y1,y2,…,y M ] H , is the signal received by the receiving end during the entire pilot transmission phase; n=[n1,n2,…,n M ] H , is the noise at the receiving end. The present invention estimates the mixed field channel h based on the known y and pilot matrix P.

[0079] The near-field channel model, the far-field channel model, and the mixed-field channel model consisting of the two channels are given below.

[0080] Line-of-sight channel between the nth antenna at the base station and the user [h] n Can be expressed as:

[0081]

[0082] Where α is the path attenuation, and it is assumed that the path attenuation is the same for all antennas; f c is the carrier frequency; r (n) is the distance between the nth antenna of the base station and the user; c is the speed of light. Therefore, the line-of-sight channel h between the user and the ULA with N antennas LOS Can be expressed as:

[0083]

[0084] In wireless communication systems, electromagnetic radiation is measured by the Rayleigh Distance It is divided into far field area and near field area, where D is the aperture of the antenna array and λ is the wavelength of the electromagnetic wave. , the user is in the far field area. Figure 1 For the far-field channel model of a uniform linear array, electromagnetic propagation is modeled as a plane wave that arrives at and leaves each antenna in the array at the same angle θ, where d is the antenna spacing. The electromagnetic wavefront is modeled as a plane wave with an arrival / departure angle of θ. The path difference between two adjacent antennas is dcosθ, and the path difference between the nth antenna and the first antenna is (n-1)dcosθ. (1)is the distance between the user and the reference antenna, far-field line-of-sight channel It can be further expressed as:

[0085]

[0086] in:

[0087]

[0088] Far-field multipath channel h far-fileld can be expressed as:

[0089]

[0090] Where L is the number of multipaths, α l is the gain of the lth path. The far-field channel has sparseness in the angle domain, which can be sparsely represented in the angle domain, that is:

[0091] h far-field =Fh a (8)

[0092] Where F is the discrete Fourier matrix, h a is the angle domain sparse channel.

[0093] When the distance between the user and the base station , the user is in the near field area. Figure 2 The near-field channel model of a uniform linear array is shown in Figure 2. Electromagnetic propagation is modeled as a spherical wave that arrives at / leaves each antenna in the array at different angles. (N) is the distance between the user / scatterer and the Nth antenna in the array. The plane wave model cannot accurately describe the near-field channel, and spherical wave modeling should be used. The distance between the user end and the first antenna in the array is r, the arrival angle / departure angle is θ, and the near-field line-of-sight channel Can be expressed as:

[0094]

[0095] in is the array response vector of the near-field channel. The distance between the user and the nth antenna of the base station is:

[0096]

[0097] Near-field multipath channel h near-field Can be expressed as:

[0098]

[0099] The near-field channel is sparse in the polar region, that is:

[0100] h near-field =Wh p (12)

[0101] where W∈C N×S is the polar domain transformation matrix, S n is the number of distance samples in different directions. p is the near-field multipath channel h near-field Sparse representation in the polar domain. W is composed of the near-field array response vectors with different angle and distance parameters, as follows:

[0102]

[0103] In ultra-large-scale MIMO systems, considering the multipath effects caused by scatterers distributed in both the far field and the near field, the actual mixed-field multipath channel can be modeled as:

[0104]

[0105] Where L = L f +L n , L is the total number of multipaths in the mixed field channel, L f and L n Represent the multipath numbers of the far-field channel and the near-field channel respectively. At the same time, the l-th far-field path gain and near-field path gain are and

[0106] Step 2: Establish a compressed sensing model for mixed field channel estimation.

[0107] By combining the near-field sparse transformation matrix and the far-field sparse transformation matrix, the mixed-field channel can be sparsely represented as a single sparse signal, and the mixed-field channel estimation is transformed into a sparse signal estimation problem. Specifically, the compressed sensing model for mixed-field channel estimation is established by equations (2), (8), (12), and (14):

[0108] y=PΨh h +n (15)

[0109] Where Ψ=[W,F], which is the sparse transformation matrix of the mixed field channel. Therefore, Ψ is the sparse transformation matrix of the far field channel h f and the near-field channel h n The sparse representation of is sufficient. That is, h n =Ψh' p ,hf=Ψh' a , where h' a ,h' p ∈C (N+S)×1 ,h' ais the sparse representation of the far-field channel under the sparse transformation matrix of the mixed-field channel, h' p is the sparse representation of the near-field channel under the sparse transformation matrix of the hybrid channel,

[0110] Step 3: Estimate the mixed field sparse channel based on the compressed sensing model of the mixed field channel estimation, and use the mixed field sparse channel to sparsely reconstruct the mixed field channel.

[0111] Under the compressed sensing model of formula (15), for the mixed field sparse channel h h The sparse reconstruction is equivalent to the mixed field channel h. In the mixed field channel estimation, based on the orthogonal matching pursuit algorithm (Orthogonal Matching Pursuit), the column with the largest correlation between the residual and the perception matrix column is selected and added to the index set. The support set of the mixed field sparse channel is determined by the index set and in the process of calculating the coefficients, the non-zero coefficients of the near-field sparse channel and the non-zero coefficients of the far-field coefficient channel are jointly determined, and the residual is updated at the same time. Figure 3 As shown, the specific mixed field channel estimation algorithm is as follows:

[0112] Step 1: Construct the mixed field sparse channel perception matrix A = P[W,F] = PΨ; initialize the residual r (0) =y; initialize the index set Calculate the near-field channel perception matrix A n =PW; far-field channel sensing matrix A f=PF; initialization loop number i=0.

[0113] Step 2: Calculate the residual r (i) The inner product of each column of the perception matrix A. And select the column corresponding to the column vector in A that makes the inner product the largest as the index, recorded as n * .

[0114] Step 3: Set index n * Add to the index set Λ.

[0115] Step 4: Classify the index set Λ: If the value of the element in the index set Λ is less than or equal to S, it is classified into the index set Λ n , if the value of the element in the index set Λ is greater than S, it is divided into the index set Λ f .

[0116] Step 5: Utilize A n and index set Λ n Construct index matrix A n (:,Λ n ), by Λ n Select h p The support set sup(h p), the support set coefficient is calculated using the least squares method

[0117] Step 6: Update intermediate residuals

[0118] Step 7: Utilize A f and index set Λ f Construct index matrix A f (:,Λ f ), by Λ f Select h a The support set sup(h a ), the support set coefficient is calculated using the least squares method

[0119] Step 8: Update the residuals

[0120] Step 9: Repeat steps 2 to 8 based on the updated residual until the set number of cycles L is reached, i.e., i = L-1.

[0121] Step 10: Using the last loop calculation and Sparse reconstruction of mixed field channels:

[0122]

[0123] Since in actual mixed field communication scenarios, it is easier to obtain the total number of multipath channels than to obtain the number of multipath channels of far-field channels and near-field channels respectively, the solution proposed in this invention avoids using the a priori condition of the number of multipath channels of near-field channels and far-field channels, and instead uses the total number of multipath channels as the number of algorithm iterations to indirectly reconstruct the mixed field channel h, which is more practical. When the algorithm determines the mixed field sparse channel h h When the support set of W is determined, it is equivalent to determining the sparse channel h in the extreme domain under W. p and the sparse channel h in the angular domain under F a The support set of .

[0124] The complexity analysis of the solution proposed in this invention is as follows: In the Lth iteration, step 2 involves N+S calculations of vector inner products, and its complexity is Steps 5 to 8 involve two pseudo-inverse calculations and matrix multiplications, and their complexity is Perform L iterations, the total complexity is Considering L n ,L f ,L is much smaller than M,N,S, the computational complexity of this scheme is recorded as

[0125] Example

[0126] The present invention sets the required system parameters and algorithm initial values. The carrier frequency is 30 GHz, the number of multipath channels L is 10, and the path gain of the multipath channel obeys a Gaussian distribution with a mean of 0 and a variance of 1, that is, The arrival angle / departure angle of the multipath channel is uniformly distributed between 0 and π, that is, Near-field users / scatterers are evenly distributed 10 to 80 meters from the base station.

[0127] In order to verify the performance of the scheme proposed in this invention under different signal-to-noise ratios, a mixed field channel is first generated according to the above parameters. Among them, the multipath number L of the near-field channel is n and the multipath number L of the far-field channel f All are 5, using random pilot P. Adding additive white Gaussian noise of different powers at the receiving end, the signal received by the receiving end is y = Ph + n. Adjust the noise power, and use the received signal and the known pilot to estimate h when the signal-to-noise ratio (SNR) is 0, 1, 2...10 dB, and record the estimated value. Repeat the simulation 1000 times and calculate the normalized mean square error between the estimated value and the true value

[0128] Figure 4 N = 512, M = 256, and the number of far-field multipaths is equal to the number of near-field multipaths (L f =L n =5), the normalized mean square error simulation diagram under different signal-to-noise ratios. As the signal-to-noise ratio increases, the normalized mean square error of different estimation schemes gradually decreases. The normalized mean square error of the estimation scheme proposed in this invention is better than that of the existing schemes under different signal-to-noise ratios. Referring to the minimum mean square error scheme, the signal-to-noise ratio is improved at low signal-to-noise ratios, and the effect is more significant. When N = 256 and M = 128, Figure 5 As shown, the solution proposed by the present invention is still better than the existing solution.

[0129] In order to verify the performance of the scheme proposed in this invention under different hybrid channel multipath channels, the noise power is fixed so that the signal-to-noise ratio SNR is 5, and the number of multipath channels of different types in the hybrid field channel h and the number of near-field multipath channels L are adjusted. n =(1-k)L, the number of far-field channel multipaths L f= kL. Each time, one near-field path is reduced and one far-field multipath channel is added until the number of far-field multipath channels reaches 10, that is, k = 0, 0.1, 0.2...1. In each case, a channel h is generated, using a random pilot P. After the pilot is transmitted through the channel, the receiver receives y = Ph + n. Using the received signal and the known pilot, h is estimated and the estimated channel value is recorded. Repeat the simulation 1000 times, calculate the normalized mean square error between the estimated value and the true value, and compare the normalized mean square error of different estimation schemes

[0130] Figure 6 The normalized mean square error of the far-field channel and the near-field channel under different multipath numbers when N=512, M=256, and SNR=5dB. The scheme proposed in the present invention has lower normalized mean square error under different multipath numbers of far-field and near-field channels. This shows that the scheme proposed in the present invention has better performance in mixed-field channel estimation and is more adaptable to mixed-field multipath channels in different situations. The simulation under the parameters of N=256 and M=128 is as follows: Figure 7 As shown in the figure, the estimation accuracy of the solution proposed by the present invention is still better than that of the existing solution.

Claims

1. A hybrid field channel estimation method for ultra-large-scale MIMO systems, characterized in that: The steps are as follows: Step 1: Determine the signal transmission model and mixed field channel model of the ultra-large-scale MIMO system and describe the problem; Model the downlink communication process between users and base stations in ultra-large-scale MIMO systems; Determine a near-field channel model, a far-field channel model, and a mixed-field channel model consisting of the two channels; Step 2: Establish a compressed sensing model for mixed field channel estimation; Combining the near-field sparse transformation matrix and the far-field sparse transformation matrix allows the mixed-field channel to be sparsely represented as a single sparse signal, transforming the mixed-field channel estimation into a sparse signal estimation problem. Step 3: Estimate the mixed field sparse channel based on the compressed sensing model of the mixed field channel estimation, and use the mixed field sparse channel to sparsely reconstruct the mixed field channel. The specific method is as follows: Under the compressed sensing model, the mixed field sparse channel h h The sparse reconstruction is equivalent to the mixed field channel h; in the mixed field channel estimation, the column with the largest correlation between the residual and the perception matrix column is selected based on the orthogonal matching pursuit algorithm and added to the index set; The support set of the mixed field sparse channel is determined by the index set, and in the process of calculating the coefficients, the non-zero coefficients of the near-field sparse channel and the non-zero coefficients of the far-field coefficient channel are jointly determined, and the residual is updated at the same time; The specific mixed field channel estimation algorithm is as follows: Step 1: Construct the mixed field sparse channel perception matrix A = P[W,F] = PΨ; initialize the residual r (0) =y; initialize the index set Calculate the near-field channel perception matrix A n =PW; far-field channel sensing matrix A f =PF; Initialization loop number i = 0; Step 2: Calculate the residual r (i) The inner product of each column of the perception matrix A; and select the column corresponding to the column vector in A that makes the inner product the largest as the index, recorded as n * ; Step 3: Set index n * Add to the index set Λ; Step 4: Classify the index set Λ: If the value of the element in the index set Λ is less than or equal to S, it is classified into the index set Λ n , if the value of the element in the index set Λ is greater than S, it is divided into the index set Λ f ; Step 5: Utilize A n and index set Λ n Construct index matrix A n (:,Λ n ), by Λ n Select h p The support set sup(h p ), the support set coefficient is calculated using the least squares method Step 6: Update intermediate residuals Step 7: Utilize A f and index set Λ f Construct index matrix A f (:,Λ f ), by Λ f Select h a The support set sup(h a ), the support set coefficient is calculated using the least squares method Step 8: Update the residuals i=i+1; Step 9: Repeat steps 2 to 8 based on the updated residual until the set number of cycles L is reached, i.e., i = L-1; Step 10: Using the last loop calculation and Sparse reconstruction of mixed field channels:

2. A mixed-field channel estimation method for ultra-large-scale MIMO systems according to claim 1, characterized in that: The signal transmission model of the ultra-large-scale MIMO system is as follows: Consider the communication process between a base station equipped with N uniform linear antenna arrays and a single antenna user in a very large-scale MIMO system, and perform downlink channel estimation. Assume that the base station sends a pilot symbol p in the mth time slot. m ∈C 1×N , then in the mth time slot, the pilot signal received by the user is in The mean is 0 and the variance is σ 2 The channel is represented by a column vector h with a dimension of N. m ∈C M×1 Denotes; define M as the pilot length, m∈1,2,…M. Assuming that the channel remains unchanged during the pilot transmission phase, the signal received by the user end is: definition is the pilot matrix, the above formula can be further expressed as: y=Ph+n (2) where y=[y1,y2,…,y M ] H , is the signal received by the receiving end during the entire pilot transmission phase; n=[n1,n2,…,n M ] H , is the noise at the receiving end; based on the known y and pilot matrix P, estimate the mixed field channel h.

3. A mixed-field channel estimation method for ultra-large-scale MIMO systems according to claim 2, characterized in that: The near-field channel model, the far-field channel model, and the mixed-field channel model consisting of the two channels are determined as follows: Line-of-sight channel between the nth antenna at the base station and the user [h] n Can be expressed as: Where α is the path attenuation, and it is assumed that the path attenuation is the same for all antennas; f c is the carrier frequency; r (n) is the distance between the nth antenna of the base station and the user; c is the speed of light; therefore, the line-of-sight channel h between the user and the ULA of N antennas LOS Can be expressed as: In wireless communication systems, electromagnetic radiation is measured by the Rayleigh distance It is divided into far field area and near field area, where D is the aperture of the antenna array and λ is the wavelength of the electromagnetic wave. When the user is in the far field, the electromagnetic wave front is modeled using a plane wave, and the arrival angle / departure angle is θ; the path difference of electromagnetic propagation between two adjacent antennas is scosθ, and the path difference between the nth antenna and the first antenna is (n-1)dcosθ; r (1) is the distance between the user and the reference antenna, far-field line-of-sight channel It can be further expressed as: in: Far-field multipath channel h far-field can be expressed as: Where L is the number of multipaths, α l is the gain of the lth path; the far-field channel is sparse in the angle domain, which can be sparsely represented in the angle domain, that is: h far-field =Fh a (8) Where F is the discrete Fourier matrix, F∈C N×N ,F :,n =a(θ n ), n=0,1,2,…N-1;h a is the angle domain sparse channel; When the distance between the user and the base station When the user is in the near field area, the plane wave model cannot accurately describe the near field channel, and the spherical wave model should be used. The distance between the user terminal and the first antenna of the array is r, the arrival angle / departure angle is θ, and the near field line-of-sight channel Can be expressed as: in is the array response vector of the near-field channel; the distance between the user and the nth antenna of the base station: Near-field multipath channel h near-field Can be expressed as: The near-field channel is sparse in the polar region, that is: h near-fueld =Wh p (12) where W∈C N×S is the polar domain transformation matrix, S n is the number of distance samples in different directions; h p is the near-field multipath channel h near-field Sparse representation in the polar domain; W is composed of the near-field array response vectors with different angle and distance parameters, as follows: In ultra-large-scale MIMO systems, considering the multipath effects caused by scatterers distributed in both the far field and the near field, the actual mixed-field multipath channel can be modeled as: Where L = L f +L n , L is the total multipath number of the mixed field channel, L f and L n Represent the multipath numbers of the far-field channel and the near-field channel respectively; at the same time, the lth far-field path gain and near-field path gain are and 4. A mixed-field channel estimation method for ultra-large-scale MIMO systems according to claim 3, characterized in that: Step 2: The compressed sensing model for mixed field channel estimation is established by equations (2), (8), (12), and (14): y=Pψh h +n (15) Where ψ=[W,F], denoted as the sparse transformation matrix of the mixed field channel; therefore, ψ is the matrix of the far field channel h f and the near-field channel h n The sparse representation of h is sufficient; that is, h n =Ψh' p ,h f =ψh' a , where h' a ,h' p ∈C (N+S)×1 ,h' a is the sparse representation of the far-field channel under the sparse transformation matrix of the mixed-field channel, h' p is the sparse representation of the near-field channel under the sparse transformation matrix of the hybrid channel,

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