Multi-satellite large-scale MIMO beam-based channel model and beam structure receiver design method

Abstract

The invention discloses a multi-satellite large-scale MIMO beam-based channel model and a beam structure receiver design method. In an established beam-based channel model, a spatial domain channel vector is expressed as a product of a random scalar, a beam matrix and a diffusion vector; the random scalar obeys Rician distribution; the beam matrix is composed of sampling rudder vectors corresponding to a group of direction cosine sampling points selected by a satellite, and each sampling rudder vector is called a beam; the diffusion vector is a full-real-number vector and represents the distribution of channel energy on different wave beams. And based on the beam-based channel model, each satellite designs a beam structure receiver by using statistical channel information. The beam structure receiver of each satellite is composed of a beam conversion module, a beam extraction module and each user beam domain receiver. According to the method, the uplink traversal and rate performance of the multi-satellite large-scale MIMO system can be improved, and the design and implementation complexity of each satellite uplink receiver can be effectively reduced.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology, and in particular relates to a multi-satellite large-scale MIMO beam-based channel model and a beam structure receiver design method. Background Art

[0002] In recent years, with the rapid development of terrestrial mobile communication technology, emerging industries such as mobile internet, the Internet of Things, and autonomous driving have flourished. However, in remote areas such as deserts, deep mountains, and oceans, communication network coverage still suffers from gaps, limiting the universal accessibility of global communications. To address this issue, satellite communications, with its unique advantage of wide-area coverage, has become a key technology choice for achieving global network coverage. By coordinating the use of low-, medium-, and high-orbit satellite resources, satellite communication systems can effectively offset the limitations of terrestrial communication networks, providing vast space and development opportunities for building a seamless global internet.

[0003] Massive Multiple-Input Multiple-Output (MIMO) is a core technology of fifth-generation mobile communications (5G). By deploying a large number of antennas at base stations, MIMO enables flexible configuration of dynamic beams, enabling multi-user communications using the same time-frequency resources. Extending Massive MIMO technology to mobile satellite communications enables the construction of efficient satellite communication systems, achieving higher spectral and power efficiency. This technological convergence can significantly enhance satellite communication performance, particularly in meeting the needs of large-scale terminal access.

[0004] In recent years, the global demand for seamless Internet access has surged, driving the development of giant satellite constellations. By launching hundreds or even thousands of low-orbit satellites, giant satellite constellations can not only cover remote areas that are difficult to reach with traditional communication networks, but also provide users with high-quality, multi-stream communication services through joint transmission technology. Compared with single-satellite systems, the deployment of giant satellite constellations will further improve the spectrum efficiency of satellite mobile communications, laying a solid foundation for achieving integrated space-ground networks and seamless global communication coverage. The existing receiver design dimension is usually the receiving antenna dimension. However, due to the limited computing power on the satellite side, when the number of satellite antennas increases, it is necessary to consider reducing the complexity of the receiver while ensuring the system rate performance. Summary of the Invention

[0005] Purpose of the invention: The purpose of the present invention is to provide a multi-satellite massive MIMO beam-based channel model and a beam structure receiver design method, which can effectively reduce the design and implementation complexity of each satellite uplink receiver while ensuring the system uplink rate performance.

[0006] Technical solution: To achieve the above-mentioned purpose, the present invention adopts the following technical solution:

[0007] In the first aspect, the present invention provides a multi-satellite large-scale MIMO beam-based channel model for communication between multiple satellites equipped with antenna arrays and multiple users equipped with single antennas. The beam-based channel model is established by utilizing the significant angular domain sparsity characteristics of the satellite channel, laying the foundation for the subsequent reduction of the computational complexity of the receiver. The spatial domain channel vector is represented as the product of a random scalar, a beam matrix and a diffusion vector; the random scalar obeys the Rice distribution; the beam matrix is composed of sampling rudder vectors corresponding to a set of direction cosine sampling points selected by the satellite, where each sampling rudder vector is called a beam; the diffusion vector is a vector of all real numbers, representing the distribution of channel energy on different beams.

[0008] Furthermore, the spreading vector is obtained by selecting a beam index set corresponding to each user of each satellite; the beam index set includes beam points in a diamond area around a beam point with the maximum channel energy corresponding to each user of each satellite.

[0009] Furthermore, the diffusion vector is approximately obtained by adding sampling points in the sampling interval of the direction cosines, and the diffusion vectors corresponding to each direction cosine sampling point in different intervals are obtained by cyclic shifting the diffusion vectors corresponding to the corresponding direction cosine sampling points in the first interval.

[0010] In second aspect, the present invention provides a multi-satellite massive MIMO beam structure receiver design method, comprising: each satellite uses the beam-based channel model and the statistical channel information of each user terminal to design a beam structure receiver for each user, and uses the obtained receiver to perform uplink linear reception processing; the beam structure receiver transforms the spatial domain reception signal vector on each subcarrier into a beam domain reception signal vector, extracts the beam domain reception signal vector according to the beam set of each user, and uses the beam domain receiver of each user and the extracted beam domain reception signal vector to perform linear processing on the signal of each user.

[0011] Furthermore, the beam domain receiver is obtained through closed-form calculation according to the average signal-to-interference-and-noise ratio (ASINR) criterion; the ASINR in a multi-satellite system is the ratio of the sum of the average power of the signal sent by the service satellite to the user, the average power of the signal sent to other users, and the average power of the signal sent by the non-service satellite in the signal formed by the user receiver. The beam domain receiver maximizes the ASINR of the user terminal.

[0012] Furthermore, the beam structure receiver exhibits a structure of multiplication of a beam matrix and a beam domain receiver; the closed-form expression of the beam domain receiver is as follows: the diffusion vectors of all user terminals corresponding to satellite s are multiplied by the beam matrix and its conjugate transpose, and the resulting vector is multiplied by the beam matrix and its conjugate transpose according to the user k corresponding to satellite s. s The beam set is extracted, and then the outer product of the vector obtained by multiplying it with the square root of the average channel energy of the corresponding user is summed up, and the resulting matrix is transposed by the conjugate of the beam matrix according to the corresponding user k of satellite s. s The beam set is extracted, and the matrix obtained by multiplying the extracted matrix by its conjugate transpose and the inverse of the transmit signal-to-noise ratio is added, and the inverse matrix of the obtained matrix is added to the corresponding user k of satellite s. s The diffusion vector is multiplied by the beam matrix and its conjugate transpose, and the corresponding user k is obtained according to the satellite s. s The vector obtained by multiplying the vector extracted from the beam set.

[0013] Furthermore, the calculation of the beam domain receiver only relies on real-valued matrix operations, and the product of the conjugate transpose of the beam matrix and itself can be calculated by the first column of the matrix obtained by the product of the conjugate transpose of the component beam matrices in two directions and themselves, and stored in advance; and the product of the conjugate transpose of the beam matrix and itself multiplied on the right by the diffusion vector of each user can be approximated by increasing the number of sampling points of the direction cosines, and calculated and stored in advance.

[0014] In a third aspect, the present invention provides a multi-satellite massive MIMO communication system, including satellites and user terminals, wherein the satellites or the gateway stations associated therewith implement the steps of the multi-satellite massive MIMO beam structure receiver design method.

[0015] In a fourth aspect, the present invention provides a computer program product, comprising a computer program / instruction, which, when executed by a processor, implements the steps of the multi-satellite massive MIMO beam structure receiver design method.

[0016] Beneficial effects: Compared with the existing technology, the present invention makes full use of the single-path characteristics of the spatial domain and the sparse characteristics of the angle domain of the satellite channel to carry out a multi-satellite large-scale MIMO beam-based channel model and a beam structure receiver design. Based on the proposed beam-based channel model, the proposed beam structure receiver exhibits a structure of multiplying the beam matrix by a vector that only involves real-valued matrix operations, and the real-valued matrices and vectors involved can be calculated and stored in advance, which can effectively reduce the design and implementation complexity of the receiver while ensuring the uplink transmission performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description only illustrate some embodiments of the present invention. For ordinary technicians in this field, they can also obtain drawings of other embodiments based on these drawings without paying any creative work.

[0018] Figure 1 This is a block diagram of a linear processing system for a beam structure receiver in multi-satellite massive MIMO mobile communications according to an embodiment of the present invention.

[0019] Figure 2 The figure is a flowchart of a satellite-side processing method in multi-satellite massive MIMO mobile communications according to an embodiment of the present invention.

[0020] Figure 3 The figure is a flowchart of a user terminal side processing method in multi-satellite massive MIMO mobile communications according to an embodiment of the present invention.

[0021] Figure 4 This is a comparison chart of traversal and rate performance in multi-satellite massive MIMO mobile communications according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0023] An embodiment of the present invention discloses a multi-satellite massive MIMO beam-based channel model for communication between multiple satellites equipped with antenna arrays and multiple users equipped with single antennas. The multi-satellite massive MIMO beam-based channel model represents a spatial domain channel vector as the product of a random scalar, a beam matrix, and a diffusion vector; the random scalar obeys a Rice distribution; the beam matrix is composed of sampling rudder vectors corresponding to a set of direction cosine sampling points selected by the satellite, where each sampling rudder vector is called a beam; and the diffusion vector is a vector of all real numbers, representing the distribution of channel energy on different beams.

[0024] Based on the aforementioned multi-satellite massive MIMO beam-based channel model, embodiments of the present invention further disclose a multi-satellite massive MIMO beam-structured receiver design method. This method is applied to satellites or gateways associated with satellites, where satellites are equipped with antenna arrays and communicate with user terminals equipped with single antennas within their coverage area. The method includes: each satellite utilizes the beam-based channel model and statistical channel information of each user terminal, including average channel energy and spatial angle information, to design a receiver for each user beam structure, and uses the resulting receiver to perform uplink linear reception processing.

[0025] like Figure 1 As shown in Figure 1, the beam structure receiver consists of a beam transformation module, a beam extraction module, and each user's beam domain receiver. The beam transformation module transforms the spatial domain received signal vector on each subcarrier into a beam domain received signal vector. The beam extraction module extracts the beam domain received signal vector based on each user's beam set. Finally, each user's beam domain receiver and the extracted beam domain received signal vector perform linear processing on each user's signal.

[0026] The statistical channel information is obtained from feedback information of each user or from an uplink sounding process; the feedback information of each user is the user's geographic location information, average channel energy or spatial angle information; during the uplink sounding process, each user periodically sends a sounding signal, and the satellite estimates the average channel energy and spatial angle information of each user based on the received sounding signal.

[0027] The beam domain receiver is obtained through closed-form calculation according to the average signal-to-interference-and-noise ratio (ASINR) criterion. The ASINR in a multi-satellite system is the ratio of the average power of the signal sent by the service satellite to the user, the average power of the signal sent to other users, and the average power of the signal sent by the non-service satellite in the signal formed by the user receiver. The beam domain receiver maximizes the ASINR of the user terminal. Using the channel average energy and spatial angle information, the uplink beam domain receiver can be obtained through closed-form calculation.

[0028] In a specific embodiment, the beam structure receiver exhibits a structure of multiplying a beam matrix and a beam domain receiver; the closed-form expression of the beam domain receiver is as follows: the diffusion vectors of all user terminals corresponding to satellite s are multiplied by the beam matrix and its conjugate transpose, and the resulting vector is multiplied by the beam matrix and its conjugate transpose according to the user k corresponding to satellite s. s The beam set is extracted, and then the outer product of the vector obtained by multiplying it with the square root of the average channel energy of the corresponding user is summed up, and the resulting matrix is transposed by the conjugate of the beam matrix according to the corresponding user k of satellite s. sThe beam set is extracted, and the matrix obtained by multiplying the extracted matrix by its conjugate transpose and the inverse of the transmit signal-to-noise ratio is added, and the inverse matrix of the obtained matrix is added to the corresponding user k of satellite s. s The diffusion vector is multiplied by the beam matrix and its conjugate transpose, and the corresponding user k is obtained according to the satellite s. s The vector obtained by multiplying the vector extracted from the beam set.

[0029] In an embodiment with lower complexity, the calculation of the beam domain receiver only relies on real-valued matrix operations, and the product of the conjugate transpose of the beam matrix and itself can be calculated by the first column of the matrix obtained by the product of the conjugate transpose of its component beam matrices in two directions and itself, and stored in advance; and the product of the conjugate transpose of the beam matrix and itself multiplied on the right by the diffusion vector of each user can be approximated by increasing the number of sampling points of the direction cosines, and calculated and stored in advance.

[0030] The following further introduces the method of the embodiment of the present invention in conjunction with specific implementation scenarios. The method of the present invention is not limited to specific scenarios. For other implementations outside the exemplary scenarios of the present invention, those skilled in the art can make adaptive adjustments based on the specific scenarios according to the technical ideas of the present invention and existing knowledge.

[0031] 1. System Configuration

[0032] Consider that each satellite is equipped with an antenna array (which can be a one-dimensional or two-dimensional array, with dozens to hundreds of antennas). The most basic is a two-dimensional uniform panel antenna array (UPA), that is, the antenna units are evenly arranged in the horizontal and vertical directions. Assume that each satellite is equipped with a UPA, and the number of antenna units in the x-axis and y-axis directions is M respectively. x and M y , then M=M x M y The total number of antennas equipped for the satellite. Assume that each user is equipped with a single antenna. represents the set of all n×m dimensional complex (real) matrices. The satellite set is denoted as Among them, satellite s serves K s Users form a collection Remember k s The kth user served by satellite s. The set of all served users is It can be expressed as The size of the collection is

[0033] The number of subcarriers in Orthogonal Frequency Division Multiplex (OFDM) is N c, the cyclic prefix (CP) length is N p , the system sampling time interval is T s , then the OFDM symbol time length is T c =N c T s , the CP time length is T p =N p T s . Remember N t =N c +N p .

[0034] 2. Beam-based Channel Model

[0035] After time-frequency synchronization, user k s The equivalent uplink channel impulse response to satellite s is assumed to remain constant within one OFDM symbol time and can be expressed as

[0036]

[0037] Where n and m are the nth moment and the mth discrete time delay respectively. From satellite s to user k s The number of multipath channels, λ represents the carrier wavelength, d w and M w represent the antenna spacing and number respectively. and represents direction cosines, and is the corresponding angle. Indicates the propagation delay caused by long distance. and They represent the channel gain, the Doppler shift caused by user movement, and the propagation delay caused by the scattering environment around the user. represents the array response vector, defined as

[0038]

[0039] in Defined as

[0040]

[0041] In the above formula To make The factor of conjugate symmetry, namely This central conjugate symmetry will be used to reduce the computational complexity of the beam structure receiver proposed later. sThe uplink channel on OFDM symbol p subcarrier r can be expressed as

[0042]

[0043] in For Channel length, and From satellite s to user k s The Doppler shift caused by user movement in the lth path of the channel. Assume that the channel gain follows the Rice distribution and the average channel energy is The Rice factor is We ignore the OFDM symbol and subcarrier subscripts and record is the channel on a certain subcarrier, expressed as

[0044]

[0045] Formula (5) represents the spatial domain channel on a certain subcarrier, and the random scalar obeys the Rice distribution, the vector In the subsequent beam-based channel model, it can be expressed as the product of the beam matrix and the diffusion vector. and Perform uniform sampling, and the number of sampling points can be greater than, equal to, or less than the number of antennas. Increasing the number of sampling points can increase the angular resolution, but it will increase the number of columns in the beam matrix and increase the computational complexity. The beam-based channel model is derived below. where 0≤n w ≤N w -1, and N w =F w M w and F w They are represented as the number of samples and the refined sampling factor respectively. The direction cosines in can be approximately expressed as The beam matrix is defined as

[0046]

[0047] therefore, It can be expressed as

[0048]

[0049] in is the diffusion vector. The uplink array response vector can be expressed as

[0050]

[0051] in N=N x Ny ,and remember when hour,

[0052]

[0053] where diag(ω) represents a diagonal matrix with diagonal elements of the vector ω, and Indicates N w Point DFT matrix, Since the sizes of N and M are limited, the channel energy will spread among different beams. The spreading vector can be obtained by selecting the beam index set of each satellite corresponding to each user; the beam index set includes the beam points in the diamond area around the beam point with the maximum channel energy of each satellite corresponding to each user; the distance between the four corners of the diamond and its center point can be increased or decreased. The spreading vector is defined as follows The beam index set corresponding to the element selected in is

[0054]

[0055] where γ represents the number of additionally selected beam indices near the beam with maximum energy in the x- and y-directions, and So can be approximately expressed as

[0056]

[0057] in and is the beam selection matrix, expressed as

[0058]

[0059] The uplink channel vector can be expressed as

[0060]

[0061] in is the uplink beam domain channel, We call the channel representation in the above equation a beam-based channel model. That is, the spatial channel vector is represented as the product of a random scalar, a beam matrix, and a diffusion vector. The random scalar follows a Ricean distribution, and the diffusion vector is a vector of all real numbers representing the distribution of channel energy across different beams.

[0062] The diffusion vector can be approximated by adding sampling points in the direction cosine sampling interval, and the diffusion vectors corresponding to the direction cosine sampling points in different intervals can be obtained by cyclic shifting the diffusion vectors corresponding to the direction cosine sampling points in the first interval. Perform N′ w Point sampling, that is To get more manageable Expression. For the closest The point is defined as

[0063]

[0064] use To approximate Can get

[0065]

[0066] At this time, the diffusion vector The sparse representation of can be obtained by sparse signal recovery algorithm. Can be Perform a cyclic shift to obtain

[0067]

[0068] where ((li-1))N w Represents the modulo operation.

[0069] 3. Uplink Signal Model

[0070] The received signal of satellite s on a certain subcarrier can be expressed as

[0071]

[0072] in For user k s The signal sent to satellite s, and Where p is the transmit power. s is a Gaussian asynchronous interference signal, and its covariance matrix is

[0073]

[0074] in is the Gaussian white noise variance on the satellite side.

[0075] 4. Beam Structure Receiver Design Based on Statistical Channel Information

[0076] First, equation (17) can be reformulated as

[0077]

[0078] in For user k s For the data stream sent by satellite s, its mean is 0 and its variance is 1.

[0079] Considering that each satellite uses a linear receiver to perform linear reception processing on the signal, the processed signal can be expressed as

[0080]

[0081] in is the satellite s to user k s Linear receiver of user k s The signal-to-interference-noise ratio can be expressed as

[0082]

[0083] make The maximum receiver vector can be expressed as

[0084]

[0085] The following study studies the receiver design using only statistical channel information. s The average signal-to-interference plus noise ratio (ASINR) can be expressed as

[0086]

[0087] make The maximum receiver can be expressed as

[0088]

[0089] Next, we use the established beam-based channel model to propose a beam-structured receiver design. First, we can prove that when M→∞, and When , (24) can be expressed as

[0090]

[0091] in

[0092]

[0093] and

[0094]

[0095] and

[0096] For a sufficiently large M, when the beam domain channels of any two users do not overlap, The design can be converted into a beam domain vector Due to the sparsity of beam-domain channels in massive MIMO satellite communications, It is usually much smaller than M, so the complexity of receiver design can be significantly reduced.

[0097] When the number of users increases, the beam domain channels of different users may overlap. In this case, it is necessary to add more beams in the beam structure receiver design to improve the system performance. When M→∞, define the set Its definition and Similarly, only γ in (10) is replaced by At this point, it can be proved

[0098]

[0099] When the collection Elements in When the following conditions are met, the above formula is equal.

[0100]

[0101] The following constraints in and Defined as

[0102]

[0103] ASINR can be rewritten as

[0104]

[0105] in make The maximum beam domain receiver can be calculated as

[0106]

[0107] The corresponding spatial domain receiver can be expressed as

[0108]

[0109] Notice It can be flexibly adjusted according to performance and complexity.

[0110] 5. Low-complexity design and implementation

[0111] The design complexity of the beam structure receiver comes from Equation (32), which can be rewritten as

[0112]

[0113] That is, the closed-form expression of the beam domain receiver is: by multiplying the diffusion vectors of all user terminals corresponding to satellite s by the beam matrix and its conjugate transpose, and then According to the satellite s corresponding to the user k s The beam set is extracted and then squared with the average channel energy of the corresponding user The outer products of the vectors obtained after multiplication are summed up, and the resulting matrix And the conjugate transpose of the beam matrix is calculated according to the satellite s corresponding to the user k s The beam set is extracted and the extracted matrix Multiply right by its conjugate transpose and the inverse of the transmit signal-to-noise ratio Add the matrices obtained after adding them together and get the inverse matrix of the matrix User k corresponding to satellite s s The diffusion vector is multiplied by the beam matrix and its conjugate transpose, and the corresponding user k is obtained according to the satellite s. s The vector after the beam set is extracted The vector obtained by multiplication.

[0114] First, the above formula only involves real matrix operations. According to the definition of the beam selection matrix, it is a real matrix. According to the beam matrix B w , the definition of w∈{x,y}, each column of which is conjugate symmetric, can be obtained and in

[0115]

[0116] Therefore B H B can be expressed as

[0117]

[0118] This shows that B H B is a real matrix. and is conjugate symmetric. We can get is a real vector. Therefore, The computation of involves only real matrix operations.

[0119] Because B H B is not related to the specific user and the received signal, so it can be calculated and stored in advance. Can get

[0120]

[0121] We can use Φ xThe first column of Φ x ,ie,

[0122] [Φ x ] m，m′ =[Φ x ] |m-m′|+1，1 (38)

[0123] Similarly, Therefore B H B can be represented by Φ x and Φ y The first column of

[0124]

[0125] To get B H B, we only need to calculate [Φ x ] ：，1 and [Φ y ] ：，1 , whose computational and storage complexities are and Notice Can be Get, ie, Real vector It can be expressed as

[0126]

[0127] in and Expressed as

[0128]

[0129] Therefore, calculate The computational complexity is The storage complexity is remember The average value of therefore The design complexity is In contrast, the spatial domain receiver The design complexity is

[0130] Using the designed beam structure receiver, the received signal can be rewritten as

[0131]

[0132] in Its implementation complexity is The complexity of the spatial domain receiver to generate the transmitted signal is

[0133] When calculating the total complexity, which is the sum of the design complexity and the implementation complexity, we consider that the uplink receiver is designed once for 100 subframes and 4 resource blocks and used to generate the transmit signal. Assume that there are 14 OFDM symbols in a subframe and 12 subcarriers in a resource block. Therefore, the total complexity of the beam structure uplink receiver can be calculated as

[0134] The total complexity of the spatial domain receiver is

[0135] Figure 4 The uplink traversal and rate performance of the proposed method (ASINR_Beam) in this embodiment, the baseline method for satellites to generate DFT beams using position information, the spatial domain receiver, and the beam structure receiver are given. Figure 4 It can be seen that the performance of the beam structure receiver can approach that of the spatial domain receiver, and compared with the baseline method based on DFT beam, it has greatly improved the traversal reachability and rate performance.

[0136] An embodiment of the present invention also discloses a multi-satellite massive MIMO communication system, including satellites and user terminals. The satellites or the gateway stations associated with the satellites implement the steps of the multi-satellite massive MIMO beam structure receiver design method.

[0137] An embodiment of the present invention further discloses a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the multi-satellite massive MIMO beam structure receiver design method.

[0138] Anything not described in detail in the present invention is well known to those skilled in the art.

[0139] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A multi-satellite massive MIMO beam-based channel model for communication between multiple satellites equipped with antenna arrays and multiple users equipped with single antennas, characterized by: The spatial domain channel vector is represented as the product of a random scalar, a beam matrix, and a diffusion vector; the random scalar obeys the Rice distribution; the beam matrix is composed of sampling rudder vectors corresponding to a set of direction cosine sampling points selected by the satellite, where each sampling rudder vector is called a beam; the diffusion vector is a vector of all real numbers, representing the distribution of channel energy on different beams.

2. The multi-satellite massive MIMO beam-based channel model according to claim 1, wherein: The spreading vector is obtained by selecting a beam index set corresponding to each user of each satellite; the beam index set includes beam points in a diamond area around a beam point with the maximum channel energy corresponding to each user of each satellite.

3. The multi-satellite massive MIMO beam-based channel model according to claim 1, wherein: The diffusion vector is approximately obtained by adding sampling points in the sampling interval of the direction cosines, and the diffusion vectors corresponding to the direction cosine sampling points in different intervals are obtained by cyclically shifting the diffusion vectors corresponding to the corresponding direction cosine sampling points in the first interval.

4. The multi-satellite massive MIMO beam-based channel model according to claim 1, wherein: The sampling of direction cosines is uniform sampling, and the number of sampling points is greater than, equal to, or less than the number of antennas.

5. A multi-satellite massive MIMO beam structure receiver design method, characterized in that: include: Each satellite uses the beam-based channel model according to any one of claims 1 to 4 and the statistical channel information of each user terminal to design a beam structure receiver for each user, and uses the obtained receiver to perform uplink linear reception processing; The beam structure receiver transforms the spatial domain received signal vector on each subcarrier into a beam domain received signal vector, extracts the beam domain received signal vector according to the beam set of each user, and linearly processes the signal of each user using the beam domain receiver of each user and the extracted beam domain received signal vector.

6. The multi-satellite massive MIMO beam structure receiver design method according to claim 5, characterized in that: The beam domain receiver is obtained through closed-form calculation according to the average signal-to-interference-and-noise ratio (ASINR) criterion. The ASINR in a multi-satellite system is the ratio of the sum of the average power of the signal sent by the service satellite to the user, the average power of the signal sent to other users, and the average power of the signal sent by non-service satellites in the signal formed by the user receiver. The beam domain receiver maximizes the ASINR of the user terminal.

7. The multi-satellite massive MIMO beam structure receiver design method according to claim 6, characterized in that: The beam structure receiver shows a structure of multiplying the beam matrix and the beam domain receiver; the closed form expression of the beam domain receiver is as follows: the diffusion vectors of all user terminals corresponding to satellite s are multiplied by the beam matrix and its conjugate transpose, and the resulting vector is multiplied by the beam matrix and its conjugate transpose according to the user k corresponding to satellite s. s The beam set is extracted, and then the outer product of the vector obtained by multiplying it with the square root of the average channel energy of the corresponding user is summed up, and the resulting matrix is transposed by the conjugate of the beam matrix according to the corresponding user k of satellite s. s The beam set is extracted, and the matrix obtained by multiplying the extracted matrix by its conjugate transpose and the inverse of the transmit signal-to-noise ratio is added, and the inverse matrix of the obtained matrix is added to the corresponding user k of satellite s. s The diffusion vector is multiplied by the beam matrix and its conjugate transpose, and the corresponding user k is obtained according to the satellite s. s The vector obtained by multiplying the vector extracted from the beam set.

8. The multi-satellite massive MIMO beam structure receiver design method according to claim 7, characterized in that: The beam structure receiver exhibits a structure of multiplication of a beam matrix and a beam domain receiver; the calculation of the beam domain receiver only relies on real-valued matrix operations, and the product of the conjugate transpose of the beam matrix and itself can be calculated by the first column of the matrix obtained by the product of the conjugate transpose of its component beam matrices in two directions and itself, and stored in advance; and the product of the conjugate transpose of the beam matrix and itself multiplied right by the diffusion vector of each user can be approximated by increasing the number of sampling points of the direction cosines, and calculated and stored in advance.

9. A multi-satellite massive MIMO communication system, comprising satellites and user terminals, characterized in that: The satellite or the gateway station associated therewith implements the steps of the multi-satellite massive MIMO beam structure receiver design method according to any one of claims 5-8.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method for designing a multi-satellite massive MIMO beam structure receiver are implemented according to any one of claims 5 to 8.

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