Joint sparse channel estimation method based on large-scale MIMO-OTFS, medium and equipment

Abstract

The invention relates to the technical field of wireless communication, new-generation mobile communication core network and access network construction, internet access and related services, in particular to a joint sparse channel estimation method based on a large-scale MIMO-OTFS, a medium and equipment. Transmitting training pilot frequencies for different antennas in the delay-Doppler domain and receiving response signals of the different antennas; s2, constructing a delay-Doppler-angle three-dimensional sparse channel model based on the received pilot frequency response signal; s3, converting a channel estimation problem into a sparse signal recovery problem based on a compressed sensing theory according to the three-dimensional sparse channel model; and S4, solving a sparse signal recovery problem by adopting a three-dimensional structured generalized orthogonal matching pursuit algorithm, recovering sparse channel response, and completing channel estimation. The channel estimation method for the large-scale MIMO-OTFS system is low in pilot frequency overhead, high in precision and high in efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of wireless communication, new generation mobile communication core network and access network construction, Internet access and related service technology, in particular to a channel estimation method suitable for a large-scale multiple-input multiple-output orthogonal time-frequency space (MIMO-OTFS) system in a high-speed mobile scenario, and more particularly to a compressed sensing channel reconstruction technology based on delay-Doppler-angle domain joint sparsity. BACKGROUND

[0002] In a wireless communication system, in order to meet the higher demand of system capacity, spectrum efficiency and data transmission rate of future mobile communication, multiple-input multiple-output (MIMO) technology and orthogonal frequency division multiplexing (OFDM) technology have been widely applied in standards such as long term evolution (LTE). MIMO technology can multiply the system capacity and spectrum efficiency without increasing the bandwidth through multi-antenna configuration; OFDM technology has become the core technology of wideband transmission due to its strong anti-multipath fading ability, non-sensitivity to narrowband interference and other advantages.

[0003] However, in a high-speed mobile scenario (such as high-speed rail, vehicle-mounted communication), a fast time-varying channel will cause serious inter-carrier interference (ICI), which significantly reduces the performance of the OFDM system. Although the existing technology reduces ICI by shortening the OFDM symbol length, using linear / nonlinear equalization or pulse shaping, etc., these methods still face the problem of low efficiency in high-speed scenarios. For example, in order to maintain the inter-symbol interference (ISI) suppression effect, the length of the cyclic prefix (CP) and the guard interval (GI) needs to be fixed, resulting in waste of spectrum resources.

[0004] Orthogonal Time Frequency Space (OTFS) modulation technology converts time-varying channel into quasi-static channel in delay-Doppler domain, effectively suppresses ICI, and becomes the preferred solution in high-speed scenarios. However, large-scale MIMO-OTFS system requires accurate channel state information (CSI), and traditional pulse pilot method needs to allocate independent pilot for each antenna, and the pilot overhead is proportional to the number of antennas, delay and Doppler length, resulting in excessive resource consumption. In addition, although the channel estimation method based on compressive sensing (CS), such as orthogonal matching pursuit algorithm (OMP), can reduce the number of pilots, its iteration efficiency is low and the sparsity is not fully utilized, and for example, 3D-SOMP algorithm selects only one atom in each iteration, which is easy to introduce error accumulation, and does not fully exploit the block sparsity characteristics of Doppler domain and angle domain, affecting the reconstruction accuracy. SUMMARY

[0005] The application provides a low pilot overhead, high precision and high efficiency joint sparse channel estimation method based on large-scale MIMO-OTFS, medium and equipment, which can at least solve one of the above technical problems.

[0006] In order to solve the above technical problems, the application adopts the following technical solutions: The joint sparse channel estimation method based on large-scale MIMO-OTFS comprises the following steps: S1, obtaining channel measurement information: using non-orthogonal pilot mechanism, transmitting training pilot for different antennas in delay-Doppler domain and receiving the response signal, the pilots of different antennas overlap in delay-Doppler domain, and the training pilots of each antenna use independent complex Gaussian random sequence; S2, establishing a three-dimensional sparse channel model: based on the received pilot response signal, a delay-Doppler-angle three-dimensional channel model is constructed, which models the channel response in delay domain, Doppler domain and angle domain as sparse; S3, constructing a sparse recovery problem: according to the three-dimensional sparse channel model, the channel estimation problem is converted into a sparse signal recovery problem based on compressive sensing theory; S4, solving and reconstructing the channel: a three-dimensional structured generalized orthogonal matching pursuit algorithm is used to solve the sparse signal recovery problem, and the sparse channel response is recovered, and the channel estimation is completed.

[0007] Further, in S2, the channel response in the three-dimensional sparse channel model has a finite support set in the delay domain, Doppler domain and angle domain, respectively, the support set of the delay domain is , and the support set of the Doppler domain is wherein, , , are the number of elements of the delay domain support set and the Doppler domain support set respectively, the angle domain is burst sparse block structure, which is a support set with block size starting at a certain position in the angle dimension.

[0008] Further, in the S3, a compressed sensing model is constructed based on the compressed sensing theory, and the compressed sensing model reconstructs a three-dimensional sparse channel response for a received pilot signal vector, and the expression is: ; wherein, is a received pilot signal vector, is a sensing matrix, is a three-dimensional sparse channel response, is a noise vector.

[0009] Further, in the S4, the implementation of the three-dimensional structured generalized orthogonal matching pursuit algorithm, i.e., the 3D-SgOMP algorithm, includes the following contents: a. Delay domain atom selection: through a generalized orthogonal matching pursuit algorithm, i.e., a gOMP algorithm, S maximum correlation atom indexes are selected from the delay domain in each iteration process of the compressed sensing model, , indicating the number of maximum correlation atoms selected from the delay domain in each iteration; b. Doppler domain block expansion: based on the selected delay domain atom, the support block range of the Doppler domain is determined through an energy threshold, and the central Doppler frequency shift region is focused; c. Angle domain burst sparse conversion: the lifting matrix is used to convert the burst sparse structure of the angle domain into a traditional block sparse structure, and the starting position of the burst sparse block is determined through the maximum energy criterion; d. Joint iterative update: the three-dimensional atom support set is updated by combining the atom selection results of the delay domain, the Doppler domain and the angle domain, and the true channel response is gradually approximated through the least square method.

[0010] Further, in the S4, the specific implementation steps of the 3D-SgOMP algorithm further include: S4.1, initialize the residual , the three-dimensional atom support set , and the iteration number , let the residual , the three-dimensional atom support set , the iteration number , set the number of selected atoms in each iteration ; S4.2, calculate the correlation vector , and three-dimensionally tensorize the vector u. S4.3. Sum the correlation vector u after 3D tensorization along the angle domain and Doppler domain to obtain the delay domain correlation vector , and select the delay domain correlation vector Center front The atomic index of the maximum value; S4.4. Calculate the Doppler domain correlation matrix based on the selected delay domain atoms and determine the Doppler domain support block range through the energy threshold; S4.5, by improving the matrix Converting the angle domain burst sparse structure into a block sparse structure and determining the starting index of the burst sparse block; S4.6. Update the 3D atomic support set , and estimate the channel response by the least squares method , update the residual ; S4.7, repeat the iteration until the estimated channel response Satisfy the preset sparsity , the channel estimation iterative algorithm outputs the final channel response estimation result .

[0011] Furthermore, in S4.4, the multi-spectral domain support block range is determined by: S4.4.1. Setting Doppler Domain Energy Threshold , the energy threshold Satisfy the following conditions: ; in, is the Doppler dimension, is the vector obtained by summing the correlation vector u along the angle domain and the delay domain after three-dimensional tensorization; S4.4.2, from Central location Start expanding and select the energy threshold that meets the above formula Conditional Doppler index block.

[0012] Furthermore, in S4.5, the improvement matrix , where C represents the complex field, N t represents the number of transmitted pulses, which is equal to the number of MIMO transmit antennas. The construction of the lifting matrix L includes the following steps: S4.5.1. The first step of the promotion matrix Columns are only The position is 1, and the rest of the positions are 0, where D is the support set of the block size, , Defined as: ; S4.5.2, converting the burst sparse correlation vector into a block sparse vector by lifting matrix , and rearranging into a matrix to determine the starting index of the angle domain burst sparse block.

[0013] A computer readable storage medium storing a computer program, the computer program being executed by a processor to cause the processor to perform the steps of the joint sparse channel estimation method based on massive MIMO-OTFS.

[0014] A computer device comprising a memory and a processor, the memory storing a computer program, the computer program being executed by the processor to cause the processor to perform the steps of the joint sparse channel estimation method based on massive MIMO-OTFS.

[0015] The beneficial effects of the present application are embodied in: The channel estimation method adopted by the present application is a three-dimensional sparse orthogonal matching pursuit algorithm (3D-SgOMP). When selecting the position index of the delay domain, the method adopts the idea of generalized orthogonal matching pursuit (gOMP) and selects S atoms at a time, instead of the original single-atom iteration mechanism. The present application analyzes the sparse characteristics of OTFS in the delay-Doppler domain in combination with the theoretical idea of compressed sensing (CS) and proposes a corresponding reconstruction algorithm to recover the channel impulse response (CIR) from a small amount of pilots. The present application realizes a low-pilot overhead, high-precision, and high-efficiency channel estimation method for massive MIMO-OTFS systems. BRIEF DESCRIPTION OF DRAWINGS

[0016] The drawings described herein are intended to provide further understanding of the present application, form a part of the present application, and are used to explain the present application, and do not constitute an improper limitation on the present application.

[0017] Figure 1 is a joint sparse channel estimation method flowchart of the embodiment of the present application.

[0018] Figure 2 is an OTFS modulation and demodulation block diagram of the embodiment of the present application.

[0019] Figure 3 is a 3D sparse model diagram of the OTFS modeling channel of the embodiment of the present application.

[0020] Figure 4 is a pilot placement diagram of the OTFS symbol frame of the embodiment of the present application.

[0021] Figure 5 is a structural block diagram of a computer device of an embodiment of the present application. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The embodiments in the present application and the features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0023] It should be noted that the meaning of "and / or" appearing throughout the text includes three parallel solutions. Taking "A and / or B" as an example, it includes the A solution, or the B solution, or the solution of A and B being satisfied at the same time. In addition, "multiple" means more than two. In addition, the technical solutions of each embodiment can be combined with each other, but it must be based on the fact that a person skilled in the art can realize it. When the combination of technical solutions appears contradictory or unachievable, it should be considered that the combination of technical solutions does not exist and is not within the scope of protection claimed by the present application.

[0024] Referring to Figures 1-4 , the embodiment of the present application provides a joint sparse channel estimation method based on large-scale MIMO-OTFS, comprising the following steps: Step 1, obtaining channel measurement information: using a non-orthogonal pilot mechanism, transmitting training pilots for different antennas in the delay-Doppler domain and receiving their response signals, the pilots of different antennas overlap in the delay-Doppler domain, and the training pilots of each antenna use independent complex Gaussian random sequences.

[0025] Step 2, establishing a three-dimensional sparse channel model: based on the received pilot response signals, a delay-Doppler-angle three-dimensional channel model is constructed, which models the channel response in the delay domain, the Doppler domain and the angle domain as sparse.

[0026] In the Step 2, the channel response in the three-dimensional sparse channel model has a finite support set in the delay domain, the Doppler domain and the angle domain, respectively, the support set of the delay domain is , the support set of the Doppler domain is , wherein , , the number of elements of the delay domain support set and the Doppler domain support set are respectively , and the angle domain is a burst sparse block structure, which is a support set with a block size of

[0027] Step3, constructing a sparse recovery problem: according to the three-dimensional sparse channel model, the channel estimation problem is converted into a sparse signal recovery problem based on the compression sensing theory.

[0028] In the Step3, a compression sensing model is constructed based on the compression sensing theory, which reconstructs the three-dimensional sparse channel response for the received pilot signal vector, expressed as: ; wherein, is the received pilot signal vector, is the sensing matrix, is the three-dimensional sparse channel response, is the noise vector.

[0029] Step4, solving and reconstructing the channel: a three-dimensional structured generalized orthogonal matching pursuit algorithm is used to solve the sparse signal recovery problem, and the sparse channel response is recovered to complete the channel estimation.

[0030] In the Step4, the implementation of the three-dimensional structured generalized orthogonal matching pursuit algorithm, i.e., the 3D-SgOMP algorithm, includes the following contents: a, delay domain atom selection: through the generalized orthogonal matching pursuit algorithm, i.e., the gOMP algorithm, S maximum correlation atom indexes are selected from the delay domain in each iteration process of the compression sensing model, , indicating the number of maximum correlation atoms selected from the delay domain in each iteration; b, Doppler domain block expansion: based on the selected delay domain atoms, the support block range of the Doppler domain is determined through an energy threshold, focusing on the central Doppler shift region; c, angle domain burst sparse conversion: the lifting matrix is used to convert the burst sparse structure in the angle domain into a traditional block sparse structure, and the starting position of the burst sparse block is determined through the maximum energy criterion; d, joint iteration update: the atom selection results of the delay domain, the Doppler domain and the angle domain are combined to update the three-dimensional atom support set, and the least square method is used to gradually approximate the true channel response.

[0031] In the Step4, the specific implementation steps of the 3D-SgOMP algorithm further include: Step4.1, initialize the residual , the three-dimensional atom support set , and the iteration number , let the residual , the three-dimensional atom support set , the iteration number , set the number of selected atoms in each iteration ; Step 4.2, calculate the correlation vector , and quantize the vector u into three-dimensional tensor; Step 4.3, sum the correlation vector u after three-dimensional tensorization along the angle domain and Doppler domain to obtain the delay domain correlation vector , and select the delay domain correlation vector Center front The atomic index of the maximum value; Step 4.4. Calculate the Doppler domain correlation matrix based on the selected delay domain atoms and determine the Doppler domain support block range through the energy threshold; In Step 4.4, the multi-domain support block range is determined by: Step 4.4.1. Set the Doppler domain energy threshold , the energy threshold Satisfy the following conditions: ; in, is the Doppler dimension, is the vector obtained by summing the correlation vector u along the angle domain and the delay domain after three-dimensional tensorization; Step 4.4.2, from Central location Start expanding and select the energy threshold that meets the above formula Conditional Doppler index block; Step 4.5, by improving the matrix Converting the angle domain burst sparse structure into a block sparse structure and determining the starting index of the burst sparse block; In Step 4.5, the improvement matrix , where C represents the complex field, N t represents the number of transmitted pulses, which is equal to the number of MIMO transmit antennas. The construction of the lifting matrix L includes the following steps: Step 4.5.1. Improve the matrix Columns are only The position is 1, and the rest of the positions are 0, where D is the support set of the block size, , Defined as: ; Step 4.5.2: Convert the burst sparse correlation vector into a block sparse vector by lifting the matrix , and rearrange it into a matrix , to determine the starting index of the angle domain burst sparse block; Step 4.6. Update the 3D atomic support set and estimate the channel response by least square method , update the residual ; Step4.7, repeat the iteration until the estimated channel response satisfies the preset sparsity , the channel estimation iteration algorithm outputs the final channel response estimation result .

[0032] In order to clarify the method as clearly as possible, the present application provides the following specific implementation case: Step one, consider arranging a QAM modulated data sequence into a 2D data block to obtain a 2D-OTFS frame in delay-Doppler domain, denoted as , wherein and respectively denote the number of resource units in delay dimension and Doppler dimension, the preprocessing module first converts the 2D data block in delay-Doppler domain into a 2D time-frequency block by inverse symplectic-Fourier transform (ISFFT) and transmitting window function , which is formulated as:

[0033] , wherein and are discrete Fourier transform matrices, let the transmitting window function be an all-one matrix, represent Hadamard product; perform IDFT on the 2D time-frequency block to obtain a transmission signal S, , which is expressed as:

[0034] plus cyclic prefix (CP), and perform parallel-serial conversion to obtain the final transmitting signal :

[0035] , wherein represents a cyclic prefix matrix, represents the length of cyclic prefix, represents vectorization of a tensor.

[0036] Step two: after channel transmission, a receiving signal r b is obtained:

[0037] , wherein represents the channel length; The received signal r b In matrix form, we have

[0038] Each column vector of H can be regarded as a CP-added OFDM symbol, and the accepted time-frequency 2D data block can be obtained by removing the CP and DFT transform :

[0039] wherein, is the DFT transform matrix, represents the CP removal matrix; The OTFS post-processing is performed on the time-frequency 2D data block to obtain

[0040] wherein, represents the receiving window function; is the CIR in the delay-Doppler domain , the i-th element of which is specifically expressed as

[0041] wherein, represents the remainder after division; has a periodic property, i.e., there is the following relationship:

[0042] From the above formula, it can be found that the transmission signal experiences a time-independent channel in the delay-Doppler domain.

[0043] Step three: based on the CS theory, a 3D-SgOMP algorithm is proposed to utilize the sparsity of the channel 3D, taking the idea of selecting multiple atoms once in the gOMP algorithm as a framework, selecting a number of element indexes in the delay domain, and then taking them as a benchmark to select and expand the atoms in the Doppler domain and the angle domain; In order to estimate , the 3D-SgOMP algorithm is adopted to reconstruct the channel impulse response , and the specific steps of the 3D-SgOMP algorithm are as follows: (1) input: observation vector, sensing matrix, main path size; (2) initialization: iteration index ​​, atom support set , estimate channel response , initialize residual , the number of atoms selected by gOMP algorithm each time , the initial sparse value is ; (4) Calculate the correlation vector: ; (5) Sum the 3D tensor by column in the angle dimension and Doppler dimension respectively: , to get the correlation vector only about the delay dimension ; (6) The size of the sparse atom set block in the Doppler dimension is:

[0044] where, is the size of the Doppler dimension, is the vector obtained by summing all the vectors in the delay dimension and the angle dimension by column; (7) Convert the burst sparse correlation vector to a traditional block sparse vector by lifting matrix :

[0045] where, is the vector composed of all the angles after summing the delay dimension atom and the Doppler dimension atom by corresponding columns; (8) Update the atom support set: , update the least squares: , , ; (9) , , if , return to (3), otherwise go to the next step; (10) Output .

[0046] It should be noted that for , represents energy, if represents a vector, then , if represents a matrix, then . The tensorization of , that is, is burst sparse along the angle domain. In the algorithm, it is assumed that is the length of the non-zero sparse block, which needs to be estimated out with the first The algorithm is implemented by lifting the matrix , converting burst sparse vectors into traditional block sparse vectors with high resolution . The first List, Only in There is a non-zero element at the position, the value is 1, Defined as:

[0047] By Rearrange into , get the initial position index of the angle domain burst sparseness of this iteration, by calculating 2-norm of the row vector, and then take the largest element To obtain the starting index position of the non-zero sparse block. The core idea is to find the element position with the largest proportion in the angle index by improving the resolution.

[0048] In this method, the gOMP algorithm adopts the idea of ​​selecting multiple atoms at a time, and the gOMP algorithm shows that for any Sparse signal, as long as the perception matrix satisfies:

[0049] The original signal can be perfectly restored, which is the ideal condition for gOMP recovery. And each iteration selects Therefore, the 3D-SgOMP algorithm takes the gOMP algorithm as its framework and first selects element index, and then use this as a benchmark, and continuously expand to select 5% of the main Doppler domain atomic indexes with a certain threshold level greater than the atomic energy in the Doppler domain. Finally, based on the delay domain and Doppler domain atomic indexes, the angle domain burst sparsity is transformed into traditional block sparsity by improving the matrix L, and the angle domain burst sparsity initial position index is selected. This iteration ends and the 3D total index set is updated. , enter the next iteration. The 3D-SOMP algorithm only selects one delay domain main path in each iteration, which will result in the secondary energy path in the current iteration not being selected, and this main element may not be selected in subsequent iterations, thus causing potential errors.

[0050] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor performs the steps of the above-mentioned joint sparse channel estimation method based on massive MIMO-OTFS.

[0051] See alsoFigure 5 The embodiment of the present application also provides a computer device, comprising a memory and a processor, the memory stores a computer program, and the computer program is executed by the processor to make the processor execute the steps of the above-mentioned joint sparse channel estimation method based on massive MIMO-OTFS.

[0052] The embodiment of the present application also provides a computer program product comprising instructions which, when executed on a computer, cause the computer to carry out the steps of the above-mentioned joint sparse channel estimation method based on massive MIMO-OTFS.

[0053] It can be understood that the system, device and storage medium provided by the embodiment of the present application correspond to the method provided by the embodiment of the present application, and the explanation, examples and beneficial effects of the related content can refer to the corresponding part in the above-mentioned joint sparse channel estimation method based on massive MIMO-OTFS.

[0054] It should be noted that all or part of the steps in the embodiments of the present application can be implemented by software, hardware, firmware or any combination thereof. When implemented by hardware, all or part of the steps can be implemented in the form of a purchase standard component or a refitting component. When implemented by software, all or part of the steps can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The computer instructions can be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through a wired (for example, coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (for example, infrared, wireless, microwave, etc.) manner. The computer readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. integrated with one or more available media. The available medium can be a magnetic medium (for example, floppy disk, hard disk, magnetic tape), an optical medium (for example, DVD) or a semiconductor medium (for example, solid state disk (SSD)) and the like.

[0055] In conclusion, the application adopts a three-dimensional structured generalized orthogonal matching pursuit (3D-SgOMP) algorithm, changes single atom iteration into a multi-atom selection mechanism on the basis of the original 3D-SOMP algorithm, designs a non-orthogonal pilot, jointly models sparsity in the delay-Doppler-angle domain and converts a burst sparse characteristic through a lifting matrix, and thus realizes a large-scale MIMO-OTFS system channel estimation method with low pilot overhead, high precision and high efficiency.

[0056] It should be understood that the examples and embodiments described herein are merely illustrative and not intended to limit the present application, and various modifications or changes can be made thereto by those skilled in the art according to the present application, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A joint sparse channel estimation method based on massive MIMO-OTFS, characterized by: The following steps are involved: S1. Acquire channel measurement information: Using a non-orthogonal pilot mechanism, training pilots are transmitted for different antennas in the delay-Doppler domain and their response signals are received. The pilots of different antennas overlap in the delay-Doppler domain, and the training pilots of each antenna use independent complex Gaussian random sequences. S2. Establish a three-dimensional sparse channel model: Based on the received pilot response signal, construct a delay-Doppler-angle three-dimensional channel model that models the channel response as sparse in the delay domain, Doppler domain, and angle domain; S3. Constructing a sparse recovery problem: Based on the three-dimensional sparse channel model, the channel estimation problem is transformed into a sparse signal recovery problem based on compressed sensing theory; S4. Solve and reconstruct the channel: Use the three-dimensional structured generalized orthogonal matching pursuit algorithm to solve the sparse signal recovery problem, recover the sparse channel response, and complete the channel estimation.

2. The joint sparse channel estimation method based on massive MIMO-OTFS according to claim 1, wherein In S2, the channel response in the three-dimensional sparse channel model has finite support sets in the delay domain, Doppler domain and angle domain respectively. The support set in the delay domain is , the support set of the Doppler domain is ,in, , , which are the number of elements in the delay domain support set and the Doppler domain support set respectively. The angle domain is a burst sparse block structure, which starts at a certain position in the angle dimension and starts with is the supporting set of block sizes.

3. The joint sparse channel estimation method based on massive MIMO-OTFS according to claim 1, wherein In S3, a compressed sensing model is constructed based on the compressed sensing theory. The compressed sensing model reconstructs the three-dimensional sparse channel response for the received pilot signal vector, and the expression is: ； in, is the pilot signal vector at the receiving end, is the sensing matrix, is the three-dimensional sparse channel response, is the noise vector.

4. The joint sparse channel estimation method based on massive MIMO-OTFS according to claim 1, wherein In S4, the implementation of the three-dimensional structured generalized orthogonal matching pursuit algorithm, i.e., the 3D-SgOMP algorithm, includes the following: a. Delay domain atom selection: Through the generalized orthogonal matching pursuit algorithm, i.e. gOMP algorithm, S maximum relevant atom indices are selected from the delay domain in each iteration of the compressed sensing model. , represents the maximum number of relevant atoms selected from the delay domain at each iteration; b. Doppler domain block expansion: Based on the selected delay domain atoms, the support block range of the Doppler domain is determined by the energy threshold, focusing on the central Doppler frequency shift region; c. Angle domain burst sparse conversion: The burst sparse structure in the angle domain is converted into a traditional block sparse structure using a lifting matrix, and the starting position of the burst sparse block is determined by the maximum energy criterion; d. Joint iterative update: Combine the atom selection results in the delay domain, Doppler domain, and angle domain to update the three-dimensional atomic support set and gradually approximate the true channel response through the least squares method.

5. The joint sparse channel estimation method based on massive MIMO-OTFS according to claim 4, wherein: In said S4, the specific implementation steps of the 3D-SgOMP algorithm further include: S4.

1. Initialize the residual , 3D atomic support set and the number of iterations , let the residual , 3D atomic support set , number of iterations , set the number of atoms selected per iteration ; S4.

2. Calculate the correlation vector , and quantize the vector u into three-dimensional tensor; S4.

3. Sum the correlation vector u after 3D tensorization along the angle domain and Doppler domain to obtain the delay domain correlation vector , and select the delay domain correlation vector Center front The atomic index of the maximum value; S4.

4. Calculate the Doppler domain correlation matrix based on the selected delay domain atoms and determine the Doppler domain support block range through the energy threshold; S4.5, by improving the matrix Converting the angle domain burst sparse structure into a block sparse structure and determining the starting index of the burst sparse block; S4.

6. Update the 3D atomic support set , and estimate the channel response by the least squares method , update the residual ; S4.7, repeat the iteration until the estimated channel response Satisfy the preset sparsity , the channel estimation iterative algorithm outputs the final channel response estimation result .

6. The joint sparse channel estimation method based on massive MIMO-OTFS according to claim 5, characterized in that In S4.4, the range of the multi-spectral support block is determined by: S4.4.

1. Setting Doppler Domain Energy Threshold , the energy threshold Satisfy the following conditions: ； in, is the Doppler dimension, is the vector obtained by summing the correlation vector u along the angle domain and the delay domain after three-dimensional tensorization; S4.4.2, from Central location Start expanding and select the energy threshold that meets the above formula Conditional Doppler index block.

7. The joint sparse channel estimation method based on massive MIMO-OTFS according to claim 1, wherein In S4.5, the improvement matrix , where C represents the complex field, N t represents the number of transmitted pulses, which is equal to the number of MIMO transmit antennas. The construction of the lifting matrix L includes the following steps: S4.5.

1. The first step of the promotion matrix Columns are only The position is 1, and the rest of the positions are 0, where D is the support set of the block size, , Defined as: ； S4.5.

2. Converting Burst Sparse Correlation Vectors into Block Sparse Vectors by Lifting Matrix , and rearrange it into a matrix , to determine the starting index of the angle domain burst sparse block.

8. A computer-readable storage medium, characterized in that A computer program is stored, and when the computer program is executed by a processor, the processor performs the steps of the joint sparse channel estimation method based on massive MIMO-OTFS according to any one of claims 1 to 7.

9. Computer device, characterized in that The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the joint sparse channel estimation method based on massive MIMO-OTFS according to any one of claims 1 to 7.

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