Multi-star massive MIMO beam-based channel model and beam structure receiver design method

Abstract

The application discloses a multi-satellite large-scale MIMO beam-based channel model and a beam structure receiver design method. In the beam-based channel model established by the application, 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 a Rice distribution; the beam matrix is composed of sampling rudder vectors corresponding to a group of direction cosine sampling points selected by a satellite, wherein each sampling rudder vector is called a beam; and the diffusion vector is a full-real number vector, representing the distribution of channel energy on different beams. Based on the beam-based channel model, each satellite utilizes statistical channel information to design a beam structure receiver. The beam structure receiver of each satellite is composed of a beam transformation module, a beam extraction module and a user beam domain receiver. The application can guarantee the improvement of uplink ergodic and rate performance of the multi-satellite large-scale MIMO system, and effectively reduce the design and implementation complexity of the uplink receiver of each satellite.

Description

Technical Field

[0001] This invention belongs to the field of communication technology, specifically relating to a multi-satellite massive MIMO beam-based channel model and a beam structure receiver design method. Background Technology

[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 shown a booming trend. However, in remote areas such as deserts, mountains, and oceans, communication network coverage still has blind spots, limiting the widespread adoption of global communication. To solve this problem, satellite communication, 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-Earth orbit, medium-Earth orbit, and high-Earth orbit satellite resources, satellite communication systems can effectively compensate for the limitations of terrestrial communication networks, providing ample space and development opportunities for building a seamless global network.

[0003] Massive Multiple-Input Multiple-Output (MIMO) is one of the core technologies of fifth-generation mobile communication (5G). It utilizes a large number of antennas deployed at base stations to achieve flexible dynamic beam configuration, enabling multi-user communication under the same time-frequency resources. Extending MIMO technology to satellite mobile communication can build highly efficient satellite communication systems, achieving higher spectral and power efficiency. This technological convergence can significantly improve the performance of satellite communication, particularly demonstrating significant value in meeting the needs of large-scale terminal access.

[0004] In recent years, the surge in global demand for seamless internet access has driven the development of mega-satellite constellations. By launching hundreds or even thousands of low-Earth orbit satellites, mega-satellite constellations can not only cover remote areas that are difficult for traditional communication networks to reach, but also provide users with high-quality, multi-stream communication services through joint transmission technologies. Compared to single satellite systems, the deployment of mega-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. Existing receiver designs are typically based on the receiver antenna dimension; however, due to limited satellite-side computing power, as the number of satellite antennas increases, it is necessary to consider reducing receiver complexity while ensuring system rate performance. Summary of the Invention

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

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

[0007] In a first aspect, this invention provides 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 beam-based channel model is established by utilizing the significant angular domain sparsity of the satellite channel, laying the foundation for reducing the computational complexity of the receiver. The spatial domain channel vector is represented as the product of a random scalar and a beam matrix and a spread vector; the random scalar follows a Ricean distribution; the beam matrix consists of sampling rudder vectors corresponding to a set of selected direction cosine sampling points of the satellite, where each sampling rudder vector is called a beam; the spread vector is a vector of all real numbers, representing the distribution of channel energy on different beams.

[0008] Furthermore, the diffusion vector is selected based on the beam index set of each user corresponding to each satellite; the beam index set includes beam points in a diamond-shaped region around the beam point with the highest channel energy for each user corresponding to each satellite.

[0009] Furthermore, the diffusion vector is approximated by adding sampling points within the sampling interval of the direction cosine. The diffusion vector corresponding to each direction cosine sampling point in different intervals is obtained by cyclically shifting the diffusion vector corresponding to the corresponding direction cosine sampling point in the first interval.

[0010] Secondly, the present invention provides a design method for a multi-satellite massive MIMO beamform receiver, comprising: each satellite designing a beamform receiver for each user using the beam-based channel model and statistical channel information of each user terminal, and performing uplink linear reception processing using the obtained receiver; the beamform 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 performs linear processing on the signal of each user using the beam domain receiver of each user and the extracted beam domain received signal vector.

[0011] Furthermore, the beam domain receiver is calculated in a closed loop according to the average signal-to-interference-plus-noise ratio (ASINR) criterion. In a multi-satellite system, ASINR is the ratio of the average power of the signal transmitted from the serving satellite to the user in the signal generated by the user receiver to the sum of the average power of the signal transmitted to other users and the average power of the signal transmitted from the non-serving satellite. The beam domain receiver maximizes the ASINR of the user terminal.

[0012] Furthermore, the beamform receiver exhibits a structure that multiplies the beam matrix with a beam domain receiver; the closed-form expression of the beam domain receiver is achieved by left-multiplying the spread vectors of all user terminals corresponding to satellite s by the beam matrix and its conjugate transpose, and then applying the resulting vector according to the corresponding user k of satellite s. s The beam set is extracted, and the sum of the outer products of the vectors obtained by multiplying the beam set by the square root of the average channel energy of the corresponding user is calculated. The resulting matrix is ​​then combined with the conjugate transpose of the beam matrix according to the satellite s and the corresponding user k. s The beam set is extracted, and the extracted matrix is ​​right-multiplied by its conjugate transpose and the reciprocal of the transmitted signal-to-noise ratio, and then the resulting matrix is ​​added. The inverse of the resulting matrix is ​​then compared with the matrix corresponding to satellite s and user k. s The diffusion vector is multiplied by the beam matrix and its conjugate transpose, and then multiplied by the corresponding user k according to satellite s. s The vector obtained by multiplying the vectors extracted from the beam set.

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

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

[0015] Fourthly, the present invention provides 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.

[0016] Beneficial effects: Compared with the prior art, the present invention makes full use of the spatial domain single-path characteristics and angular domain sparsity characteristics of satellite channels to carry out multi-satellite large-scale MIMO beam-based channel model and 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 with a vector that only involves real-valued matrix operations. Moreover, the real-valued matrix and vector involved can be calculated and stored in advance, which can effectively reduce the design and implementation complexity of the receiver while ensuring uplink transmission performance. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below only show some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained from these drawings without creative effort.

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

[0019] Figure 2 This is a schematic diagram of the satellite-side processing method in multi-satellite massive MIMO mobile communication according to an embodiment of the present invention.

[0020] Figure 3 This is a schematic diagram of the user terminal-side processing method in multi-satellite massive MIMO mobile communication 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 communication according to an embodiment of the present invention. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0023] This 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 the spatial domain channel vector as the product of a random scalar, a beam matrix, and a spread vector. The random scalar follows a Ricean distribution. The beam matrix consists of sampling rudder vectors corresponding to a set of selected direction cosine sampling points of the satellite, where each sampling rudder vector is called a beam. The spread vector is a vector of all real numbers, representing the distribution of channel energy across different beams.

[0024] Based on the aforementioned multi-satellite massive MIMO beam-based channel model, this invention also discloses a design method for a multi-satellite massive MIMO beam-structure receiver. This method is applied to a satellite or a gateway station connected to a satellite, wherein the satellite is configured with an antenna array and communicates with user terminals within its coverage area, which are configured with single antennas. The method includes: each satellite designing a user beam-structure receiver using the beam-based channel model and statistical channel information from each user terminal, including average channel energy and spatial angle information; and using the obtained receivers for uplink linear reception processing.

[0025] like Figure 1 As shown, the beamformer receiver consists of a beam conversion module, a beam decimation module, and beam domain receivers for each user. The beam conversion module transforms the spatial domain received signal vectors on each subcarrier into beam domain received signal vectors. The beam decimation module decimates the beam domain received signal vectors according to the beam sets of each user. Finally, the beam domain receivers for each user and the decimated beam domain received signal vectors are used to perform linear processing on the signals of each user.

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

[0027] The beam domain receiver is calculated using a closed-form formula based on the average signal-to-interference-plus-noise ratio (ASINR) criterion. In a multi-satellite system, the ASINR is the ratio of the average power of the signal transmitted from the serving satellite to the user to the sum of the average power of the signal transmitted to other users and the average power of the signal transmitted from the non-serving satellite in the signal generated by the user receiver. The beam domain receiver maximizes the ASINR of the user terminal. The uplink beam domain receiver can be calculated using the channel average energy and spatial angle information.

[0028] In one specific embodiment, the beamform receiver exhibits a structure that multiplies the beam matrix with a beam domain receiver; the closed-form expression of the beam domain receiver is achieved by left-multiplying the spread vectors of all user terminals corresponding to satellite s by the beam matrix and its conjugate transpose, and then applying the resulting vectors according to the corresponding user k of satellite s. s The beam set is extracted, and the sum of the outer products of the vectors obtained by multiplying the beam set by the square root of the average channel energy of the corresponding user is calculated. The resulting matrix is ​​then combined with the conjugate transpose of the beam matrix according to the satellite s and the corresponding user k. sThe beam set is extracted, and the extracted matrix is ​​right-multiplied by its conjugate transpose and the reciprocal of the transmitted signal-to-noise ratio, and then the resulting matrix is ​​added. The inverse of the resulting matrix is ​​then compared with the matrix corresponding to satellite s and user k. s The diffusion vector is multiplied by the beam matrix and its conjugate transpose, and then multiplied by the corresponding user k according to satellite s. s The vector obtained by multiplying the vectors extracted from the beam set.

[0029] In a less complex embodiment, the calculation of the beam domain receiver relies solely on real-valued matrix operations, where the product of the conjugate transpose of the beam matrix and itself can be calculated from the first column of the matrix obtained by multiplying the conjugate transposes of the component beam matrices in both directions by themselves, and stored in advance; and the product of the conjugate transpose of the beam matrix and itself multiplied by the spread vectors of each user can be approximated by increasing the number of sampling points of the direction cosine, and calculated and stored in advance.

[0030] The method of the present invention will be further described below with reference to specific implementation scenarios. The method of the present invention does not limit the specific scenario. For other implementations outside the exemplary scenario of the present invention, those skilled in the art can make adaptive adjustments based on the technical ideas of the present invention and existing knowledge according to the specific scenario.

[0031] I. System Configuration

[0032] Consider that each satellite is equipped with an antenna array (which can be one-dimensional or two-dimensional, with tens to hundreds of antennas). The most basic is a two-dimensional uniform planar array (UPA), where the antenna elements are uniformly arranged horizontally and vertically. Assume each satellite is equipped with a UPA, with M antenna elements along the x-axis and y-axis respectively. x and M y Then M = M x M y The total number of antennas provided for the satellite. Assume each user has a single antenna. (Note: The original text contains some inconsistencies and unclear formatting. A more accurate translation would require the full context.) Let represent the set of all n×m dimensional complex (real) matrices. Let the set of satellites be denoted as . Among them, satellite s serves K s A set of users Let k be the number of k. s Let the k-th user serving satellite s be denoted as . Let the set of all users served be denoted as . It can be represented as The size of the set is

[0033] Let N be the number of subcarriers in Orthogonal Frequency Division Multiplexing (OFDM). cThe length of the cyclic prefix (CP) 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 Let N be the number of digits. t =N c +N p .

[0034] II. 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 over one OFDM symbol time and can be expressed as follows:

[0036]

[0037] Where n and m are the nth time moment and the mth discrete time delay, respectively. For satellite s to user k s The number of multipath paths in the channel, λ represents the carrier wavelength, d w and M w These represent the antenna spacing and number, respectively. and Indicates direction cosine. and For the corresponding angle. This indicates the propagation delay caused by long distances. and These represent the channel gain, the Doppler shift caused by user movement, and the propagation delay caused by the scattering environment around the user, respectively. The array response vector is defined as follows:

[0038]

[0039] in Defined as

[0040]

[0041] In the above formula In order to make The factor of conjugate symmetry, i.e. This central conjugate symmetry will be used to reduce the computational complexity of the proposed beamformer receiver. (Satellite s to user k) sThe uplink channel on OFDM symbol p subcarrier r can be represented as:

[0042]

[0043] in For channel The length, and For satellite s to user k s The Doppler shift caused by user movement in the l-th path of the channel. Assume the channel gain follows a Ricean distribution and the average channel energy is... Rice factor is We ignore the OFDM symbol and subcarrier subscripts, and denote... For a channel on a certain subcarrier, it is represented as

[0044]

[0045] Equation (5) represents the spatial domain channel on a certain subcarrier, where the random scalar follows a Rice distribution, and the vector... In subsequent beam-based channel models, this can be represented as the product of the beam matrix and the spread vector. This is achieved by adjusting the direction cosine. and Uniform sampling is performed, 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 angular resolution, but it also increases the number of columns in the beam matrix, increasing 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 These represent the number of samples and the refinement sampling factor, respectively. The direction cosine in can be approximated as: Beam matrix is ​​defined as

[0046]

[0047] therefore, It can be represented as

[0048]

[0049] in Let be the diffusion vector. The uplink array response vector can be represented 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 vector ω, and N represents w Point DFT matrix, Since N and M are finite, channel energy will spread between different beams. The spread vector can be obtained by selecting from the beam index set for each user corresponding to each satellite; the beam index set contains beams within a rhombus-shaped region surrounding the beam with the highest channel energy for each user corresponding to each satellite; specifically, the distances from the four corners of the rhombus to its center point can be increased or decreased. The spread vector is defined below. The set of beam indices corresponding to the selected elements is

[0054]

[0055] Where γ represents the number of additional beam indices selected near the beam with the maximum energy in the x and y directions, and So It can be approximated as

[0056]

[0057] in and The beam selection matrix is ​​represented as follows:

[0058]

[0059] The uplink channel vector can be represented as

[0060]

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

[0062] The diffusion vector can be approximated by adding sampling points within the sampling interval of the direction cosines, and the diffusion vector corresponding to each direction cosine sampling point in different intervals can be obtained by cyclically shifting the diffusion vector corresponding to the corresponding direction cosine sampling point in the first interval. Below, we will use the i-th sampling interval... Perform N′ w Point sampling, i.e. To get easier to handle The expression. Remember. For the closest The point is defined as

[0063]

[0064] use To approximate It can be obtained

[0065]

[0066] At this time, the diffusion vector The sparse representation of the signal can be obtained through the sparse signal recovery algorithm. It can be by This is obtained by performing a cyclic shift, i.e.

[0067]

[0068] Where ((li-1))N w This indicates a modulo operation.

[0069] III. 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 is transmitted to satellite s, and Where p is the transmission power. s The signal is a Gaussian asynchronous interference signal, and its covariance matrix is...

[0073]

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

[0075] IV. Beamstructure Receiver Design Based on Statistical Channel Information

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

[0077]

[0078] in For user k s The data stream transmitted to satellite s has a mean of 0 and a variance of 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 For satellite s to user k s A linear receiver. User k s The signal-to-interference-plus-noise ratio can be expressed as

[0082]

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

[0084]

[0085] The following study examines receiver design utilizing only statistical channel information. User k s The average signal-to-interference-plus-noise ratio (ASINR) can be expressed as:

[0086]

[0087] make The largest receiver can be represented as

[0088]

[0089] The following section uses the established beam-based channel model to propose a beamform receiver design. First, it can be proven that as 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. The design. Due to the sparsity of the beam domain channel in massive MIMO satellite communication, It is usually much smaller than M, thus significantly reducing the complexity of receiver design.

[0097] As the number of users increases, the beam domain channels of different users may overlap. In this case, more beams need to be added to the beamform receiver design to improve system performance. When M→∞, define the set... Its definition and Similarly, simply replace γ in (10) with At this point, it can be proven

[0098]

[0099] When set elements in The above equation is valid if the following conditions are met.

[0100]

[0101] The following constraints in and Defined as

[0102]

[0103] ASINR can be rewritten as

[0104]

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

[0106]

[0107] The corresponding space domain receiver can be represented as

[0108]

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

[0110] V. Low-complexity design and implementation

[0111] The design complexity of a beamformer 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 left-multiplying the spread vectors of all user terminals corresponding to satellite s by the beam matrix and its conjugate transpose, and then applying the resulting vector... According to satellite s and corresponding user k s The beam set is extracted and then the square root of the average channel energy of the corresponding user is taken. Summing the outer product of the vectors obtained after multiplication, and then applying the result to the matrix The conjugate transpose of the beam matrix is ​​determined based on the satellite s and the corresponding user k. s The beam set is extracted, and the extracted matrix is... Right-multiply by its conjugate transpose and the reciprocal of the transmitted signal-to-noise ratio. The resulting matrices are then added together, and the inverse of the resulting matrix is ​​obtained. User k corresponding to satellite s s The diffusion vector is multiplied by the beam matrix and its conjugate transpose, and then multiplied by the corresponding user k according to satellite s. s The vector after beam set extraction The vector obtained by multiplication.

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

[0115]

[0116] Therefore B H B can be represented as

[0117]

[0118] This indicates that B H B is a real matrix. Because and It is conjugate symmetric. We can obtain... It is a real vector. Therefore, The calculations only involve real number matrix operations.

[0119] Because of B H B is independent of the specific user and the received signal, therefore it can be calculated and stored in advance. It can be obtained

[0120]

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

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

[0123] Similarly, Therefore B H B can be derived from Φ x and Φ y The first column is obtained, that is

[0124]

[0125] In order to obtain B H B, we only need to calculate [Φ x ] ：，1 and [Φ y ] ：，1 Its computational and storage complexities are respectively and Note It can be by Get, ie, Real vector It can be represented as

[0126]

[0127] in and Represented as

[0128]

[0129] Therefore, calculation The computational complexity is The storage complexity is remember The average value is therefore The design complexity is In contrast, space domain receivers The design complexity is

[0130] Using a pre-designed beamform receiver, the received signal can be rewritten as

[0131]

[0132] in Its implementation complexity is The complexity of a space-domain receiver generating a transmitted signal is...

[0133] When calculating the total complexity, which is the sum of design and implementation complexity, we consider that the uplink receiver is designed once across 100 subframes and 4 resource blocks, and used to generate the transmitted signal. Assume one subframe has 14 OFDM symbols and one resource block has 12 subcarriers. Therefore, the total complexity of the beamformed uplink receiver can be calculated as follows:

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

[0135] Figure 4 The method (ASINR_Beam) proposed in this embodiment, the baseline method for generating DFT beams using satellite position information, the space domain receiver, and the uplink ergodic and rate performance of the beam structure receiver are presented. From Figure 4 It can be seen that the performance of the beamform receiver can approach that of the space domain receiver, and compared with the baseline method based on DFT beamform, it has a significant improvement in ergonomic reachability and rate performance.

[0136] This invention also discloses a multi-satellite massive MIMO communication system, including satellites and user terminals, wherein the satellites or their associated gateways implement the steps of the multi-satellite massive MIMO beam structure receiver design method.

[0137] This invention also 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] Any aspects of this invention not described in detail are 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 variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope 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 in that: The spatial domain channel vector is represented as the product of a random scalar and a beam matrix and a spread vector; the random scalar follows a Rice distribution; the beam matrix consists 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 spread 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, characterized in that, The diffusion vector is selected based on the beam index set of each user corresponding to each satellite; the beam index set includes the beam points in the diamond-shaped region around the beam point with the highest channel energy for each user corresponding to each satellite.

3. The multi-satellite massive MIMO beam-based channel model according to claim 1, characterized in that, The diffusion vector is approximated by adding sampling points within the sampling interval of the direction cosine. The diffusion vector corresponding to each direction cosine sampling point in different intervals is obtained by cyclically shifting the diffusion vector corresponding to the corresponding direction cosine sampling point in the first interval.

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

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

6. The design method for a multi-satellite massive MIMO beamformer receiver according to claim 5, characterized in that, The beam domain receiver is calculated in a closed loop according to the average signal-to-interference-plus-noise ratio (ASINR) criterion. In a multi-satellite system, ASINR is the ratio of the average power of the signal transmitted from the serving satellite to the user in the signal generated by the user's beam structure receiver to the sum of the average power of the signal transmitted to other users and the average power of the signal transmitted from non-serving satellites. The beam domain receiver maximizes the ASINR of the user terminal.

7. The design method for a multi-satellite massive MIMO beamformer receiver according to claim 6, characterized in that, The closed-form expression for the beam domain receiver is as follows: by analyzing the satellite... The spread vectors of all corresponding user terminals are left-multiplied by the beam matrix and its conjugate transpose, and the resulting vectors are then processed according to the satellite... Relevant users The beam set is extracted, and then the square root of the average channel energy of the corresponding user is multiplied to obtain the outer product of the vector. The resulting matrix is ​​then summed with the conjugate transpose of the beam matrix according to the satellite... Relevant users The beam set is extracted, and the extracted matrix is ​​right-multiplied by its conjugate transpose and the reciprocal of the transmitted signal-to-noise ratio, then the resulting matrix is ​​added together. The inverse of the resulting matrix is ​​then combined with the satellite beam set. Relevant users The diffusion vector is multiplied by the beam matrix and its conjugate transpose, and then multiplied by the satellite. Relevant users The vector obtained by multiplying the vectors extracted from the beam set.

8. The design method for a multi-satellite massive MIMO beamformer receiver according to claim 7, characterized in that, The calculation of the beam domain receiver relies solely on real-valued matrix operations. The product of the conjugate transpose of the beam matrix and itself can be calculated from the first column of the matrix obtained by multiplying the conjugate transposes of the component beam matrices in both directions by themselves, and stored in advance. Furthermore, the product of the conjugate transpose of the beam matrix and itself multiplied by the diffusion vector of each user can be approximated by increasing the number of sampling points of the direction cosine, 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 its associated gateway station implements the steps of the multi-satellite massive MIMO beamform 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 the processor, they implement the steps of the multi-satellite massive MIMO beamformer receiver design method according to any one of claims 5-8.

Images