Scene-proportion-unknown super-large-scale MIMO mixed field channel estimation method based on OMP algorithm

Abstract

The invention relates to a scene-proportion-unknown super-large-scale MIMO mixed field channel estimation method based on an OMP algorithm. The method comprises the following steps: constructing a signal model from a sending end to a receiving end; calculating a Rayleigh distance according to the transmitting end antenna array and the receiving end antenna array; constructing a far-field channel model and a near-field channel model between the transmitting end and the receiving end according to the calculated Rayleigh distance; constructing a mixed field channel model between the transmitting end and the receiving end according to the constructed far-field channel model and near-field channel model; and solving the mixed field channel model by using an OMP algorithm with an unknown scene proportion, and estimating the mixed field channel between the sending end and the receiving end. According to the method, the signal can be effectively estimated in a scene in which the proportion of far-field and near-field path numbers is unknown.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology, and in particular relates to an ultra-large-scale MIMO mixed-field channel estimation method with unknown scene ratio based on an OMP algorithm. Background Art

[0002] With the continuous growth of wireless communication demand, new generation communication systems such as 5G and 6G have put forward higher requirements for spectrum efficiency, system capacity and service quality. XL-MIMO technology can significantly improve system capacity and coverage by deploying a large number of antenna units in the communication system, so it is regarded as one of the core technologies for future communications. However, the far-field and near-field channel components in the XL-MIMO system bring complex propagation characteristics, making channel modeling and estimation challenging. Traditional channel estimation methods are mostly based on uniform linear array models, and usually assume that the proportion of far-field and near-field path components in the communication environment is known. However, the propagation environment in actual scenarios is complex and changeable. Factors such as building occlusion, obstacle reflection, user density, and propagation distance will affect the proportion of far-field and near-field components, making this assumption often difficult to meet in practical applications.

[0003] In addition, existing XL-MIMO channel estimation methods are mostly designed for single-antenna users. This single channel model is difficult to capture the complex propagation characteristics of multi-antenna users in actual scenarios. Some current studies have tried to improve channel estimation in scenarios where the ratio is known by introducing technologies such as sparse representation and compressed sensing. However, when the ratio of far-field and near-field paths is unknown, the performance of these methods is often unstable and difficult to adapt to the needs of mixed-field channel estimation with unknown ratios in real communication environments.

[0004] As a classic sparse signal reconstruction method, the orthogonal matching pursuit algorithm has efficient iterative search capabilities and can gradually select the sparse components that best match the observed data, thereby achieving high-precision channel estimation. However, in the problem of mixed-field channel estimation with unknown scale, the traditional OMP algorithm and other sparse reconstruction algorithms have not been fully optimized and applied. The XL-MIMO system based on the Saleh-Valenzuela model for channel modeling has high flexibility and can accurately describe the multipath propagation characteristics. Therefore, it has application potential in scenarios with unknown scales when combined with the OMP algorithm.

[0005] Therefore, it is necessary to propose a new method that can realize mixed-field channel estimation in XL-MIMO systems through adaptive channel modeling and efficient sparse channel estimation methods when the ratio of far field and near field is unknown. This method should not only have strong channel reconstruction capabilities, but also have good robustness and adaptability in complex and changing communication scenarios to meet the channel estimation needs in future high-density and complex communication environments. Summary of the invention

[0006] In order to solve the problems existing in the background technology and achieve the purpose mentioned in the background technology, the present invention provides a very large-scale MIMO mixed field channel estimation method with unknown scene ratio based on OMP algorithm, comprising:

[0007] S1: Construct the signal model from the sender to the receiver;

[0008] S2: Calculate the Rayleigh distance based on the transmitting antenna array and the receiving antenna array;

[0009] S3: constructing a far-field channel model and a near-field channel model between the transmitter and the receiver according to the calculated Rayleigh distance;

[0010] S4: constructing a mixed field channel model between the transmitting end and the receiving end according to the constructed far-field channel model and the near-field channel model;

[0011] S5: The mixed-field channel model is solved using the OMP algorithm with unknown scene ratio to estimate the mixed-field channel between the transmitter and the receiver.

[0012] Preferably, the signal model from the transmitting end to the receiving end includes:

[0013] Y=WHP+N

[0014]

[0015]

[0016] y m =WHQs m +n m

[0017]

[0018]

[0019]

[0020] Where, Y represents the pilot signal received by the receiving end, W represents the merging matrix, represents the channel matrix between the transmitter and the receiver; N represents the noise at the receiver, P represents the pilot signal sent by the transmitter; M represents the number of time slots; y m represents the pilot signal received by the receiving end in the mth time slot; Indicates the number of RF chains of the transmitting antenna array; represents the number of RF chains of the receiving antenna array; N1 and N2 represent the number of transmitting antennas and the number of receiving antennas respectively; s mrepresents the pilot signal sent by the transmitter in the mth time slot; Q represents the hybrid precoding matrix; express The receiving noise, σ 2 is the noise power after the mth time slot is combined, Indicates size The unit array of Indicates that the noise follows a complex Gaussian distribution, M <N1,I M represents the unit matrix of size M×M, represents the Kronecker product.

[0021] Preferably, the Rayleigh distance includes:

[0022]

[0023] Wherein, Z represents the Rayleigh distance, D1 and D2 represent the apertures of the transmitting antenna array and the receiving antenna array respectively; λ represents the wavelength.

[0024] Preferably, the far-field channel model between the transmitting end and the receiving end includes: when the distance between the scatterer and the transmitting antenna array is greater than the Rayleigh distance or the distance between the scatterer and the receiving antenna array is greater than the Rayleigh distance, the channel should be modeled as a far-field channel and modeled based on a plane wave, and the far-field channel model is constructed as follows:

[0025]

[0026]

[0027]

[0028] Among them, α l is the complex gain of the far-field path, l f is the number of far-field paths, and are the steering vectors of the transmitting antenna array and the receiving antenna array, respectively. H represents conjugate transpose, j represents imaginary unit, N1 and N2 represent the number of transmitting antennas and the number of receiving antennas respectively; e represents the natural base, π represents pi, and denote the angles of the antennas of the transmitting antenna array and the receiving antenna array at the lth path, respectively, and and Respectively represent the actual physical angles of the antennas of the transmitting antenna array and the receiving antenna array, and their value ranges are n1=1,2,..,N1,n2=1,2,..,N2;

[0029] The above non-sparse channel Hf Sparse angular channel It is expressed as:

[0030]

[0031]

[0032]

[0033] in, is the sparse far-field angle domain channel; F t and F r are the Fourier transform matrices of the transmitting and receiving ends respectively; and represent the sampling angles of the far-field steering vectors of the transmitting antenna array and the receiving antenna array respectively.

[0034] Preferably, the near-field channel model between the transmitting end and the receiving end includes: when the distance between the scatterer and the transmitting antenna array is less than the Rayleigh distance or the distance between the scatterer and the receiving antenna array is less than the Rayleigh distance, the channel should be modeled as a near-field channel and modeled based on spherical waves, and the near-field channel model is constructed as follows:

[0035]

[0036]

[0037]

[0038]

[0039]

[0040]

[0041]

[0042] Among them, g l is the complex gain of the near-field path, l n is the number of near-field paths, and are the steering vectors of the transmitting antenna array and the receiving antenna array respectively; H represents conjugate transpose; and They represent the distances from the lth scatterer to the center of the transmitting antenna array and the receiving antenna array, respectively. represents the distance from the lth scatterer to the n1th antenna of the transmitting antenna array; represents the distance from the lth scatterer to the n2th antenna of the receiving antenna array; Represents the spacing between antennas in the transmit antenna array and the receive antenna array; Indicates the distance between the n1th antenna of the transmitting antenna array and the central array antenna; Indicates the distance between the n2th antenna of the receiving antenna array and the central array antenna;

[0043] The above non-sparse channel H n Sparse polar channel It is expressed as:

[0044]

[0045]

[0046]

[0047] in, Sparse polar channel Matrix D t Each column element represents the angle and distance Upsampled near-field array steering vector; D r Each column element represents the angle and distance Upsampled near-field array steering vector, Indicates the sampling angle The number of sampling distances at Indicates the sampling angle The number of sampling distances at which

[0048] Preferably, the mixed field channel model comprises:

[0049]

[0050] Among them, H hybrid Represents a mixed-field channel model.

[0051] Preferably, the solving of the mixed-field channel model includes: performing channel estimation of the far-field channel model and the near-field channel model respectively, assuming that the proportion of the far-field channel model is 1 when estimating the far-field channel model, and traversing the proportion with a certain step size when estimating the near-field channel model until the residual between the received signal and the predicted signal of the current estimated channel model reaches a set threshold, and finally updating the channel parameters to obtain the estimation result of the mixed-field channel model.

[0052] The present invention has at least the following beneficial effects

[0053] For the scenario where both the transmitting and receiving ends are equipped with antenna arrays and the ratio of the number of far-field and near-field paths is unknown, the present invention constructs far-field and near-field channel models respectively, thereby effectively constructing a mixed-field channel model, and adopts the OMP algorithm to perform channel estimation in three stages. The proposed OMP mixed-field channel estimation algorithm always shows the best estimation performance under all ratio values, with an estimation performance improvement of about 5.8dB compared with the most traditional OMP algorithm, and an estimation performance improvement of about 1.8dB compared with the traditional OMP-1 algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0055] Figure 2 It is a schematic diagram of far field and near field distribution of the present invention;

[0056] Figure 3 It is a schematic diagram of the mixed field channel model of the present invention;

[0057] Figure 4 It is a schematic diagram of experimental simulation comparison of the present invention. DETAILED DESCRIPTION

[0058] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0059] See also Figure 1 to Figure 4 The present invention provides a method for estimating a mixed-field channel of a very large-scale MIMO with unknown scene ratio based on an OMP algorithm, comprising:

[0060] S1: Construct the signal model from the sender to the receiver;

[0061] Assume that the transmitting end and the receiving end are equipped with N1 antennas and N2 antennas respectively. Since the antenna array is usually implemented in a digital-analog hybrid manner with a certain number of radio frequency (RF) chains, it is assumed that the number of RF chains of the transmitting antenna array and the receiving antenna array is and set up Represents the channel from the transmitter to the receiver, then the received signal at the receiver can be expressed as:

[0062] y m =WHQsm +n m (0)

[0063] in, denote the pilot signal received in the mth time slot, the merging matrix, the hybrid precoding matrix and the pilot signal transmitted, respectively. express The receiving noise, σ 2 is the noise power after combining in the mth time slot.

[0064] set up p m The i-th element of is the transmission signal of the i-th antenna of the transmitting antenna array in the m-th time slot, then the pilot signal received in the M time slots can be expressed as:

[0065] Y=WHP+N(2)

[0066] in, are the pilot signal sent in M ​​time slots, the received pilot signal and the noise at the receiving end, and Indicates size The identity matrix, I M The identity matrix of size M×M, represents the Kronecker product, indicating that the noise follows a complex Gaussian distribution. In the channel estimation problem, H needs to be estimated based on the given P, W, and Y. In the XL-MIMO system, the value of the number of antennas N1 in the transmitting antenna array is usually large. Therefore, in order to reduce the pilot overhead in the actual communication system, a channel estimation scheme with low overhead (M <N1)。

[0067] S2: Calculate the Rayleigh distance based on the transmitting antenna array and the receiving antenna array;

[0068] The electromagnetic radiation field in wireless communication systems can be divided into far field and near field. Different fields will lead to different channel models. The boundary between these two fields is determined by the Rayleigh distance Determine, where D1 and D2 are the apertures of the transmitting and receiving antenna arrays, respectively, and λ is the wavelength. The models of the far field and near field are as follows: Figure 2 shown.

[0069] S3: constructing a far-field channel model and a near-field channel model between the transmitter and the receiver according to the calculated Rayleigh distance;

[0070] Constructing the far-field channel model:

[0071] When the distance between the scatterer and the transmitting antenna array and the receiving antenna is greater than the Rayleigh distance, the channel is in the far field region, and the channel model should be modeled based on plane waves. The far-field channel model can be constructed as follows:

[0072]

[0073] Among them, α l is the complex gain of the far-field path, l f is the number of far-field paths, and are the steering vectors of the transmitting antenna array and the receiving antenna array, respectively. H represents conjugate transpose, j represents imaginary unit, e represents natural base, π represents pi, and They represent the angles of the antennas of the transmitting antenna array and the receiving antenna array at the lth path, respectively, which are expressed as:

[0074]

[0075]

[0076] Among them, α l is the complex gain of the far-field path, l f is the number of far-field paths, and are the steering vectors of the transmitting antenna array and the receiving antenna array, respectively. H represents conjugate transpose, j represents imaginary unit, N1 and N2 represent the number of transmitting antennas and the number of receiving antennas respectively; e represents the natural base, π represents pi, and denote the angles of the antennas of the transmitting antenna array and the receiving antenna array at the lth path, respectively, and and Respectively represent the actual physical angles of the antennas of the transmitting antenna array and the receiving antenna array, and their value ranges are

[0077] Due to the limited scattering in the communication environment, the angular channel The far field of is usually sparse. In order to reduce the pilot overhead for channel estimation, the non-sparse channel H f Sparse angular domain channels It is expressed as;

[0078]

[0079]

[0080] in, is the sparse far-field angle domain channel; F t and F rare the Fourier transform matrices of the transmitting and receiving ends respectively; and represent the sampling angles of the far-field steering vectors of the transmitting antenna array and the receiving antenna array respectively.

[0081] Constructing a near-field channel model:

[0082] When the distance between the scatterer and the transmitting antenna array and the receiving antenna is less than the Rayleigh distance, the channel is in the near field region, and the channel model should be modeled based on spherical waves. The near field channel model can be constructed as follows:

[0083]

[0084] Among them, g l is the complex gain of the near-field path, l n is the number of near-field paths, and are the steering vectors of the transmitting antenna array and the receiving antenna array respectively; and They represent the distances from the lth scatterer to the center of the transmitting antenna array and the receiving antenna array, respectively, and are expressed as:

[0085]

[0086] in, represents the distance from the lth scatterer to the n1th antenna of the transmitting antenna array; represents the distance from the lth scatterer to the n2th antenna of the receiving antenna array; Indicates the distance between the n1th antenna of the transmitting antenna array and the central array antenna; represents the distance between the n2th antenna of the receiving antenna array and the central array antenna; it can be expressed as:

[0087]

[0088] in, Represents the spacing between the antennas in the transmit antenna array and the receive antenna array; its expression is:

[0089]

[0090] Different from the far field, the near field steering vector has an additional distance parameter, so the Fourier (DFT) transform matrix cannot sparse the original non-sparse channel matrix. Therefore, the near field polar domain transform matrix is ​​used here to solve this problem. The polar domain transform matrix can be expressed as:

[0091]

[0092] Based on the polar domain transformation matrix D t and D r , the above non-sparse channel H n Sparse polar channel It is expressed as:

[0093]

[0094] in, Sparse polar channel Matrix D t Each column element represents the angle and distance Upsampled near-field array steering vector; D r Each column element represents the angle and distance Upsampled near-field array steering vector, Indicates the sampling angle The number of sampling distances at Indicates the sampling angle The number of sampling distances at which

[0095] S4: constructing a mixed field channel model between the transmitting end and the receiving end according to the constructed far-field channel model and the near-field channel model;

[0096] Based on the far-field channel model and the near-field channel model, a mixed-field channel model is constructed, considering that the scatterers in the actual XL-MIMO scenario may be located in the near field or in the far field. This model aims to more comprehensively describe the impact of different scatterers on channel characteristics during signal propagation. hybrid It can be expressed as:

[0097]

[0098] Among them, H hybrid Represents a mixed-field channel model.

[0099] S5: The mixed-field channel model is solved using the OMP algorithm with unknown scene ratio to estimate the mixed-field channel between the transmitter and the receiver.

[0100] Based on equations (5) and (12), equation (2) can be expressed as:

[0101]

[0102] Among them, A r =WF r and A t =F t HP represents the receiving and transmitting perception matrices of the far field, B r =WD r and They represent the near-field receiving and transmitting sensing matrices respectively. By the derivation of equations (5) and (12), we can get and They are all sparse, and thus the entire mixed field channel can be estimated.

[0103] In this section, the specific implementation process of the proposed method will be elaborated in detail. The mixed field channel estimation algorithm for the scenario with unknown ratio of far-field and near-field components can be summarized as the following process:

[0104] Input: received signal Y, hybrid precoding matrix W, pilot signal matrix P, far-field transmitter transformation matrix F t , the far-field receiver transformation matrix F r , near-field transmitter transformation matrix D t , near-field transmitter transformation matrix D r , the total number of paths L, and the ratio of the number of far-field and near-field paths γ.

[0105] Output: Estimation results

[0106] The specific process of estimating the mixed field channel between the transmitter and the receiver is as follows:

[0107] Initialization: γ = 1, Residual matrix R = Y, actual number of iterations x = 0,

[0108] Stage 1: Estimation of far-field path components in the angular domain.

[0109] step1: command

[0110] Step 2: For each far-field path, calculate the correlation between the far-field perception matrix and the residual matrix, through Find the most relevant column index of the far-field sensing matrix and the residual matrix as the far-field support index, A r H represents the conjugate transpose of the calculated far-field receiving perception matrix, A t H represents the conjugate transpose of the far-field transmission sensing matrix, ||.||2 represents the two-norm, and is expressed by Calculate the index of the far-field transmitter and receiver, floor means rounding down, mod means the remainder function, and update the total support set and component support set

[0111] Step 3: Least squares method Indicated in Ωf The far-field channel estimation matrix at: First, calculate the far-field sparse matrix Indicates that the far-field transmission perception matrix takes the OK, Indicates that the far-field receiving perception matrix takes the Then calculate the least squares method Represents the channel estimation vector corresponding to the far-field channel estimation matrix, and finally the vector matrix is ​​obtained

[0112] Step 4: Update the far-field residual matrix:

[0113] Step 5: Update the number of iterations x: x = x + 1. If x ≤ L, repeat step 2 to continue iterating. Otherwise, the iteration ends and the latest iteration result is obtained.

[0114] Stage 2: Estimation of near-field path components in the polar coordinate domain.

[0115] step6: command The minimum residual vector r m =r, r represents the residual vector corresponding to the residual matrix.

[0116] Step 7: Adjust γ from 1 to 0 in steps of -1 / L to traverse all possibilities: through Ω = Ω f (1:γL),r=R(:,γL+1),Ω f (1:γL) means extracting the 1st to γLth elements in the far-field support set, R(:,γL+1) means taking the γL+1th column in the residual matrix, and converting the far-field support set corresponding to γL and the residual vector r m Stored in Ω f (1:γL) and R(:,γL+1).

[0117] Step 8: For each near-field path, calculate the correlation between the near-field perception matrix and the residual matrix, through Find the perception matrix B r , B t The column index most relevant to the residual matrix R is used as the near-field support index and is passed through Calculate the index of the near-field transmitter and receiver, and update the total support set and the near-field component support set

[0118] Step9: Least squares method Indicated in Ω n The near-field estimation matrix at: First, calculate the near-field sparse matrix Indicates that the near-field transmission perception matrix takes the first OK, Indicates that the near-field receiving perception matrix takes the Then calculate the least squares method Represents the channel estimation vector corresponding to the near-field channel estimation matrix, and finally the vector is matrixed to obtain

[0119] Step 10: Update the near-field residual matrix and residual vector:

[0120] Step 11: Update the number of iterations x: x = x + 1. If x ≤ (1-γ) L, repeat step 8 to continue iterating. Otherwise, the iteration ends and the latest iteration result is obtained.

[0121] Step 12: If the value of the residual vector is less than or equal to the minimum value of the residual vector, that is, ||r||≤||r m ||, then update the minimum residual vector r m =r.

[0122] Phase 3: Output the final estimation result.

[0123] step13: Initialize the mixed field channel matrix:

[0124] Step 14: If the near-field support set but If the near-field support set but

[0125] Experimental simulation:

[0126] The simulation parameters are set as follows:

[0127] Carrier frequency: 30GHz

[0128] Carrier wavelength: 0.01m

[0129] Number of transmitting antennas: 2

[0130] Number of receiving antennas: 256

[0131] Total number of paths: 10

[0132] Pilot length: 1

[0133] SNR:-6dB

[0134] The normalized mean square error (NMSE) between the channel estimation matrix and the true channel matrix is ​​used as the performance indicator, which is defined as:

[0135]

[0136] Where H represents the real channel matrix, represents the estimated channel matrix, and E represents the mathematical expectation.

[0137] The performance of the ultra-large-scale MIMO mixed-field channel estimation algorithm proposed in the present invention is compared with the mixed-field channel estimation algorithm of the traditional OMP algorithm as well as LS and MMSE. The comparison results are as follows: Figure 4 As shown:

[0138] from Figure 4 It can be seen that the proposed OMP mixed-field channel estimation algorithm always achieves the lowest NMSE value under all γ values. Specifically, compared with the most traditional OMP algorithm, the performance is improved by about 5.8dB, and compared with the traditional OMP-1 algorithm, the performance is improved by about 1.8dB. Especially when γ = 0, that is, when there is only a near-field scene, the NMSE value of the proposed algorithm reaches the lowest value, showing the best estimation performance. When γ = 1, that is, when there is only a far-field scene, the NMSE value of the proposed algorithm is the second lowest.

[0139] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A method for channel estimation of ultra-large-scale MIMO mixed field with unknown scene ratio based on OMP algorithm, characterized in that: include: S1: Construct the signal model from the sender to the receiver; S2: Calculate the Rayleigh distance based on the transmitting antenna array and the receiving antenna array; S3: constructing a far-field channel model and a near-field channel model between the transmitter and the receiver according to the calculated Rayleigh distance; S4: constructing a mixed field channel model between the transmitting end and the receiving end according to the constructed far-field channel model and the near-field channel model; S5: The mixed-field channel model is solved using the OMP algorithm with unknown scene ratio to estimate the mixed-field channel between the transmitter and the receiver.

2. According to claim 1, a method for channel estimation of ultra-large-scale MIMO mixed field with unknown scene ratio based on OMP algorithm, characterized in that: The signal model from the transmitting end to the receiving end includes: Y=WHP+N the m =WHQs m +n m Where, Y represents the pilot signal received by the receiving end, W represents the merging matrix, represents the channel matrix between the transmitter and the receiver; N represents the noise at the receiver, P represents the pilot signal sent by the transmitter; M represents the number of time slots; y m represents the pilot signal received by the receiving end in the mth time slot; Indicates the number of RF chains of the transmitting antenna array; represents the number of RF chains of the receiving antenna array; N1 and N2 represent the number of transmitting antennas and the number of receiving antennas respectively; s m represents the pilot signal sent by the transmitter in the mth time slot; Q represents the hybrid precoding matrix; express The receiving noise, σ 2 is the noise power after the mth time slot is combined, Indicates size The identity matrix of Indicates that the noise follows a complex Gaussian distribution, M <N1,I M represents the identity matrix of size M×M, represents the Kronecker product.

3. According to claim 2, a method for channel estimation of ultra-large-scale MIMO mixed field with unknown scene ratio based on OMP algorithm, characterized in that: The Rayleigh distance includes: Wherein, Z represents the Rayleigh distance, D1 and D2 represent the apertures of the transmitting antenna array and the receiving antenna array respectively; λ represents the wavelength.

4. According to claim 3, a method for channel estimation of ultra-large-scale MIMO mixed field with unknown scene ratio based on OMP algorithm, characterized in that: The far-field channel model between the transmitting end and the receiving end includes: when the distance between the scatterer and the transmitting antenna array is greater than the Rayleigh distance or the distance between the scatterer and the receiving antenna array is greater than the Rayleigh distance, the channel should be modeled as a far-field channel and modeled based on a plane wave. The far-field channel model is constructed as follows: Among them, α l is the complex gain of the far-field path, l f is the number of far-field paths, and are the steering vectors of the transmitting antenna array and the receiving antenna array respectively, H represents the conjugate transpose, j represents the imaginary unit, N1 and N2 represent the number of transmitting antennas and the number of receiving antennas respectively; e represents the natural base, π represents the circumference of a circle, and denote the angles of the antennas of the transmitting antenna array and the receiving antenna array at the lth path, respectively, and and Respectively represent the actual physical angles of the antennas of the transmitting antenna array and the receiving antenna array, and their value ranges are n1=1,2,..,N1,n2=1,2,..,N2; The above non-sparse channel H f Sparse angular channel It is expressed as: in, is the sparse far-field angle domain channel; F t and F r are the Fourier transform matrices of the transmitting and receiving ends respectively; and represent the sampling angles of the far-field steering vectors of the transmitting antenna array and the receiving antenna array respectively.

5. According to claim 4, a method for channel estimation of ultra-large-scale MIMO mixed field with unknown scene ratio based on OMP algorithm, characterized in that: The near-field channel model between the transmitting end and the receiving end includes: when the distance between the scatterer and the transmitting antenna array is less than the Rayleigh distance or the distance between the scatterer and the receiving antenna array is less than the Rayleigh distance, the channel should be modeled as a near-field channel and modeled based on spherical waves. The near-field channel model is constructed as follows: Among them, g l is the complex gain of the near-field path, l n is the number of near-field paths, and are the steering vectors of the transmitting antenna array and the receiving antenna array respectively; H represents the conjugate transpose; and They represent the distances from the lth scatterer to the center of the transmitting antenna array and the receiving antenna array, respectively. represents the distance from the lth scatterer to the n1th antenna of the transmitting antenna array; represents the distance from the lth scatterer to the n2th antenna of the receiving antenna array; Indicates the spacing between antennas in the transmit antenna array and the receive antenna array; Indicates the distance between the n1th antenna of the transmitting antenna array and the central array antenna; Indicates the distance between the n2th antenna of the receiving antenna array and the central array antenna; The above non-sparse channel H n Sparse polar channel It is expressed as: in, Sparse polar channel Matrix D t Each column element represents the angle and distance Upsampled near-field array steering vector; D r Each column element represents the angle and distance Upsampled near-field array steering vector, Indicates the sampling angle The number of sampling distances at Indicates the sampling angle The number of sampling distances at which 6. The method for channel estimation of ultra-large-scale MIMO mixed field with unknown scene ratio based on OMP algorithm according to claim 5, characterized in that: The mixed field channel model includes: Among them, H hybrid Represents a mixed-field channel model.

7. The method for channel estimation of ultra-large-scale MIMO mixed field with unknown scene ratio based on OMP algorithm according to claim 6, characterized in that: The solving of the mixed field channel model comprises: performing channel estimation of the far field channel model and the near field channel model respectively, assuming that the proportion of the far field channel model is 1 when estimating the far field channel model, and traversing the proportion with a certain step length when estimating the near field channel model until the residual between the received signal and the predicted signal of the current estimated channel model reaches a set threshold, and finally updating the channel parameters to obtain the estimation result of the mixed field channel model.

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