Satellite massive-mimo beam structure downlink precoding method and uplink detection method

Abstract

The application discloses a satellite large-scale MIMO beam structure downlink precoding method and uplink detection method. In the beam base channel model established by the application, the scaling direction cosine is uniformly sampled and quantized, and the uplink / downlink beam matrix is constructed, the space domain channel vector is expressed as the product of the equivalent uplink / downlink channel gain, the uplink / downlink beam matrix and the diffusion vector. Based on the downlink / uplink beam base channel model, the statistical channel information is used to design the downlink precoder / uplink detector under the beam structure, and the high-dimensional space domain precoder / detector design is converted into the low-dimensional beam domain precoder / detector design. Using the designed precoder and detector, low-complexity downlink precoding transmission processing and uplink receiving processing are respectively carried out. The application fully utilizes the sparse characteristics of the satellite channel, guarantees the uplink and downlink ergodic and rate performance of the satellite large-scale MIMO system, and effectively reduces the design and implementation complexity of the precoder and the detector.

Description

Technical Field

[0001] This invention belongs to the field of communication technology, specifically relating to a downlink precoding method and an uplink detection method for a satellite massive MIMO beam structure. Background Technology

[0002] Satellite communication, with its wide coverage, lack of geographical limitations, and low dependence on infrastructure, has become an important means of bridging the global digital divide. In recent years, with the proposal and implementation of satellite constellation projects such as OneWeb and Starlink, and the rise of technologies like digital twins and artificial intelligence, satellite communication has ushered in a new development boom. Low Earth Orbit (LEO) satellites have significant advantages over medium Earth Orbit (MEO) and geostationary orbit (GEO) satellites in terms of end-to-end latency, path loss, power consumption, and deployment costs.

[0003] Massive Multiple-Input Multiple-Output (MIMO) is one of the core technologies of fifth-generation mobile communication (5G). Extending MIMO to satellite mobile communication, enabling flexible dynamic beamforming, and constructing MIMO satellite mobile communication systems has significant theoretical and practical implications for improving the capabilities of satellite communication systems. In the future, satellite communication will be more closely integrated with terrestrial 5G networks, forming a unified space-ground network architecture and providing more seamless global coverage. Furthermore, with the development of 5G technology, satellite communication will further enhance its capabilities in high-speed data transmission and low-latency communication, providing higher-quality communication services to users worldwide.

[0004] In recent years, an increasing number of studies have applied massive MIMO to satellite communications. However, the precoder and detector designs used in these studies for massive MIMO LEO satellite communications typically involve complex signal processing, including operations such as large-dimensional matrix inversion, resulting in high design and implementation complexity, which may be difficult to implement on spaceborne payloads. Furthermore, many existing studies have not fully considered and utilized the sparsity characteristics of LEO satellite channels in system modeling and design. Summary of the Invention

[0005] Purpose of the invention: The purpose of this invention is to provide a downlink precoding method and an uplink detection method for satellite large-scale MIMO beam structures, which effectively reduces the design and implementation complexity of satellite downlink precoders and uplink detectors while ensuring system ergodicity and rate performance.

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

[0007] In a first aspect, the present invention provides a downlink precoding method for a satellite massive MIMO beam structure for communication between a satellite equipped with an antenna array and multiple users equipped with single antennas, comprising the following steps:

[0008] The scaling direction cosine is uniformly sampled and quantized to construct a downlink beam matrix. The spatial domain channel vector is represented as the product of the equivalent downlink channel gain, the downlink beam matrix, and the spread vector. The spread vector represents the energy on different beams. The sampling range of the scaling direction cosine is between positive and negative twice the ratio of the antenna spacing to the carrier wavelength. The downlink beam matrix is ​​a standard DFT matrix multiplied by a block diagonal-zero matrix on the left and right, respectively.

[0009] Beam selection is performed on the spread vector to obtain the downlink beam selection matrix;

[0010] Based on the downlink beam matrix, downlink beam selection matrix, and statistical channel information obtained from each user terminal, the satellite side designs a beam-structured downlink precoder for transmitting signals to each user terminal. The beam-structured downlink precoder has a structure that multiplies the beam selection beam matrix and the low-dimensional beam domain precoder, transforming the high-dimensional spatial domain precoder design into a low-dimensional beam domain precoder design. The closed-form expression of the beam domain precoder involves only real-valued operations, and some calculations can be quickly implemented using DFT.

[0011] The designed beamform precoder is used for low-complexity downlink precoding transmission processing; the downlink transmission signal is represented as the sum of the products of the beamform precoder of all user terminals and the transmission signal, and can be quickly calculated using DFT through the relationship between Kronecker product and vectorization.

[0012] Furthermore, each column of the downlink beam matrix is ​​a sampling direction vector, and the independent variable of the sampling direction vector is a uniformly sampled and quantized scaling direction cosine. Each sampling direction vector represents a satellite side beam.

[0013] Furthermore, the downlink beam selection matrix is ​​obtained based on the downlink beam index set of each user; the downlink beam selection matrix ignores beam points with approximately 0 energy in the spread vector of each user, and only retains beam points in the diamond-shaped region around the beam point with the highest downlink channel energy of each user.

[0014] Furthermore, the beam domain precoder is calculated in a closed loop according to the average signal leakage-to-noise ratio (ASLNR) criterion; ASLNR is the ratio of the average signal power transmitted by the satellite to the target user to the sum of the average interference power and average noise power leaked to other users, and the beam domain precoder maximizes the ASLNR of each user terminal. The beamform precoder exhibits a structure that multiplies the downlink beam matrix with the beam domain precoder. The closed-form expression of the beam domain precoder is as follows: the vector obtained by multiplying the spread vector of all user terminals sequentially by the downlink beam matrix, the conjugate transpose of the downlink beam matrix, and the downlink beam selection matrix, and then multiplying the resulting vector by the square root of the vector and the average channel energy of the corresponding user, summing the outer product of the resulting vector, multiplying the resulting matrix by the downlink beam selection matrix sequentially by the downlink beam matrix, the conjugate transpose of the downlink beam matrix, the conjugate transpose of the downlink beam selection matrix, and the reciprocal of the downlink signal-to-noise ratio, and then adding the resulting matrix. The inverse of the resulting matrix is ​​multiplied by the vector obtained by multiplying the spread vector of the user terminals sequentially by the downlink beam matrix, the conjugate transpose of the downlink beam matrix, and the downlink beam selection matrix, and then energy normalizing the resulting vector.

[0015] Secondly, the present invention provides a satellite massive MIMO beam structure uplink detection method for communication between a satellite equipped with an antenna array and multiple users equipped with single antennas, comprising the following steps:

[0016] The scaling direction cosine is uniformly sampled and quantized to construct an uplink beam matrix. The spatial domain channel vector is represented as the product of the equivalent uplink channel gain, the uplink beam matrix, and the spread vector. The spread vector represents the energy on different beams. The sampling range of the scaling direction cosine is between positive and negative twice the ratio of the antenna spacing to the carrier wavelength. The uplink beam matrix is ​​a standard DFT matrix multiplied by a block diagonal-zero matrix on the left and right, respectively.

[0017] Beam selection is performed on the spread vector to obtain the uplink beam selection matrix.

[0018] Based on the uplink beam matrix, uplink beam selection matrix, and statistical channel information obtained from each user terminal, the satellite side designs uplink detectors for each user's beam structure. These uplink detectors have a structure that multiplies the beam selection matrix with a low-dimensional beam domain detector, transforming the design of a high-dimensional spatial domain detector into a low-dimensional beam domain detector design. The closed-form expression of the beam domain detector involves only real-valued operations, and some calculations can be quickly implemented using DFT.

[0019] The designed beamform detector is used for low-complexity uplink reception processing. The uplink recovered signal is represented as the product of the beamform detector and the received signal, and can be quickly calculated using DFT through the relationship between the Kronecker product and vectorization.

[0020] Furthermore, each column of the uplink beam matrix is ​​a sampling direction vector, and the independent variable of the sampling direction vector is a uniformly sampled and quantized scaling direction cosine. Each sampling direction vector represents a satellite side beam.

[0021] Furthermore, the uplink beam selection matrix is ​​obtained based on the uplink beam index set of each user; the uplink beam selection matrix ignores beam points with approximately 0 energy in the spread vector of each user, and only retains beam points in the diamond-shaped region around the beam point with the largest uplink channel energy of each user.

[0022] Furthermore, the beam domain detector is calculated in a closed loop according to the average signal-to-interference-plus-noise ratio (ASINR) criterion. ASINR is the ratio of the average signal power of the target user's transmitted signal to the sum of the average signal power and average noise power of the signals transmitted by other users in the signal generated by the detector. The beam domain detector maximizes the ASINR of each user terminal. The beam structure detector exhibits a structure that multiplies the uplink beam matrix with the beam domain detector. The closed-form expression of the beam domain detector is as follows: by multiplying the spread vectors of all user terminals sequentially by the uplink beam matrix, the conjugate transpose of the uplink beam matrix, and the uplink beam selection matrix, the resulting vector is multiplied by the square root of the vector and the average channel energy of the corresponding user, and the resulting matrix is ​​summed. The resulting matrix is ​​then multiplied by the uplink beam selection matrix sequentially by the uplink beam matrix, the conjugate transpose of the uplink beam matrix, the conjugate transpose of the uplink beam selection matrix, and the reciprocal of the uplink signal-to-noise ratio. The inverse of the resulting matrix is ​​then multiplied by the vector obtained by sequentially multiplying the spread vectors of the user terminals by the uplink beam matrix, the conjugate transpose of the uplink beam matrix, and the uplink beam selection matrix.

[0023] Thirdly, the present invention provides a satellite massive MIMO communication system, including a satellite and a user terminal, wherein the satellite or a gateway station connected to it implements the downlink precoding method and uplink detection method of the satellite massive MIMO beam structure.

[0024] 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 satellite massive MIMO beam structure downlink precoding method and uplink detection method.

[0025] Beneficial Effects: Compared to existing technologies, this invention fully utilizes the spatial domain single-path characteristics and angular domain sparsity characteristics of satellite channels to design a beam structure downlink precoding and uplink detection method suitable for satellite massive MIMO systems. The proposed beam structure precoder and detector both exhibit a structure that multiplies a beam selection beam matrix with a low-dimensional beam domain vector requiring only real-valued computation. During the calculation of the beam domain vector, some real-valued matrices and vectors can be pre-calculated and stored. This structure significantly reduces the design and implementation complexity of the precoder and detector while ensuring uplink and downlink transmission performance. Attached Figure Description

[0026] 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.

[0027] Figure 1 This is a schematic diagram of the downlink precoding method for satellite massive MIMO beam structure according to an embodiment of the present invention.

[0028] Figure 2 This is a schematic diagram of the uplink detection method for satellite large-scale MIMO beam structure according to an embodiment of the present invention.

[0029] Figure 3 This is a diagram illustrating a satellite-based massive MIMO mobile communication scenario according to an embodiment of the present invention.

[0030] Figure 4 This is a comparison chart of traversal and rate performance in satellite massive MIMO mobile communication according to an embodiment of the present invention. Among them, (a) is a comparison chart of traversal and rate performance of the downlink precoder; (b) is a comparison chart of traversal and rate performance of the uplink detector.

[0031] Figure 5 This is a complexity comparison diagram in satellite massive MIMO mobile communication according to an embodiment of the present invention. Among them, (a) is a complexity comparison diagram of the downlink precoder; (b) is a complexity comparison diagram of the uplink detector. Detailed Implementation

[0032] 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.

[0033] This invention discloses a satellite massive MIMO beamstructure downlink precoding method. This method is applied to a satellite or a gateway station connected to the satellite, wherein the satellite is equipped with an antenna array and communicates with user terminals within its coverage area, each equipped with a single antenna. The satellite uses a beam-based channel model and statistical channel information from each user terminal, including average channel energy and spatial angle information, to design a beamstructure precoder for each user, and then uses the designed precoder for signal transmission.

[0034] 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.

[0035] like Figure 1 As shown, the downlink precoding method for the satellite massive MIMO beam structure includes:

[0036] The scaling direction cosine is uniformly sampled and quantized to construct a downlink beam matrix. The spatial domain channel vector is represented as the product of the equivalent downlink channel gain, the downlink beam matrix, and the spread vector. The spread vector represents the energy on different beams. The sampling range of the scaling direction cosine is between positive and negative twice the ratio of the antenna spacing to the carrier wavelength. The downlink beam matrix is ​​a standard DFT matrix multiplied by a block diagonal-zero matrix on the left and right, respectively.

[0037] Beam selection is performed on the spread vector to obtain the downlink beam selection matrix;

[0038] Based on the downlink beam matrix, downlink beam selection matrix, and statistical channel information obtained from each user terminal, the satellite side designs a beam-structured downlink precoder for transmitting signals to each user terminal. The beam-structured downlink precoder has a structure that multiplies the beam selection matrix and the low-dimensional beam domain precoder, transforming the high-dimensional spatial domain precoder design into a low-dimensional beam domain precoder design. The closed-form expression of the beam domain precoder involves only real-valued operations, and some calculations can be quickly implemented using DFT.

[0039] The designed beamform downlink precoder is used for low-complexity downlink precoding transmission processing. The downlink transmission signal is represented as the sum of the products of the beamform precoder of all user terminals and the transmission signal, and can be quickly calculated using DFT through the relationship between Kronecker product and vectorization.

[0040] Each column of the downlink beam matrix is ​​a sampling direction vector, and the independent variable of the sampling direction vector is a uniformly sampled and quantized scaling direction cosine. Each sampling direction vector represents a satellite side beam.

[0041] The downlink beam selection matrix is ​​obtained based on the downlink beam index set of each user; the downlink beam selection matrix ignores beam points with approximately 0 energy in the spread vector of each user, and only retains beam points in the diamond-shaped area around the beam point with the largest downlink channel energy of each user.

[0042] The beam domain precoder is obtained through closed-form calculation based on the average signal-to-noise ratio (ASLNR) criterion. ASLNR is the ratio of the average signal power transmitted by the satellite to the target user to the sum of the average interference power and average noise power leaked to other users. The beam domain precoder maximizes the ASLNR of each user terminal. The downlink beam domain precoder can be obtained through closed-form calculation using the channel average energy and spatial angle information.

[0043] In a specific example, the beamform precoder exhibits a structure that multiplies the downlink beam matrix with the beam domain precoder. The closed-form expression of the beam domain precoder is as follows: by multiplying the spread vectors of all user terminals sequentially by the downlink beam matrix, the conjugate transpose of the downlink beam matrix, and the downlink beam selection matrix, the resulting vector is multiplied by the square root of the vector and the average channel energy of the corresponding user, and the resulting matrix is ​​summed. The resulting matrix is ​​then multiplied by the downlink beam selection matrix sequentially by the downlink beam matrix, the conjugate transpose of the downlink beam matrix, the conjugate transpose of the downlink beam selection matrix, and the reciprocal of the downlink signal-to-noise ratio, and the resulting matrix is ​​added together. The inverse of the resulting matrix is ​​then multiplied by the spread vectors of the user terminals sequentially by the downlink beam matrix, the conjugate transpose of the downlink beam matrix, and the downlink beam selection matrix, and the resulting vector is then energy-normalized.

[0044] This invention also discloses a satellite massive MIMO beam structure uplink detection method. This method is applied to a satellite or a gateway station connected to the satellite, wherein the satellite is equipped with an antenna array and communicates with user terminals within its coverage area, each equipped with a single antenna. The satellite uses a beam-based channel model and statistical channel information from each user terminal, including average channel energy and spatial angle information, to design a beam structure detector for each user, and then uses the designed detector for reception processing.

[0045] 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.

[0046] like Figure 2 As shown, the satellite large-scale MIMO beam structure uplink detection method includes:

[0047] The scaling direction cosine is uniformly sampled and quantized to construct an uplink beam matrix. The spatial domain channel vector is represented as the product of the equivalent uplink channel gain, the uplink beam matrix, and the spread vector. The spread vector represents the energy on different beams. The sampling range of the scaling direction cosine is between positive and negative twice the ratio of the antenna spacing to the carrier wavelength. The uplink beam matrix is ​​a standard DFT matrix multiplied by a block diagonal-zero matrix on the left and right, respectively.

[0048] Beam selection is performed on the spread vector to obtain the uplink beam selection matrix;

[0049] Based on the uplink beam matrix, uplink beam selection matrix, and statistical channel information obtained from each user terminal, the satellite side designs uplink detectors for each user beam structure. The uplink detector for the beam structure has a structure that multiplies the beam selection beam matrix and the low-dimensional beam domain detector, transforming the design of the high-dimensional spatial domain detector into the design of the low-dimensional beam domain detector. The closed-form expression of the beam domain detector involves only real-valued operations, and some calculations can be quickly implemented using DFT.

[0050] The designed beamform detector is used for low-complexity uplink reception processing. The uplink recovered signal is represented as the product of the beamform detector and the received signal, and can be quickly calculated using DFT through the relationship between the Kronecker product and vectorization.

[0051] Each column of the uplink beam matrix is ​​a sampling direction vector, and the independent variable of the sampling direction vector is a uniformly sampled and quantized scaling direction cosine. Each sampling direction vector represents a satellite side beam.

[0052] The uplink beam selection matrix is ​​obtained based on the uplink beam index set of each user; the uplink beam selection matrix ignores beam points with approximately 0 energy in the spread vector of each user, and only retains beam points in the diamond-shaped area around the beam point with the largest uplink channel energy of each user.

[0053] The beam domain detector is obtained through closed-form calculation based on the average signal-to-interference-plus-noise ratio (ASINR) criterion. ASINR is the ratio of the average signal power of the target user's transmitted signal to the sum of the average signal power and average noise power of the other user's transmitted signal in the signal generated by the detector. The beam domain detector maximizes the ASINR of each user terminal. The uplink beam domain detector can be obtained through closed-form calculation using the channel average energy and spatial angle information.

[0054] In a specific example, the beam structure detector exhibits a structure of multiplying the uplink beam matrix with the beam domain detector. The closed-form expression of the beam domain detector is as follows: by successively left-multiplying the spread vectors of all user terminals by the uplink beam matrix, the conjugate transpose of the uplink beam matrix, and the uplink beam selection matrix, the resulting vector is multiplied by the square root of the vector and the average channel energy of the corresponding user, and the resulting matrix is ​​summed. The resulting matrix is ​​then multiplied by the uplink beam selection matrix by successively left-multiplying the uplink beam matrix, the conjugate transpose of the uplink beam matrix, the conjugate transpose of the uplink beam selection matrix, and the reciprocal of the uplink signal-to-noise ratio, and the resulting matrix is ​​multiplied by the inverse of the resulting matrix and the vector obtained by successively left-multiplying the spread vectors of the user terminals by the uplink beam matrix, the conjugate transpose of the uplink beam matrix, and the uplink beam selection matrix.

[0055] The calculation of the beam domain precoder and detector in the above embodiments depends only on real-valued matrix operations, and some of these matrix operations can be calculated and stored in advance.

[0056] 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.

[0057] I. System Configuration

[0058] like Figure 3 Considering that a single satellite can serve multiple user terminals simultaneously, the satellite is equipped with a two-dimensional uniform planar array (UPA). shaft and The number of antenna elements in the axial direction are respectively and ,but Total number of antennas provided for the satellite. Assume each user has a single antenna. Record the satellite service... The set of user terminals is , can be represented as .

[0059] Let the number of subcarriers in Orthogonal Frequency Division Multiplexing (OFDM) be . The length of the cyclic prefix (CP) is The system sampling time interval is Then the OFDM symbol time length is CP time length is .remember .

[0060] II. Beam-structured downlink pre-encoder

[0061] After time and frequency synchronization, satellite to user The equivalent downlink channel impulse response is assumed to remain constant over one OFDM symbol time and can be expressed as

[0062] (1)

[0063] in, Satellite to user The number of multipaths, It is the basic complex unit. , , It is the scaling direction cosine. Indicates the downlink carrier wavelength. and These represent the antenna spacing and number, respectively. and Indicates direction cosine. and It is the corresponding spatial angle. , , 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:

[0064] (2)

[0065] in, Defined as

[0066] (3)

[0067] The central conjugate symmetry of the array response vector will be applied to reduce the computational complexity of the proposed beamform precoder. Ignoring the subscripts of OFDM symbols and subcarriers, let... For a channel on a certain subcarrier, it is represented as

[0068] (4)

[0069] Equation (4) represents the spatial domain channel on a certain subcarrier, where It is the equivalent downlink channel gain, a random scalar following a Caiuss distribution, and the array response vector This can be further expressed as the product of the downlink beam matrix and the spread vector. The downlink beam matrix and the downlink beam-based channel model are given below. exist Inner Uniform sampling at sampling intervals, where , It is the oversampling factor. (Note: The last part is a typo and can be left as is.) for The first direction Each sampling interval , ,in Indicates not less than The smallest integer. The interval All scaling direction cosines within are approximately represented as .exist The downlink beam matrix in the direction is defined as

[0070] (5)

[0071] Each column of the downlink beam matrix is ​​a sampling direction vector, the independent variable of which is a uniformly sampled and quantized scaling direction cosine, and each sampling direction vector represents a satellite side beam.

[0072] Note that when When it is an integer, and The discrete Fourier transform (DFT) matrices of a point have the following relationship:

[0073] (6)

[0074] in, and It is a block matrix. , In other words, the downlink beam matrix can be represented as a standard DFT matrix multiplied by a block diagonal-zero matrix on both the left and right sides. This relationship can be applied to subsequent fast calculations. The complete downlink beam matrix is ​​represented as follows:

[0075] (7)

[0076] in, Note that the array response vector defined in equation (3) is conjugate centrosymmetric, therefore Each column is conjugately centrally symmetric, and thus... Each column is also conjugately centrally symmetric.

[0077] Next, we will analyze the array response vector. Represented as

[0078] (8)

[0079] in Let be the diffusion vector. The diffusion vector can be approximated by adding sampling points within the sampling interval of the direction cosine. Specifically, we... Internal Uniform sampling is performed at points, and all sampling points are denoted as . .remember For the closest The point is defined as

[0080] (9)

[0081] use To approximate You can get

[0082] (10)

[0083] At this time, the diffusion vector The sparse representation of can be obtained by sparse signal recovery algorithms. Let be the sparse representation of . The array response vector is then expressed as

[0084]

[0085] in .

[0086] Due to the limited number of antennas, channel energy will diffuse between different beams. The diffused channel energy will concentrate in a diamond-shaped region around the beam point with the highest user channel energy. Since the energy outside the diamond-shaped region is negligible compared to the energy within it, their energy values ​​can be directly set to 0. Next, a downlink beam selection matrix is ​​defined to extract all beam points within this diamond-shaped region. The downlink beam index set is defined as follows:

[0087] (11)

[0088] in, and Modulo operation is represented. The size of the rhomboid region was specified, and At this time, the user terminal The downlink beam selection matrix is ​​defined as

[0089] (12)

[0090] At this time, the downlink channel vector It can be represented as

[0091] (13)

[0092] in, For downlink beam domain channels, , .

[0093] Considering that the satellite uses linear precoding to generate the transmitted signal to each user terminal, then the user terminal The received signal can be represented as

[0094] (14)

[0095] in, It is a user terminal Normalized transmission precoding vector, It is assigned to the user terminal The transmission power; It is a user terminal The signal has a mean of 0 and a variance of 1. It is a user terminal The additive complex white Gaussian noise has a mean and variance of 0 and 0, respectively. The following study examines the design of a precoder utilizing only statistical channel information. User terminal The average signal-to-leakage-and-noise ratio (ASLNR) can be expressed as:

[0096] (15)

[0097] in, , It is the downlink signal-to-noise ratio.

[0098] The downlink precoding problem that maximizes the average signal-to-noise ratio can be expressed as:

[0099] (16)

[0100] The closed-form solution to (16) is

[0101] (17)

[0102] in, It is a guarantee The normalization factor.

[0103] A beamform precoder design is proposed using the downlink beam matrix and beam selection matrix. First, it can be proven that when... and When, (17) can be expressed as

[0104] (18)

[0105] in,

[0106] (19)

[0107] For a sufficiently large When the beam domain channels of any two users do not overlap, The design can be transformed into a beam domain vector. The design. Due to the sparsity of the beam domain channel in massive MIMO satellite communication, Typically much smaller Therefore, it can effectively reduce the complexity of pre-encoder design.

[0108] consider and The relationship between them will be rewritten as ASLNR.

[0109] (20)

[0110] in, , At this point, the downlink precoding problem of maximizing the average signal-to-noise ratio (SNR) becomes the optimization of the beam-domain precoder, and can be expressed as:

[0111] (twenty one)

[0112] The beam domain precoder that satisfies (21) is

[0113] (twenty two)

[0114] in, It is the energy normalization factor.

[0115] Subsequently, the beam structure pre-encoder It can be used for low-complexity downlink precoding transmission processing.

[0116] III. Beam Structure Uplink Detector

[0117] For TDD systems, the uplink channel impulse response can be obtained by transposing the downlink channel impulse response. For FDD systems, the difference between the uplink and downlink center frequencies is usually small relative to the uplink (downlink) center frequency, and the corresponding physical channel parameters are approximately identical for both uplink and downlink channels. Therefore, the main difference between the uplink and downlink channels lies in the channel gain of different paths. Furthermore, the uplink array response vector... It has a structure similar to the downlink array response vector, but in FDD systems there is a center frequency offset, which can be expressed as:

[0118] (twenty three)

[0119] in, , , Indicates the uplink carrier wavelength, and Defined as

[0120] (twenty four)

[0121] The central conjugate symmetry of the array response vector will be applied to reduce the computational complexity of the proposed beamform detector. Ignoring the subscripts of OFDM symbols and subcarriers, let... For a channel on a certain subcarrier, it is represented as

[0122] (25)

[0123] Equation (25) represents the spatial domain channel on a certain subcarrier, where, It is the equivalent uplink channel gain, a random scalar following a Caiuss distribution, and the array response vector This can be further expressed as the product of the uplink beam matrix and the spread vector. The uplink beam matrix and the uplink beam-based channel model are given below. by Uniform sampling at sampling intervals, where , It is the oversampling factor. (Note: The last part is a typo and can be left as is.) for The first direction Each sampling interval , . The interval All scaling direction cosines within are approximately represented as .exist The uplink beam matrix in the direction is defined as

[0124] (26)

[0125] Each column of the uplink beam matrix is ​​a sampling direction vector, and the independent variable of the sampling direction vector is a uniformly sampled and quantized scaling direction cosine. Each sampling direction vector represents a satellite side beam.

[0126] Note that when When it is an integer, and The discrete Fourier transform (DFT) matrices of a point have the following relationship:

[0127] (27)

[0128] in, and It is a block matrix. , In other words, the uplink beam matrix can be represented as a standard DFT matrix multiplied by a block diagonal-zero matrix on both the left and right sides. This relationship can be applied to subsequent fast calculations. The complete uplink beam matrix is ​​represented as follows:

[0129] (28)

[0130] in, .akin, Each column is also conjugate centrosymmetric. Using this uplink beam matrix, the uplink array response vector can be re-represented as...

[0131] (29)

[0132] in, .

[0133] Due to the high similarity between uplink and downlink channels, the energy of the uplink channel will also diffuse between different beams. Similarly, the uplink beam index is defined as...

[0134] (30)

[0135] remember User terminal The uplink beam selection matrix is ​​defined as

[0136] (31)

[0137] At this time, the uplink channel vector It can be represented as

[0138] (32)

[0139] in, For uplink beam domain channels, , .

[0140] The uplink received signal of a satellite on a certain subcarrier can be written as:

[0141] (33)

[0142] in, This is the transmission power of each user terminal; It is the first The transmitted signals of each user terminal have a mean of 0 and a variance of 1. .

[0143] Considering that the satellite uses a linear detector to perform linear signal reception processing, It is a user terminal The linear receiving vector. The processed signal can be represented as...

[0144] (34)

[0145] The following study examines detector design utilizing only statistical channel information. The average signal-to-interference-plus-noise ratio (ASINR) can be expressed as:

[0146] (35)

[0147] in, , This refers to the uplink signal-to-noise ratio.

[0148] make The largest detector can be represented as

[0149] (36)

[0150] A beamform detector design is proposed using the uplink beam matrix and beam selection matrix. First, it can be proven that when... and When, (36) can be expressed as

[0151] (37)

[0152] in,

[0153] (38)

[0154] For a sufficiently large When the beam domain channels of any two users do not overlap, The design can be transformed into a beam domain vector. The design. Due to the sparsity of the beam domain channel in massive MIMO satellite communication, Usually more It is much smaller, thus significantly reducing the complexity of detector design.

[0155] consider and The relationship between them can be rewritten as ASINR.

[0156] (39)

[0157] in, , At this time, make The largest beam domain detector can be calculated as

[0158] (40)

[0159] Subsequently, beam structure detector It can be used for low-complexity uplink receive processing.

[0160] IV. Low-complexity design and implementation

[0161] The design complexity of the beamform precoder comes from equation (22), which can be rewritten as

[0162] (41)

[0163] The closed-form expression of the beam domain precoder is: by multiplying the spread vectors of all user terminals sequentially by the downlink beam matrix, the conjugate transpose of the downlink beam matrix, and the downlink beam selection matrix, the resulting vector... The vector is then multiplied by the square root of the vector and the average channel energy of the corresponding user, and the sum of the outer products of the resulting vectors is taken. The resulting matrix is ​​then multiplied by the downlink beam selection matrix, the conjugate transpose of the downlink beam selection matrix, the conjugate transpose of the downlink beam selection matrix, and the reciprocal of the downlink signal-to-noise ratio in sequence to obtain the final matrix. Add them together, and then take the inverse of the resulting matrix. The vector obtained by left-multiplying the spread vector of the user terminal by the downlink beam matrix, the conjugate transpose of the downlink beam matrix, and the downlink beam selection matrix in sequence. The vector obtained by multiplying is then normalized by energy.

[0164] First, it should be noted that the above formula relies solely on real matrix operations. According to the definition of a beam selection matrix, it is a real matrix. According to the definition of a beam matrix, each column is conjugately centrally symmetric, therefore... ,in It is an anti-diagonal matrix in which all anti-diagonal elements are 1. Therefore ,Right now It is a real matrix, therefore It is a real matrix. Because... ,and It is conjugate centrally symmetric, and we can obtain It is a real number vector. Therefore, the calculation of the beam domain precoder only involves real number matrix operations.

[0165] because It depends only on the sampling direction vector and is not related to any specific user, therefore it can be calculated and stored in advance. , .because It is a symmetric Toeplitz matrix, therefore it can be used... The first column represents Each element in can be further described using The first column represents ,Right now

[0166] (42)

[0167] Depend on The relationship with the DFT matrix and Can be respectively by and The point fast Fourier transform is used to obtain the computational and storage complexity, respectively. and .

[0168] Real vector It can be represented as

[0169] (43)

[0170] Note the arbitrary diffusion vector It can be by One of the certainties, and It is independent of specific users, therefore all of them can be calculated and stored in advance. The computational complexity and storage complexity are respectively and Subsequently, the Kronecker product is used to obtain the data for all user terminals. The computational complexity is .

[0171] Based on the definition of the downlink beam selection matrix, the number of non-zero beams for all user terminals They are all the same, so we denote them as Using the information calculated and stored in advance, the design complexity of the beam domain precoder can be obtained as follows: In contrast, we present a spatial domain precoder. The design complexity is .

[0172] Using a pre-coded pre-encoder for signal transmission, the transmitted signal on a certain subcarrier can be represented as:

[0173] (44)

[0174] in The relationship between the downlink beam matrix and the DFT matrix can be used to quickly calculate equation (44) using DFT, with a computational complexity of O(n). This refers to the implementation complexity of beamforming precoding. Correspondingly, the implementation complexity of generating the transmitted signal on each subcarrier in the spatial domain is... .

[0175] Since all effective subcarriers share the same beam matrix and beamform precoder, each OFDM symbol requires one design and several implementations for each effective subcarrier. The total complexity of the beamform precoder is defined as one design and several implementations for each effective subcarrier. The sum of the complexities of each implementation, i.e. ,in This represents the number of effective subcarriers. Correspondingly, the total complexity of the spatial domain precoder is... .

[0176] The design complexity of the beamform detector comes from equation (40), which can be rewritten as

[0177] (45)

[0178] The closed-form expression for the beam domain detector is: by successively left-multiplying the spread vectors of all user terminals by the uplink beam matrix, the conjugate transpose of the uplink beam matrix, and the uplink beam selection matrix, the resulting vector... The vector is then multiplied by the square root of the vector and the average channel energy of the corresponding user, and the sum of the outer products of the resulting vectors is taken. The resulting matrix is ​​then multiplied by the downlink beam selection matrix in turn by the uplink beam matrix, the conjugate transpose of the uplink beam matrix, the conjugate transpose of the uplink beam selection matrix, and the reciprocal of the uplink signal-to-noise ratio. Add them together, and then take the inverse of the resulting matrix. The vector obtained by left-multiplying the spread vector of the user terminal by the uplink beam matrix, the conjugate transpose of the uplink beam matrix, and the uplink beam selection matrix in sequence is... The vector obtained by multiplication.

[0179] Similar to the analysis of beamstructure precoders, it can be proven that the computation of beam domain detectors involves only real-valued matrix operations. , and utilize The first column represents ,have

[0180] (46)

[0181] Depend on The relationship with the DFT matrix and Can be respectively by and The point fast Fourier transform is used to obtain the computational and storage complexity, respectively. and .

[0182] real number vector Represented as

[0183] (47)

[0184] Among them, diffusion vector It can be by One of them is certain, and all The computational complexity and storage complexity are respectively and Subsequently, the Kronecker product is used to obtain the data for all user terminals. The computational complexity is .

[0185] Based on the definition of the uplink beam selection matrix, the number of non-zero beams for all user terminals They are all the same, so we denote them as Using the information calculated and stored in advance, the design complexity of the beam domain detector can be obtained as follows: In contrast, we present a spatial domain detector. The design complexity is .

[0186] The uplink received signal is detected using a pre-designed beamform detector, and the recovered signal is...

[0187] (48)

[0188] in It can be calculated in a manner similar to equation (44), with a computational complexity of O(n). After that, each Only one vector multiplication is required, therefore the implementation complexity of the beam structure detector is O(n log n). Correspondingly, the implementation complexity of generating and recovering the signal in the spatial domain is... .

[0189] The total complexity of a beam structure detector is defined as a single design and... The sum of the complexities of each implementation is expressed as Correspondingly, the total complexity of the spatial domain detector is... .

[0190] Figure 4 Figure (a) presents the downlink ergodic and rate performance of the beamstructure precoder, the maximum ratio transmission (MRT) method, and the spatial domain precoder proposed in this embodiment. As can be seen from the figure, the beamstructure precoder exhibits significantly improved ergodic reachability and rate performance compared to the MRT method, and can approach the performance of the spatial domain precoder. The performance of the beamstructure precoder is further improved with an increase in the number of selected non-zero beam points. Figure 4 Figure (b) presents the uplink ergodicity and rate performance of the beamstructure detector, MRT method, and spatial domain detector proposed in this embodiment. As can be seen from the figure, the beamstructure detector exhibits significantly improved ergodic reachability and rate performance compared to the MRT method, and can approach the performance of the spatial domain detector. The performance of the beamstructure detector further improves with an increase in the number of selected non-zero beam points.

[0191] Figure 5 (a) and Figure 5 Figure (b) presents a comparison of the complexity of the spatial domain and beamform precoder and detector, respectively. As can be seen from the figure, the proposed beamform precoder and detector have a significant complexity advantage over the spatial domain precoder and detector.

[0192] This invention also discloses a satellite massive MIMO communication system, including a satellite and a user terminal, wherein the satellite or a gateway station connected to it implements the downlink precoding method and uplink detection method of the satellite massive MIMO beam structure.

[0193] This invention also discloses a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the satellite massive MIMO beam structure downlink precoding method and uplink detection method.

[0194] Any aspects of this invention not described in detail are well-known to those skilled in the art.

[0195] 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 downlink precoding method for a satellite massive MIMO beam structure, characterized in that, The steps include the following: The scaling direction cosine is uniformly sampled and quantized to construct a downlink beam matrix. The spatial domain channel vector is represented as the product of the equivalent downlink channel gain, the downlink beam matrix, and the spread vector. The spread vector represents the energy on different beams. The sampling range of the scaling direction cosine is between positive and negative twice the ratio of the antenna spacing to the carrier wavelength. The downlink beam matrix is ​​a standard DFT matrix multiplied by a block diagonal-zero matrix on the left and right, respectively. Beam selection is performed on the spread vector to obtain the downlink beam selection matrix; Based on the downlink beam matrix, downlink beam selection matrix, and statistical channel information obtained from each user terminal, the satellite side designs a beam-structured downlink precoder for transmitting signals to each user terminal. The beam-structured downlink precoder has a structure that multiplies the beam selection matrix and the low-dimensional beam domain precoder, transforming the high-dimensional spatial domain precoder design into a low-dimensional beam domain precoder design. The closed-form expression of the beam domain precoder involves only real-valued operations, and some calculations can be quickly implemented using DFT. The designed beamform downlink precoder is used for low-complexity downlink precoding transmission processing; the downlink transmission signal is represented as the sum of the products of the beamform precoder of all user terminals and the transmission signal, and can be quickly calculated using DFT through the relationship between Kronecker product and vectorization. The beam domain precoder is calculated using a closed-form formula based on the Average Signal-to-Noise Ratio (ASLNR) criterion. ASLNR is the ratio of the average signal power transmitted by the satellite to the target user to the sum of the average interference power and average noise power leaked to other users. The beam domain precoder maximizes the ASLNR of each user terminal. The closed-form expression of the beam domain precoder is as follows: the vector obtained by multiplying the spread vector of all user terminals sequentially by the downlink beam matrix, the conjugate transpose of the downlink beam matrix, and the downlink beam selection matrix, and then multiplying the resulting vector by the square root of the average channel energy of the corresponding user, summing the outer product of the resulting vector, multiplying the resulting matrix by the downlink beam selection matrix sequentially by the downlink beam matrix, the conjugate transpose of the downlink beam matrix, the conjugate transpose of the downlink beam selection matrix, and the reciprocal of the downlink signal-to-noise ratio, and then adding the resulting inverse matrix to the vector obtained by multiplying the spread vector of the user terminal sequentially by the downlink beam matrix, the conjugate transpose of the downlink beam matrix, and the downlink beam selection matrix, and then normalizing the resulting vector.

2. The downlink precoding method for satellite massive MIMO beam structure according to claim 1, characterized in that, Each column of the downlink beam matrix is ​​a sampling direction vector, and the independent variable of the sampling direction vector is a uniformly sampled and quantized scaling direction cosine. Each sampling direction vector represents a satellite side beam.

3. The downlink precoding method for satellite massive MIMO beam structure according to claim 1, characterized in that, The downlink beam selection matrix is ​​obtained based on the downlink beam index set of each user; the downlink beam selection matrix ignores beam points with approximately 0 energy in the spread vector of each user, and only retains beam points in the diamond-shaped area around the beam point with the largest downlink channel energy of each user.

4. A method for uplink detection of satellite massive MIMO beam structure, characterized in that, The steps include the following: The scaling direction cosine is uniformly sampled and quantized to construct an uplink beam matrix. The spatial domain channel vector is represented as the product of the equivalent uplink channel gain, the uplink beam matrix, and the spread vector. The spread vector represents the energy on different beams. The sampling range of the scaling direction cosine is between positive and negative twice the ratio of the antenna spacing to the carrier wavelength. The uplink beam matrix is ​​a standard DFT matrix multiplied by a block diagonal-zero matrix on the left and right, respectively. Beam selection is performed on the spread vector to obtain the uplink beam selection matrix; Based on the uplink beam matrix, uplink beam selection matrix, and statistical channel information obtained from each user terminal, the satellite side designs uplink detectors for each user beam structure. The uplink detector for the beam structure has a structure that multiplies the beam selection beam matrix and the low-dimensional beam domain detector, transforming the design of the high-dimensional spatial domain detector into the design of the low-dimensional beam domain detector. The closed-form expression of the beam domain detector involves only real-valued operations, and some calculations can be quickly implemented using DFT. The designed beamform detector is used for low-complexity uplink reception processing. The uplink recovered signal is represented as the product of the beamform detector and the received signal, and can be quickly calculated using DFT through the relationship between the Kronecker product and vectorization. The beam domain detector is calculated using a closed-form formula based on the average signal-to-interference-plus-noise ratio (ASINR) criterion. ASINR is the ratio of the average signal power of the target user's transmitted signal to the sum of the average signal power and average noise power of signals transmitted by other users in the signal generated by the detector. The beam domain detector maximizes the ASINR of each user terminal. The closed-form expression of the beam domain detector is as follows: The vector is obtained by multiplying the spread vector of all user terminals sequentially by the uplink beam matrix, the conjugate transpose of the uplink beam matrix, and the uplink beam selection matrix. The resulting vector is then multiplied by the square root of the vector and the average channel energy of the corresponding user. The resulting matrix is ​​then summed with the matrix obtained by multiplying the uplink beam selection matrix sequentially by the uplink beam matrix, the conjugate transpose of the uplink beam matrix, the conjugate transpose of the uplink beam selection matrix, and the reciprocal of the uplink SNR. The inverse of this matrix is ​​then multiplied by the vector obtained by multiplying the spread vector of the user terminal sequentially by the uplink beam matrix, the conjugate transpose of the uplink beam matrix, and the uplink beam selection matrix.

5. The satellite massive MIMO beam structure uplink detection method according to claim 4, characterized in that, Each column of the uplink beam matrix is ​​a sampling direction vector, and the independent variable of the sampling direction vector is a uniformly sampled and quantized scaling direction cosine. Each sampling direction vector represents a satellite side beam.

6. The satellite massive MIMO beam structure uplink detection method according to claim 4, characterized in that, The uplink beam selection matrix is ​​obtained based on the uplink beam index set of each user; the uplink beam selection matrix ignores beam points with approximately 0 energy in the spread vector of each user, and only retains beam points in the diamond-shaped area around the beam point with the largest uplink channel energy of each user.

7. A satellite massive MIMO communication system, comprising a satellite and a user terminal, characterized in that, The satellite or the gateway station associated with it is used to implement the steps of the satellite massive MIMO beam structure downlink precoding method according to any one of claims 1-3 and the satellite massive MIMO beam structure uplink detection method according to any one of claims 4-6.

8. 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 method according to any one of claims 1-6.

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