Satellite downlink wireless transmission method, device, equipment and storage medium

Abstract

The application discloses a satellite downlink wireless transmission method, device and equipment and a storage medium, and belongs to the technical field of wireless communication. The method comprises the following steps: acquiring target statistical channel state information between a satellite and a user terminal; determining a target problem by using the target statistical channel state information, wherein the target problem is to maximize the ergodic achievable rate of the user terminal under the constraints of satellite transmission power and unmanned aerial vehicle relay power; updating a transmission covariance matrix and a relay amplification matrix by using an alternating optimization algorithm on the basis of the target problem, so as to obtain an optimized transmission covariance matrix and an optimized relay amplification matrix; and transmitting a signal to the user terminal through the unmanned aerial vehicle relay based on the optimized transmission covariance matrix and the optimized relay amplification matrix. The technical scheme provided by the application can realize downlink wireless transmission in an unmanned aerial vehicle assisted satellite communication system by using statistical channel state information.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a satellite downlink wireless transmission method, apparatus, device, and storage medium. Background Technology

[0002] With the explosive growth in demand for mobile data services, wireless communication systems face stringent requirements for ultra-high spectrum efficiency, massive connectivity, and seamless global coverage. Although significant progress has been made in terrestrial networks for 5G mobile communications, their performance in remote and underserved areas is constrained by geographical limitations, high infrastructure deployment costs, and environmental interference, making it difficult to meet future communication needs. Therefore, integrated space-ground networks that combine satellite and airborne communication platforms have become an important solution.

[0003] Massive MIMO (Multiple Input Multiple Output) technology, through the deployment of large-scale antenna arrays, achieves higher spatial degrees of freedom and spectral efficiency, thus becoming a key technology for next-generation satellite communications. However, the performance of massive MIMO hinges on accurate channel state information at the transmitter. In satellite communication systems, the acquisition and utilization of instantaneous channel state information is severely challenged by inherent characteristics such as long propagation delays and high relative speeds, which cause rapid channel changes and Doppler shifts. These changes render instantaneous channel state information outdated within the channel coherence time, significantly reducing the reliability of schemes based on instantaneous channel state information. Furthermore, the feedback overhead for acquiring instantaneous channel state information in massive MIMO satellite systems is prohibitive. Meanwhile, direct links between satellites and ground users are susceptible to interruptions due to terrain obstruction or atmospheric conditions; therefore, introducing UAVs as relays can enhance communication connectivity. Against this backdrop, designing transmission schemes using statistical channel state information is easier for long-term estimation and has lower feedback and computational overhead, making it suitable for massive MIMO satellite communication scenarios. While existing technologies have performed single-hop beam design and capacity analysis using statistical channel state information, multi-hop joint optimization transmission strategies have not yet been analyzed.

[0004] Existing technologies address the challenges of acquiring instantaneous channel state information and high feedback overhead in large-scale MIMO communication systems for LEO (Low Earth Orbit) satellites. This study investigates a downlink transmission design utilizing only statistical channel state information, proving that single-stream precoding is the optimal strategy for maximizing ergodicity and rate. This transmission design is suitable for single-hop communication scenarios. However, if there is no direct link between the satellite and the user, an UAV is needed as a relay, making this method unsuitable for direct application. For UAV-supported integrated air-space-ground relay networks, an energy efficiency optimization scheme based on non-orthogonal multiple access is proposed; however, this research is based on analysis using instantaneous channel state information.

[0005] Therefore, a more reliable solution is needed. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a satellite downlink wireless transmission method, apparatus, device and storage medium that can realize downlink wireless transmission in UAV-assisted satellite systems by utilizing statistical channel state information, while reducing computational complexity and channel feedback overhead.

[0007] To achieve the above objectives, the present invention employs the following technical solution:

[0008] On one hand, the present invention provides a satellite downlink wireless transmission method, the method comprising:

[0009] Acquire target statistical channel status information between the satellite and the user terminal;

[0010] Using the target statistical channel state information, the target problem is determined, which is to maximize the traversal reachability of the user terminal under the constraints of satellite transmit power and UAV relay power.

[0011] Based on the aforementioned objective problem, the alternating optimization algorithm is used to update the transmission covariance matrix and the relay amplification matrix, thereby obtaining the optimized transmission covariance matrix and the optimized relay amplification matrix.

[0012] Based on the optimized transmission covariance matrix and the optimized relay amplification matrix, signals are transmitted downlink to the user terminal via UAV relay.

[0013] Furthermore, based on the stated objective problem, the alternating optimization algorithm is used to update the transmission covariance matrix and the relay amplification matrix to obtain the optimized transmission covariance matrix and the optimized relay amplification matrix, including:

[0014] Based on the aforementioned target problem, and using the current relay amplification matrix, the transmission covariance matrix is ​​updated to obtain the updated transmission covariance matrix;

[0015] The updated transmission covariance matrix is ​​determined as the current transmission covariance matrix. Based on the target problem, the relay amplification matrix is ​​updated based on the current transmission covariance matrix to obtain the updated relay amplification matrix.

[0016] The updated relay amplification matrix is ​​determined as the current relay amplification matrix. The process then proceeds to the step of updating the transmission covariance matrix based on the current relay amplification matrix, based on the target problem, to obtain the updated transmission covariance matrix. This process continues until a preset convergence condition is met, resulting in the optimized transmission covariance matrix and the optimized relay amplification matrix.

[0017] Furthermore, determining the target problem using the target statistical channel state information includes:

[0018] Using the target statistical channel state information, establish the satellite-to-aircraft channel model and the terminal-to-aircraft channel model;

[0019] Based on the satellite channel model and the terminal channel model, the initial traversal reachable rate is determined as shown in equation (1):

[0020] (1);

[0021] in, Indicates the initial traversal reachable speed; This represents the expected value of a random variable. This indicates how to find the determinant of a matrix; Denotes the conjugate transpose; Q represents the transmit covariance matrix, G represents the relay amplification matrix; H1 represents the satellite-to-aircraft channel model, H2 represents the receiver-to-aircraft channel model; I N The dimension is N The identity matrix, N This indicates the total number of antennas equipped in the user terminal;

[0022] Based on the initial traversal reachable rate, the traversal reachable rate is determined using the large-dimensional random matrix method, as shown in equation (2):

[0023] (2);

[0024] in, Indicates the traversal reachability; I M The dimension is M The identity matrix, M I represents the total number of antennas equipped on the satellite. L The dimension is L The identity matrix, L R1 represents the total number of antennas equipped on the UAV; R2 represents the receiver correlation matrix on the user terminal side. , , , , , and These are intermediate variables used in calculations and have no specific physical meaning.

[0025] , , , , ,

[0026] ;

[0027] in, The matrix representing the first direct path component of the first statistical channel state information. T2 represents the second direct path component matrix, which represents the second statistical channel state information; T2 represents the UAV-side transmission correlation matrix. This indicates finding the trace of a matrix; , , and These are intermediate variables used in calculations and have no specific physical meaning.

[0028] , , , ,

[0029] ;

[0030] in, and These are intermediate variables used in calculations and have no specific physical meaning.

[0031] , ;

[0032] Under the constraints of satellite transmission power and UAV relay power, the target problem is determined based on the traversal reachable rate, as shown in equation (3):

[0033] (3);

[0034] in, This indicates maximizing the traversal reachability. This indicates a constraint on satellite launch power. This indicates the relay power constraint for drones. MP 1 indicates the satellite's maximum transmission power. LP 2 indicates the maximum transmission power of the drone relay; The matrix representing the first direct path component of the first statistical channel state information.

[0035] Furthermore, based on the stated objective problem, updating the transmission covariance matrix based on the current relay amplification matrix to obtain the updated transmission covariance matrix includes:

[0036] Based on the target problem, a first optimization problem is determined, which is used to update the transmission covariance matrix based on the current relay amplification matrix, as shown in equation (4):

[0037] (4);

[0038] in, Maximize ; represents the determinant of a matrix; Q represents the covariance matrix; G represents the relay amplification matrix; Indicates conjugate transpose; I M The dimension is M The identity matrix, M This indicates the total number of antennas equipped on the satellite; The first direct path component matrix represents the first statistical channel state information; MP 1 indicates the satellite's maximum transmission power. LP 2 indicates the maximum transmission power of the drone relay; These are intermediate variables used in calculations and have no specific physical meaning.

[0039] The first dual problem corresponding to the first optimization problem is determined as shown in equation (5):

[0040] (5);

[0041] (6);

[0042] in, This represents the first dual problem. Denotes the first dual function; Represents the first Lagrange multiplier. This indicates the UAV relay power constraint corresponding to the first Lagrange multiplier; Indicates the second Lagrange multiplier. This indicates the satellite transmit power constraint corresponding to the second Lagrange multiplier;

[0043] Further, the optimization of the transmission covariance matrix based on the first optimization problem and the first dual problem, combined with the current relay amplification matrix, to obtain the updated transmission covariance matrix includes:

[0044] Based on the processing of the first Lagrange function, the objective constraints are determined. The first Lagrange function is obtained based on the first optimization problem.

[0045] Based on the objective constraints, the first optimization problem is optimized to obtain the first objective optimization problem, as shown in equation (7):

[0046] (7);

[0047] in, This indicates how to find the determinant of a matrix; Maximize K represents variable substitution and has no specific physical meaning. ; Variable substitution is used to optimize the first optimization problem. ;

[0048] Based on the first objective optimization problem, the transmission covariance matrix is ​​solved using a water-filling algorithm to obtain the target transmission covariance matrix, as shown in equation (8):

[0049] (8);

[0050] in, V represents the target transmission covariance matrix; V represents eigenvector matrix; express eigenvalue matrix; express Eigenvalue matrix; Indicates a false reversal; Represents eigenvalue decomposition;

[0051] Based on the target transmission covariance matrix and the first dual problem, and in conjunction with the current relay amplification matrix, the updated transmission covariance matrix is ​​determined.

[0052] Furthermore, based on the target problem, updating the relay amplification matrix based on the current transmission covariance matrix to obtain the updated relay amplification matrix includes:

[0053] Based on the target problem, a second optimization problem is determined, which is used to update the relay amplification matrix based on the current transmission covariance matrix, as shown in equation (9):

[0054] (9);

[0055] in, Maximize ; represents the determinant of a matrix; Q represents the covariance matrix; G represents the relay amplification matrix; Indicates conjugate transpose; I L The dimension is L The identity matrix, L This indicates the total number of antennas equipped on the drone; LP 2 indicates the maximum transmission power of the drone relay; The first direct path component matrix represents the first statistical channel state information; , and These are intermediate variables used in calculations and have no specific physical meaning.

[0056] The second dual function corresponding to the second optimization problem is determined as shown in equation (10):

[0057] (10);

[0058] in, Describe the second dual function; Indicates the third Lagrange multiplier; and These are intermediate variables used in calculations and have no specific physical meaning.

[0059] , ;

[0060] in, T1 represents the second direct path component matrix, which represents the second statistical channel state information; T2 represents the UAV-side transmission correlation matrix; R2 represents the user terminal-side reception correlation matrix. , , and These are intermediate variables used in calculations and have no specific physical meaning.

[0061] Based on the second dual function and the current transmission covariance matrix, the relay amplification matrix is ​​optimized using a nested loop algorithm to obtain the updated relay amplification matrix.

[0062] The target statistical channel state information includes first statistical channel state information and second statistical channel state information; the acquisition of the target statistical channel state information between the satellite and the user terminal includes:

[0063] Receive the detection signal sent by the user terminal;

[0064] Based on the detection signal, the first average channel energy and the first spatial angle information corresponding to the satellite communication channel are determined, and the second average channel energy and the second spatial angle information corresponding to the terminal communication channel are determined.

[0065] Based on the first average channel energy and the first spatial angle information, the first statistical channel state information corresponding to the satellite communication channel is determined;

[0066] Based on the second average channel energy and the second spatial angle information, the second statistical signal information corresponding to the machine-side communication channel is determined;

[0067] The target statistical channel state information is determined based on the first statistical channel state information and the second statistical channel state information.

[0068] On the other hand, a satellite downlink wireless transmission device is provided, the device comprising:

[0069] The information acquisition module is used to acquire target statistical channel status information between the satellite and the user terminal;

[0070] The optimization module is used to determine the target problem using the target statistical channel state information. The target problem is to maximize the traversal reachability of the user terminal under the constraints of satellite transmit power and UAV relay power. Based on the target problem, the module uses an alternating optimization algorithm to update the transmit covariance matrix and the relay amplification matrix to obtain the optimized transmit covariance matrix and the optimized relay amplification matrix.

[0071] The signal transmission module is used to transmit downlink signals to the user terminal via UAV relay based on the optimized transmission covariance matrix and the optimized relay amplification matrix.

[0072] On the other hand, an electronic device is provided, the device including a processor and a memory, the memory storing at least one instruction and at least one program, the at least one instruction and the at least one program being loaded and executed by the processor to implement the satellite downlink wireless transmission method as described above.

[0073] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction and at least one program are stored therein, the at least one instruction and the at least one program being loaded and executed by a processor to implement the satellite downlink wireless transmission method as described above.

[0074] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0075] In this invention, target statistical channel state information between the satellite and the user terminal is acquired. Using this information, a target problem is determined: maximizing the traversal reachability of the user terminal under constraints of satellite transmit power and UAV relay power. Then, based on this target problem, an alternating optimization algorithm is used to update the transmit covariance matrix and the relay amplification matrix, resulting in optimized transmit covariance and relay amplification matrices. This improves the performance and reliability of optimizing these matrices, thus facilitating the determination of low-complexity... The system transmits a covariance matrix and a relay amplification matrix. Then, based on optimized transmission of the covariance matrix and optimized relay amplification matrix, downlink signals are transmitted to the user terminal via a UAV relay. This allows for downlink wireless transmission of the satellite communication system with the assistance of a UAV, based on statistical channel state information. Furthermore, this reduces computational complexity and channel feedback overhead during downlink wireless transmission. Since statistical channel state information is readily available, easy to use, and can be used for extended periods, it improves the reliability of satellite communication. Additionally, using a UAV as a communication relay enhances the received signal strength, thereby improving the communication performance of the satellite communication system. Attached Figure Description

[0076] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0077] Figure 1 This is a schematic flowchart of a satellite downlink wireless transmission method provided in an embodiment of the present invention;

[0078] Figure 2 This is a flowchart illustrating another satellite downlink wireless transmission method provided in an embodiment of the present invention;

[0079] Figure 3 This is a schematic diagram of the structure of a satellite downlink wireless transmission device provided in an embodiment of the present invention. Detailed Implementation

[0080] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0081] The satellite downlink wireless transmission method provided in this embodiment of the invention can be applied to a UAV-assisted satellite communication system. The system may include: a satellite, a UAV, and a user terminal. The satellite is equipped with M antennas, the UAV is equipped with L antennas, and the user terminal is equipped with N antennas.

[0082] The satellite includes a transmission unit, which is used to determine the optimized transmission covariance matrix and the optimized relay amplification matrix according to the satellite downlink wireless transmission method, and to perform downlink wireless transmission.

[0083] The drone includes a relay unit, which is used to receive the relay amplification matrix sent by the satellite, and uses the relay amplification matrix to amplify and forward the downlink transmission signal sent by the satellite.

[0084] The user terminal is used to receive downlink transmission signals after relay amplification and forwarding.

[0085] In one specific embodiment, the satellite antenna array, the UAV antenna array, and the ground user terminal antenna array can be arranged into different shapes according to preset installation conditions such as quantity. Optionally, the satellite antenna array, the UAV antenna array, and the ground user terminal antenna array can be arranged into a uniform panel antenna array. Specifically, the satellite is equipped with a uniform panel antenna array, and the number of antennas in the x-axis and y-axis directions of the satellite are respectively... M x and M y ,but M = M x M y The total number of antennas equipped for satellites; the drone is equipped with a uniform panel antenna array, the drone's shaft and The number of antennas in the axial direction are respectively and ,but The total number of antennas equipped for the drone; the user terminal is equipped with a uniform panel antenna array, and the user terminal's shaft and The number of antennas in the axial direction are respectively and ,but The total number of antennas provided for the user terminal. Specifically, this UAV-assisted satellite system can be a dual-hop MIMO communication system that includes a satellite or a gateway station connected to the satellite, the UAV, and the ground user terminal.

[0086] Figure 1 This is a flowchart illustrating a satellite downlink wireless transmission method according to an embodiment of the present invention. This specification provides the method operation steps as shown in the embodiments or flowcharts, but based on conventional or non-inventive methods, more or fewer operation steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only execution order. In actual system or server products, the method can be executed sequentially according to the embodiments or drawings, or in parallel (e.g., in a parallel processor or multi-threaded processing environment). Specifically, as shown... Figure 1 As shown, the above method may include:

[0087] S101: Obtain target statistical channel status information between the satellite and the user terminal;

[0088] In one specific embodiment, communication between the satellite and the user terminal is achieved through UAV relay. The target statistical channel status information may include the statistical channel status information corresponding to the communication channel between the satellite and the UAV, as well as the statistical channel status information corresponding to the communication channel between the UAV and the user terminal.

[0089] In an optional embodiment, the target statistical channel state information includes first statistical channel state information and second statistical channel state information; the acquisition of the target statistical channel state information between the satellite and the user terminal may include:

[0090] Receive detection signals sent by user terminals;

[0091] Based on the detection signal, the first average channel energy and the first spatial angle information corresponding to the satellite-to-aircraft communication channel are determined, as well as the second average channel energy and the second spatial angle information corresponding to the aircraft-to-aircraft communication channel.

[0092] Based on the first average channel energy and the first spatial angle information, the first statistical channel state information corresponding to the satellite communication channel is determined;

[0093] Based on the second average channel energy and the second spatial angle information, the second statistical information corresponding to the machine-end communication channel is determined;

[0094] The target statistical channel state information is determined based on the first statistical channel state information and the second statistical channel state information.

[0095] In a specific embodiment, the first statistical channel status information can be the statistical channel status information corresponding to the communication channel between the satellite and the UAV, and the second statistical channel status information can be the statistical channel status information corresponding to the communication channel between the UAV and the user terminal; the satellite-to-drone communication channel can be the communication channel between the satellite and the UAV, and the drone-to-drone communication channel can be the communication channel between the UAV and the user terminal.

[0096] In one specific embodiment, the target statistical channel state information can be obtained through the uplink detection process. During the uplink detection process, the user terminal periodically sends detection signals to the satellite. The satellite receives the detection signals sent by the user terminal and then, based on the detection signals, determines the first average channel energy and first spatial angle information corresponding to the satellite-to-aircraft communication channel, as well as the second average channel energy and second spatial angle information corresponding to the aircraft-to-aircraft communication channel. Next, using the first average channel energy and first spatial angle information, the first statistical channel state information corresponding to the satellite-to-aircraft communication channel is determined; using the second average channel energy and second spatial angle information, the second statistical channel state information corresponding to the aircraft-to-aircraft communication channel is determined; and based on the first and second statistical channel state information, the target statistical channel state information is determined. Optionally, the target statistical channel state information may include a direct path component matrix, a transmit-receive correlation matrix, a Ricean factor, and a link signal-to-noise ratio, wherein the transmit-receive correlation matrix may include a transmit correlation matrix on the UAV side and a receive correlation matrix on the user terminal side.

[0097] In the above embodiments, statistical channel state information is easy to obtain and use, and can be used for a long time. By obtaining the target statistical channel state information between the satellite and the user terminal, the reliability and efficiency of satellite communication can be improved.

[0098] S102: Using the target statistical channel state information, determine the target problem, which is to maximize the traversal reachability of the user terminal under the constraints of satellite transmit power and UAV relay power.

[0099] In one specific embodiment, the objective problem can be to maximize the traversal reachability of the user terminal under constraints of satellite transmission power and UAV relay power.

[0100] In an optional embodiment, the above-described method of determining the target problem using target statistical channel state information may include:

[0101] Using target statistical channel state information, establish satellite-to-aircraft channel model and terminal-to-aircraft channel model;

[0102] Based on the satellite channel model and the terminal channel model, the initial traversal reachable rate is determined as shown in equation (1):

[0103] (1);

[0104] Based on the initial traversal reachable rate, the traversal reachable rate is determined using the large-dimensional random matrix method, as shown in equation (2):

[0105] (2);

[0106] , , ,

[0107] , ,

[0108] ;

[0109] , , , ,

[0110] ;

[0111] , ;

[0112] Under the constraints of satellite transmission power and UAV relay power, the target problem is determined based on the traversal reachable rate, as shown in equation (3):

[0113] (3).

[0114] In a specific embodiment, the first and second statistical channel state information included in the target statistical channel state information can be used to determine the first model parameters corresponding to the satellite-to-aircraft channel model and the second model parameters corresponding to the aircraft-to-aircraft channel model, respectively. The first model parameters may include a first direct path component matrix, a first transmit-receive correlation matrix, a first Ricean factor, and the satellite-to-aircraft link signal-to-noise ratio. The second model parameters may include a second direct path component matrix, a second transmit-receive correlation matrix, a second Ricean factor, and the aircraft-to-aircraft link signal-to-noise ratio. Then, a satellite-to-aircraft channel model can be established based on the first model parameters, and an aircraft-to-aircraft channel model can be established based on the second model parameters. Optionally, the satellite-to-aircraft channel model can be represented as H1, and the specific matrix dimensions can be... L × M The machine-side channel model can be represented as H2, with specific matrix dimensions as follows: N × L Optionally, since there are fewer scattering objects around satellites and drones, the satellite-drone channel model can include only the first path component matrix. The ground user terminal is surrounded by abundant scatterers, and the terminal channel model can include a second direct path component. Non-direct path components The machine-side channel model follows a Rice distribution, which can be used as... .

[0115] In one specific embodiment, the second transmit-receive correlation matrix may include a UAV-side transmit correlation matrix and a user terminal-side receive correlation matrix; optionally, based on the satellite channel model and the terminal channel model, the UAV-side transmit correlation matrix T2, the user terminal-side receive correlation matrix R2, and the second Rice factor can be determined. Signal-to-noise ratio of satellite link ρ 1. Signal-to-noise ratio of the terminal link ρ 2, as shown in the following formula:

[0116] , , , , ;

[0117] in, Indicates the non-direct path component; Indicates the first average channel power; Indicates the second average channel power; Indicates the noise power of the satellite communication channel; n represents the noise power of the on-board communication channel; n1 represents the additive Gaussian noise on the UAV side, and follows the rule of law. n2 represents the additive Gaussian noise on the user terminal side, and follows the equation: , where I represents the identity matrix.

[0118] Optionally, the signal-to-noise ratio of the satellite link can be adjusted. ρ 1. Signal-to-noise ratio of the terminal link ρ 2 and the second Rice factor Include T2, R2, and And normalized to:

[0119] , , , ;

[0120] Based on the above normalization results, the achievable speed of the initial traversal can be further determined.

[0121] In a specific embodiment, based on the initial traversal reachability rate, the traversal reachability rate can be determined using the theory of large-dimensional random matrices, as shown in equation (2), and the variables in the traversal reachability rate can be specifically represented as follows:

[0122] , , ;

[0123] In the above formula ε 2. , ε 3 and It can be determined by the following fixed-point iterative equation:

[0124] , ,

[0125] , ;

[0126] The other variables in the above fixed-point iterative equation can be specifically represented as follows:

[0127] , , ,

[0128] , , , ;

[0129] and, , , These represent incorporating the transmission covariance matrix Q and the relay amplification matrix G into T2, respectively. and middle.

[0130] in, , , , , , and These can be variables related to the machine-side channel model H2.

[0131] In a specific embodiment, the objective problem can be to maximize the traversal reachability of the user terminal by jointly optimizing the transmission covariance matrix and the relay amplification matrix under satellite transmission power constraints and UAV relay power constraints. The determination of the objective problem using the aforementioned target statistical channel state information may further include: establishing a satellite-to-aircraft channel model and a terminal-to-device channel model using the target statistical channel state information; determining the initial traversal reachability based on the satellite-to-aircraft channel model and the terminal-to-device channel model, as shown in equation (1); and determining the initial objective problem based on the initial traversal reachability under satellite transmission power constraints and UAV relay power constraints, as shown in the following equation:

[0132] ;

[0133] in, This represents maximizing the initial traversal reachable rate; based on the initial traversal reachable rate, the traversal reachable rate is determined using a large-dimensional random matrix method, as shown in equation (2); under the constraints of satellite transmission power and UAV relay power, the target problem is determined based on the traversal reachable rate and the initial target problem, as shown in equation (3). By jointly optimizing the transmission covariance matrix and the relay amplification matrix to maximize the traversal reachable rate of the user terminal, the target problem can be determined based on the traversal reachable rate under the constraints of satellite transmission power and UAV relay power. Optionally, the transmission covariance matrix can be Q, and the matrix dimension can be [missing information]. M × M The relay amplification matrix can be G, and the matrix dimension can be 10 ... L × L .

[0134] In the above embodiments, since there is no direct link between the satellite and the user terminal, the user terminal relies on a UAV relay to receive signals. By defining the target problem, the transmission covariance matrix and the relay amplification matrix can be optimized based on the ergodic reachable rate under the constraints of satellite transmission power and UAV relay power. This optimized transmission covariance matrix and relay amplification matrix can then be used to achieve downlink transmission between the satellite and the user terminal, improving the reliability and efficiency of communication between them. Furthermore, based on the initial ergodic reachable rate, the ergodic reachable rate is determined using a large-dimensional random matrix method. Determining the target problem based on this ergodic reachable rate simplifies the optimization process of the transmission covariance matrix and relay amplification matrix, thereby improving the efficiency of optimization and further enhancing the efficiency of communication between the satellite and the user terminal.

[0135] S103: Based on the target problem, use the alternating optimization algorithm to update the transmission covariance matrix and the relay amplification matrix to obtain the optimized transmission covariance matrix and the optimized relay amplification matrix;

[0136] In one specific embodiment, the optimized transmission covariance matrix can be the transmission covariance matrix used for satellite-side downlink transmission and precoding to generate the transmitted signal; the optimized relay amplification matrix can be the relay amplification matrix used for UAV-side relay amplification and forwarding of the satellite transmitted signal.

[0137] In an optional embodiment, the above-described alternating optimization algorithm, based on the target problem, is used to update the transmission covariance matrix and the relay amplification matrix to obtain the optimized transmission covariance matrix and the optimized relay amplification matrix, which may include:

[0138] Based on the target problem, and using the current relay amplification matrix, the transmission covariance matrix is ​​updated to obtain the updated transmission covariance matrix;

[0139] The updated transmission covariance matrix is ​​determined as the current transmission covariance matrix. Based on the target problem, the relay amplification matrix is ​​updated based on the current transmission covariance matrix to obtain the updated relay amplification matrix.

[0140] The updated relay amplification matrix is ​​determined as the current relay amplification matrix. The process then proceeds to the step of updating the transmission covariance matrix based on the current relay amplification matrix, based on the target problem, to obtain the updated transmission covariance matrix. This process continues until the preset convergence condition is met, resulting in the optimized transmission covariance matrix and the optimized relay amplification matrix.

[0141] In a specific embodiment, the process of optimizing the transmission covariance matrix and the relay amplification matrix is ​​decomposed into two one-dimensional sub-optimization processes. Specifically, based on the target problem and the current relay amplification matrix (which remains unchanged), the transmission covariance matrix is ​​updated to obtain an updated transmission covariance matrix. This updated transmission covariance matrix can be determined as the current transmission covariance matrix. Based on the target problem and the current transmission covariance matrix (which remains unchanged), the relay method matrix is ​​updated to obtain an updated relay amplification matrix. This updated relay amplification matrix can be determined as the current relay amplification matrix. The process then returns to the previous step of updating the transmission covariance matrix based on the target problem and the current relay amplification matrix (which remains unchanged), until a preset convergence condition is met, resulting in the optimized transmission covariance matrix and the optimized relay amplification matrix. Optionally, the optimized transmission covariance matrix can be the optimal transmission covariance matrix determined after the above process and satisfying the preset convergence condition; the optimized relay amplification matrix can also be the optimal relay amplification matrix determined after the above process and satisfying the preset convergence condition. Optionally, the preset convergence condition can be set according to actual application requirements. Specifically, the preset convergence condition can include the difference between the traversal reachable rate obtained in the current iteration and the traversal reachable rate obtained in the previous iteration being less than a preset threshold, i.e. ,in, The traversal reachable speed obtained in the current iteration. The traversal reachable speed obtained from historical iterations. The threshold is set; or the number of iterations is greater than or equal to the preset number of iterations.

[0142] In the above embodiments, since the transmitting covariance matrix and the relay amplification matrix are coupled to each other, the above optimization process can decouple and optimize the transmitting covariance matrix and the relay amplification matrix, thereby improving the reliability, accuracy and simplification of the optimization of the transmitting covariance matrix and the relay amplification matrix, and thus improving the reliability of satellite communication with user terminals via UAVs.

[0143] In an optional embodiment, the above-described updating of the transmission covariance matrix based on the current relay amplification matrix, based on the target problem, to obtain the updated transmission covariance matrix, may include:

[0144] Based on the target problem, the first optimization problem is determined. The first optimization problem is used to update the transmission covariance matrix based on the current relay amplification matrix, as shown in equation (4):

[0145] (4);

[0146] in, Maximize ; represents the determinant of a matrix; Q represents the covariance matrix; G represents the relay amplification matrix; Indicates conjugate transpose; I M The dimension is M The identity matrix, M This indicates the total number of antennas equipped on the satellite; The first direct path component matrix represents the first statistical channel state information; MP 1 indicates the satellite's maximum transmission power. LP 2 indicates the maximum transmission power of the drone relay; These are intermediate variables used in calculations and have no specific physical meaning.

[0147] The first dual problem corresponding to the first optimization problem is determined as shown in equation (5):

[0148] (5);

[0149] (6);

[0150] in, This represents the first dual problem. Denotes the first dual function; Represents the first Lagrange multiplier. This represents the first Lagrange multiplier, corresponding to the UAV relay power constraint; Indicates the second Lagrange multiplier. This represents the second Lagrange multiplier, corresponding to the satellite transmission power constraint;

[0151] Based on the first optimization problem and the first dual problem, and combined with the current relay amplification matrix, the transmission covariance matrix is ​​optimized to obtain the updated transmission covariance matrix.

[0152] Based on the first optimization problem and the first dual problem, and combined with the current relay amplification matrix, the transmission covariance matrix is ​​optimized to obtain the updated transmission covariance matrix.

[0153] In a specific embodiment, based on the target problem, the transmission covariance matrix is ​​optimized separately while keeping the relay amplification matrix fixed. That is, the transmission covariance matrix is ​​updated based on the current relay amplification matrix, which determines the first optimization problem. Optionally, the first optimization problem can be maximizing the transmission covariance matrix when updating the transmission covariance matrix based on the current relay amplification matrix. The first optimization problem can be defined as updating the transmission covariance matrix by fixing the relay amplification matrix, and maximizing... Optionally, determining the first dual problem corresponding to the first optimization problem may include: determining the first Lagrangian function corresponding to the first optimization problem; determining the first dual problem based on the first Lagrangian function; optionally, the first Lagrangian function simplifies the optimization problem by introducing Lagrange multipliers to transform the above-mentioned first optimization problem into an unconstrained optimization problem. Specifically, the first Lagrangian function can be as follows:

[0154] ;

[0155] in, Represents the first Lagrange function; For Lagrange multipliers, the corresponding transmit covariance matrix is ​​a positive semidefinite matrix constraint;

[0156] The above-mentioned determination of the first dual problem based on the first Lagrange function may include determining the first dual function based on the first Lagrange function, as shown in equation (6), and further determining the first dual problem based on the first dual function, i.e., equation (5).

[0157] In a specific embodiment, the first and second Lagrange multipliers can be initialized to obtain initialized first and second Lagrange multipliers; based on the initialized first and second Lagrange multipliers, combined with the current relay amplification matrix, the first dual function is solved to update the transmission covariance matrix, resulting in an updated transmission covariance matrix; based on the updated transmission covariance matrix, combined with the current relay amplification matrix, the first dual problem is solved to update the first and second Lagrange multipliers, resulting in updated first and second Lagrange multipliers; based on the updated first and second Lagrange multipliers, the process returns to the step of combining the current relay amplification matrix to solve the first dual function, update the transmission covariance matrix, and obtain an updated transmission covariance matrix, until the first convergence condition is met, thus determining the optimal transmission covariance matrix, the optimal first Lagrange multiplier, and the optimal second Lagrange multiplier. Optionally, the optimal transmission covariance matrix can be the updated transmission covariance matrix corresponding to the current update process; the first convergence condition can be set according to actual needs, and the first convergence condition can be reaching a first preset number of iterations. Optionally, based on the updated transmission covariance matrix, the first dual problem is solved, and the first and second Lagrange multipliers can be updated using the subgradient method. about and The subgradients can be represented by the following equations:

[0158] and .

[0159] In an optional embodiment, the above-described optimization of the transmission covariance matrix based on the first optimization problem and the first dual problem, combined with the current relay amplification matrix, to obtain the updated transmission covariance matrix, may include:

[0160] Based on the processing of the first Lagrange function, the objective constraints are determined. The first Lagrange function is obtained based on the first optimization problem.

[0161] Based on the objective constraints, the first optimization problem is optimized to obtain the first objective optimization problem, as shown in equation (7):

[0162] (7);

[0163] in, This indicates how to find the determinant of a matrix; Maximize K represents variable substitution and has no specific physical meaning. ; Variable substitution is used to optimize the first optimization problem. ;

[0164] Based on the first objective optimization problem, the transmission covariance matrix is ​​solved using a water-filling algorithm to obtain the target transmission covariance matrix, as shown in equation (8):

[0165] (8);

[0166] in, V represents the target transmission covariance matrix; V represents eigenvector matrix; express eigenvalue matrix; express Eigenvalue matrix; Indicates a false reversal; Represents eigenvalue decomposition;

[0167] Based on the target transmission covariance matrix and the first dual problem, and in conjunction with the current relay amplification matrix, the updated transmission covariance matrix is ​​determined.

[0168] In a specific embodiment, the objective constraint can be a necessary condition for determining the optimal solution to the optimization problem; specifically, the objective constraint is the KKT condition. Optionally, the corresponding objective constraint can be determined by taking the partial derivative of the first Lagrange function with respect to the variable covariance matrix, as shown in the following equation:

[0169] ;

[0170] based on Therefore, the first equation in the above objective constraint can be determined as shown in the following equation:

[0171] ;

[0172] in, , At this point, based on the above objective constraints, the first optimization problem is optimized to obtain the first objective optimization problem as shown in equation (7).

[0173] Optionally, based on the first objective optimization problem, let the eigenvalues ​​be decomposed into... , It can be A diagonal matrix composed of eigenvalues, It can be The matrix composed of eigenvectors can be used to obtain the target transmission covariance matrix using a water-filling algorithm. The updated transmission covariance matrix can be determined based on the target transmission covariance matrix, which is the optimal transmission covariance matrix in the current optimization process.

[0174] In a specific embodiment, based on the specific representation of each variable in the traversal reachability described above, it can be determined that... It can be Therefore, in the process of determining the updated transmission covariance matrix, it is necessary to optimize the transmission covariance matrix, update the first and second Lagrange multipliers, and also update the function. , It can be the first variable. This can be a second variable. Based on the target transmission covariance matrix and the first dual problem, combined with the current relay amplification matrix, determining the updated transmission covariance matrix can include:

[0175] Initialize the first and second Lagrange multipliers, as well as the first and second variables, to obtain the initialized first and second Lagrange multipliers, and the initialized first and second variables. Based on the initialized first and second Lagrange multipliers, and the initialized first and second variables, update the transmission covariance matrix according to the target transmission covariance matrix and in conjunction with the current relay amplification matrix, to obtain the updated transmission covariance matrix. Based on the updated transmission covariance matrix and in conjunction with the current relay amplification matrix, solve the first dual problem to update the first and second Lagrange multipliers, to obtain the updated... The process involves using the first and second Lagrange multipliers after the initial transmission covariance matrix. Based on the updated transmission covariance matrix, and combining the equations and variables related to the first and second variables in the fixed-point iterative equation, along with the current relay amplification matrix, the first and second variables are updated to obtain the updated first and second variables. Based on the updated first and second Lagrange multipliers and the updated first and second variables, the process returns to the step of updating the transmission covariance matrix according to the target transmission covariance matrix and the current relay amplification matrix, until the first convergence condition is met, resulting in the updated transmission covariance matrix. Optionally, the above "until the first convergence condition is met" can also be "until the target transmission covariance matrix and..." convergence.

[0176] In one specific embodiment, the target transmission covariance matrix is ​​updated using satellite channel characteristics, as shown in the following equation:

[0177] ;

[0178] in, q express traces, , ; Represents a diagonal matrix The eigenvectors corresponding to the non-zero eigenvalues.

[0179] Optionally, based on the determined target transmission covariance matrix, a simplified rank-one transmission covariance matrix can be obtained by utilizing the rank-one property of the channel.

[0180] Optionally, based on the satellite channel characteristics, the rank of the updated transmission covariance matrix is ​​determined to be one, i.e. With a rank of one, it can be It can be determined as .

[0181] In the above embodiments, with the current relay amplification matrix fixed, the transmission covariance matrix is ​​updated to determine that the updated transmission covariance matrix has been determined. This facilitates the optimization of the transmission covariance matrix in the current optimization process, and further facilitates the optimization of the relay amplification matrix in subsequent optimization processes. This makes it easier to determine the optimized transmission covariance matrix and the optimized relay amplification matrix, improving the reliability and efficiency of determining the optimized transmission covariance matrix and the optimized relay amplification matrix. Furthermore, by optimizing the transmission covariance matrix based on the rank-one characteristic of the channel, the computational complexity of the algorithm can be further reduced.

[0182] In an optional embodiment, the above-described updating of the relay amplification matrix based on the current transmission covariance matrix, based on the target problem, to obtain the updated relay amplification matrix, may include:

[0183] Based on the target problem, a second optimization problem is determined. The second optimization problem is used to update the relay amplification matrix based on the current transmission covariance matrix, as shown in equation (9):

[0184] (9);

[0185] in, Maximize ; represents the determinant of a matrix; Q represents the covariance matrix; G represents the relay amplification matrix; Indicates conjugate transpose; I L The dimension is L The identity matrix, L This indicates the total number of antennas equipped on the drone; LP 2 indicates the maximum transmission power of the drone relay; The first direct path component matrix represents the first statistical channel state information; , and These are intermediate variables used in calculations and have no specific physical meaning.

[0186] The second dual function corresponding to the second optimization problem is determined as shown in equation (10):

[0187] (10);

[0188] in, Describe the second dual function; Indicates the third Lagrange multiplier; and These are intermediate variables used in calculations and have no specific physical meaning.

[0189] , ;

[0190] in, T1 represents the second direct path component matrix, which represents the second statistical channel state information; T2 represents the UAV-side transmission correlation matrix; R2 represents the user terminal-side reception correlation matrix. , , and These are intermediate variables used in calculations and have no specific physical meaning.

[0191] Based on the second dual function and the current transmission covariance matrix, the relay amplification matrix is ​​optimized using a nested loop algorithm to obtain the updated relay amplification matrix.

[0192] In one specific embodiment, based on the target problem, the relay amplification matrix is ​​optimized separately while maintaining the fixed transmission covariance matrix. That is, the relay amplification matrix is ​​updated based on the current transmission covariance matrix, which determines the second optimization problem. Optionally, when optimizing the relay amplification matrix separately while maintaining the fixed transmission covariance matrix, the achievable rates are traversed. Three of them are related to G, namely , and This can be obtained through a simple matrix transformation, as shown in the following equation:

[0193] ;

[0194] This allows us to determine the second optimization problem.

[0195] Optionally, the second optimization problem can be to update the relay amplification matrix based on the current transmission covariance matrix to maximize the... That is, the second optimization problem can be solved by updating the relay amplification matrix with a fixed transmission covariance matrix, and maximizing... .

[0196] Optionally, determining the second dual function corresponding to the second optimization problem may include: determining an initial dual function based on the second optimization problem; then, based on the initial dual function, taking the partial derivative with respect to the variable G to determine the second dual function. The initial dual function may be as follows:

[0197] ;

[0198] in, This represents the initial dual function. Optional. It can represent the second dual function determined by taking the partial derivative with respect to the variable G in the initial dual function.

[0199] In a specific embodiment, the above-described optimization of the relay amplification matrix based on the second dual function and the current transmission covariance matrix, using a nested loop algorithm, to obtain the updated relay amplification matrix may include: initializing the third Lagrange multiplier as the current third Lagrange multiplier; updating the current relay amplification matrix using gradient descent based on the current third Lagrange multiplier, according to the third dual function and combined with the current transmission covariance matrix, to obtain the updated relay amplification matrix; updating the current third Lagrange multiplier using a bisection method based on the updated relay amplification matrix, according to the third dual function and combined with the current transmission covariance matrix, to obtain the updated third Lagrange multiplier; and returning to the step of updating the current relay amplification matrix using gradient descent based on the third dual function and combined with the current transmission covariance matrix, to obtain the updated relay amplification matrix, until the second convergence condition is met, thus determining the updated relay amplification matrix. Optionally, the second convergence condition can be set according to actual needs, and the second convergence condition can be reaching a second preset number of iterations. Optionally, the loop can be stopped once the optimal third Lagrange multiplier has been determined, thus determining the updated relay amplification matrix. Alternatively, the gradient descent compensation can be determined using a backtracking search method to find a suitable step size that allows the function to descent rapidly.

[0200] In a specific embodiment, based on the specific representation of each variable in the traversal reachability described above, it can be determined that the third dual function includes the first variable. Second variable Third variable and the fourth variable In determining the updated relay amplification matrix, it is necessary to optimize the relay amplification matrix, update the third Lagrange multiplier, and update the first, second, third, and fourth variables. The above optimization of the relay amplification matrix based on the second dual function and the current transmission covariance matrix, using a nested loop algorithm, may also include:

[0201] Initialize the third Lagrange multiplier as the current third Lagrange multiplier, and initialize the first, second, third, and fourth variables as the current first, second, third, and fourth variables, respectively. Based on the current third Lagrange multiplier and the current first, second, third, and fourth variables, update the current relay amplification matrix using gradient descent according to the third dual function and the current transmission covariance matrix, obtaining the updated relay amplification matrix. Based on the updated relay amplification matrix, update the current third Lagrange multiplier using bisection according to the third dual function and the current transmission covariance matrix, obtaining the updated third Lagrange multiplier. The relay amplification matrix is ​​then used in conjunction with the equations and variables related to the first, second, third, and fourth variables in the aforementioned fixed-point iterative equations, and with the current transmission covariance matrix. This process updates the first, second, third, third, and fourth variables, yielding the updated first, second, third, third, and fourth variables. Based on the updated third Lagrange multiplier and the updated first, second, third, third, and fourth variables, the process returns to updating the current relay amplification matrix using gradient descent based on the third dual function and the current transmission covariance matrix, until the second convergence condition is met, confirming that the updated relay amplification matrix has been determined.

[0202] In the above embodiments, with the current transmission covariance matrix fixed, the relay amplification matrix is ​​updated to determine that the updated relay amplification matrix has been determined. This facilitates the optimization of the relay amplification matrix in the current optimization process, and in turn facilitates the optimization of the transmission covariance matrix in the subsequent optimization process. This makes it easier to determine the optimized relay amplification matrix and the optimized transmission covariance matrix, thereby improving the reliability and efficiency of determining the optimized relay amplification matrix and the optimized transmission covariance matrix.

[0203] S104: Based on the optimized transmission covariance matrix and optimized relay amplification matrix, signals are transmitted downlink to the user terminal via UAV relay.

[0204] In one specific embodiment, the satellite can generate a transmission signal by performing downlink precoding based on an optimized transmission covariance matrix. The satellite can then transmit the transmission signal and an optimized relay amplification matrix to a UAV. The UAV can amplify and relay the transmission signal using the optimized relay amplification matrix before sending it to the user terminal. Optionally, the signal received by the user terminal can be the transmission signal generated by downlink precoding using the optimized transmission covariance matrix, amplified and relayed by the UAV using the optimized relay amplification matrix. The user terminal will perform frequency and time compensation processing on the received signal.

[0205] In one specific embodiment Figure 2This is a flowchart illustrating another satellite downlink wireless transmission method provided in an embodiment of the present invention; as shown below. Figure 2 As shown, the above-mentioned satellite downlink wireless transmission method may include:

[0206] S201: Obtain target statistical channel status information between the satellite and the user terminal;

[0207] S202: Using the target statistical channel state information, determine the target problem, which is to maximize the traversal reachability of the user terminal under the constraints of satellite transmit power and UAV relay power.

[0208] S203: Based on the target problem, update the transmission covariance matrix based on the current relay amplification matrix to obtain the updated transmission covariance matrix;

[0209] S204: Determine the updated transmission covariance matrix as the current transmission covariance matrix. Based on the target problem, update the relay amplification matrix based on the current transmission covariance matrix to obtain the updated relay amplification matrix. Determine the updated relay amplification matrix as the current relay amplification matrix.

[0210] S205: Determine whether the preset convergence condition is met. If the preset convergence condition is met, proceed to step S206; if the preset convergence condition is not met, return to step S203.

[0211] S206: Determine the optimized transmission covariance matrix and the optimized relay amplification matrix, and based on the optimized transmission covariance matrix and the optimized relay amplification matrix, transmit signals downlink to the user terminal via UAV relay.

[0212] In one specific embodiment, as the satellite or user terminal moves, the first average channel energy and first spatial angle information corresponding to the satellite-to-satellite communication channel, as well as the second average channel energy and second spatial angle information corresponding to the terminal communication channel, will be dynamically updated, thereby adjusting and changing the transmission covariance matrix and relay amplification matrix accordingly.

[0213] As can be seen from the technical solutions provided in the embodiments of this specification above, this specification obtains target statistical channel state information between the satellite and the user terminal; using the target statistical channel state information, it determines the target problem, which is to maximize the traversal reachability of the user terminal under the constraints of satellite transmit power and UAV relay power; then, based on the target problem, it uses an alternating optimization algorithm to update the transmit covariance matrix and the relay amplification matrix, obtaining optimized transmit covariance matrix and optimized relay amplification matrix, which can improve the performance of optimizing transmit covariance matrix and relay amplification matrix, and improve the reliability of the optimization of transmit covariance matrix and relay amplification matrix, thereby... This facilitates the determination of low-complexity transmit covariance and relay amplification matrices. Then, based on optimized transmit covariance and relay amplification matrices, downlink signals are transmitted to user terminals via UAV relay. This allows for downlink wireless transmission of the satellite communication system with the assistance of UAVs, leveraging statistical channel state information. Furthermore, downlink wireless transmission reduces computational complexity and channel feedback overhead. Since statistical channel state information is readily available, easy to use, and can be used for extended periods, it improves the reliability of satellite communication. Additionally, using UAVs as communication relays enhances received signal strength, thereby improving the communication performance of satellite communication.

[0214] This invention also provides a satellite downlink wireless transmission device, accordingly, Figure 3 This is a schematic diagram of the structure of a satellite downlink wireless transmission device provided in an embodiment of the present invention; as shown below. Figure 3 As shown, the above-mentioned device includes:

[0215] Information acquisition module 310 is used to acquire target statistical channel status information between the satellite and the user terminal;

[0216] The optimization module 320 is used to determine the target problem using the target statistical channel state information. The target problem is to maximize the traversal reachability of the user terminal under the constraints of satellite transmit power and UAV relay power. Based on the target problem, the alternating optimization algorithm is used to update the transmit covariance matrix and the relay amplification matrix to obtain the optimized transmit covariance matrix and the optimized relay amplification matrix.

[0217] The signal transmission module 330 is used to transmit downlink signals to the user terminal via UAV relay based on the optimized transmission covariance matrix and the optimized relay amplification matrix.

[0218] Finally, it should be noted that the embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A satellite downlink wireless transmission method, characterized in that, The method includes: Acquire target statistical channel status information between the satellite and the user terminal; Using the target statistical channel state information, the target problem is determined, which is to maximize the traversal reachability of the user terminal under the constraints of satellite transmit power and UAV relay power. Based on the aforementioned objective problem, the alternating optimization algorithm is used to update the transmission covariance matrix and the relay amplification matrix, thereby obtaining the optimized transmission covariance matrix and the optimized relay amplification matrix. Based on the optimized transmission covariance matrix and the optimized relay amplification matrix, signals are transmitted downlink to the user terminal via UAV relay. The step of using the target statistical channel state information to determine the target problem includes: Using the target statistical channel state information, establish the satellite-to-aircraft channel model and the terminal-to-aircraft channel model; Based on the satellite channel model and the terminal channel model, the initial traversal reachable rate is determined as shown in equation (1): （1）； in, Indicates the initial traversal reachable speed; This represents the expected value of a random variable. This indicates how to find the determinant of a matrix; Denotes the conjugate transpose; Q represents the transmit covariance matrix, G represents the relay amplification matrix; H1 represents the satellite-to-aircraft channel model, H2 represents the receiver-to-aircraft channel model; I N The dimension is N The identity matrix, N This indicates the total number of antennas equipped in the user terminal; Based on the initial traversal reachable rate, the traversal reachable rate is determined using the large-dimensional random matrix method, as shown in equation (2): （2）； in, Indicates the traversal reachability; I M The dimension is M The identity matrix, M I represents the total number of antennas equipped on the satellite. L The dimension is L The identity matrix, L R1 represents the total number of antennas equipped on the UAV; R2 represents the receiver correlation matrix on the user terminal side. , , , , , and These are intermediate variables used in calculations and have no specific physical meaning. ， ， ， ， ， ； in, The matrix representing the first direct path component of the first statistical channel state information. T2 represents the second direct path component matrix, which represents the second statistical channel state information; T2 represents the UAV-side transmission correlation matrix. This indicates finding the trace of a matrix; , , and These are intermediate variables used in calculations and have no specific physical meaning. ， ， ， ， ； in, and These are intermediate variables used in calculations and have no specific physical meaning. ， ； Under the constraints of satellite transmission power and UAV relay power, the target problem is determined based on the traversal reachable rate, as shown in equation (3): （3）； in, This indicates maximizing the traversal reachability. This indicates a constraint on satellite launch power. This indicates the relay power constraint for drones. MP 1 indicates the satellite's maximum transmission power. LP 2 indicates the maximum transmission power of the drone relay; The matrix representing the first direct path component of the first statistical channel state information.

2. The satellite downlink wireless transmission method according to claim 1, characterized in that, Based on the aforementioned objective problem, the alternating optimization algorithm is used to update the transmission covariance matrix and the relay amplification matrix, resulting in optimized transmission covariance matrix and optimized relay amplification matrix, including: Based on the aforementioned target problem, and using the current relay amplification matrix, the transmission covariance matrix is ​​updated to obtain the updated transmission covariance matrix; The updated transmission covariance matrix is ​​determined as the current transmission covariance matrix. Based on the target problem, the relay amplification matrix is ​​updated based on the current transmission covariance matrix to obtain the updated relay amplification matrix. The updated relay amplification matrix is ​​determined as the current relay amplification matrix. The process then proceeds to the step of updating the transmission covariance matrix based on the current relay amplification matrix, based on the target problem, to obtain the updated transmission covariance matrix. This process continues until a preset convergence condition is met, resulting in the optimized transmission covariance matrix and the optimized relay amplification matrix.

3. The satellite downlink wireless transmission method according to claim 2, characterized in that, Based on the aforementioned target problem, and using the current relay amplification matrix, the transmission covariance matrix is ​​updated to obtain the updated transmission covariance matrix, including: Based on the target problem, a first optimization problem is determined, which is used to update the transmission covariance matrix based on the current relay amplification matrix, as shown in equation (4): （4）； in, Maximize ; represents the determinant of a matrix; Q represents the covariance matrix; G represents the relay amplification matrix; Indicates conjugate transpose; I M The dimension is M The identity matrix, M This indicates the total number of antennas equipped on the satellite; The first direct path component matrix represents the first statistical channel state information; MP 1 indicates the satellite's maximum transmission power. LP 2 indicates the maximum transmission power of the drone relay; These are intermediate variables used in calculations and have no specific physical meaning. The first dual problem corresponding to the first optimization problem is determined as shown in equation (5): （5）； （6）； in, This represents the first dual problem. Denotes the first dual function; Represents the first Lagrange multiplier. This indicates the UAV relay power constraint corresponding to the first Lagrange multiplier; Indicates the second Lagrange multiplier. This indicates the satellite transmit power constraint corresponding to the second Lagrange multiplier; Based on the first optimization problem and the first dual problem, and combined with the current relay amplification matrix, the transmission covariance matrix is ​​optimized to obtain the updated transmission covariance matrix.

4. The satellite downlink wireless transmission method according to claim 3, characterized in that, The step of optimizing the transmission covariance matrix based on the first optimization problem and the first dual problem, combined with the current relay amplification matrix, to obtain the updated transmission covariance matrix includes: Based on the processing of the first Lagrange function, the objective constraints are determined. The first Lagrange function is obtained based on the first optimization problem. Based on the objective constraints, the first optimization problem is optimized to obtain the first objective optimization problem, as shown in equation (7): （7）； in, This indicates how to find the determinant of a matrix; Maximize K represents variable substitution and has no specific physical meaning. ; Variable substitution is used to optimize the first optimization problem. ; Based on the first objective optimization problem, the transmission covariance matrix is ​​solved using a water-filling algorithm to obtain the target transmission covariance matrix, as shown in equation (8): （8）； in, V represents the target transmission covariance matrix; V represents eigenvector matrix; express eigenvalue matrix; express Eigenvalue matrix; Indicates a false reversal; Represents eigenvalue decomposition; Based on the target transmission covariance matrix and the first dual problem, and in conjunction with the current relay amplification matrix, the updated transmission covariance matrix is ​​determined.

5. The satellite downlink wireless transmission method according to claim 2, characterized in that, Based on the target problem, and based on the current transmission covariance matrix, the relay amplification matrix is ​​updated to obtain the updated relay amplification matrix, including: Based on the target problem, a second optimization problem is determined, which is used to update the relay amplification matrix based on the current transmission covariance matrix, as shown in equation (9): （9）； in, Maximize ; represents the determinant of a matrix; Q represents the covariance matrix; G represents the relay amplification matrix; Indicates conjugate transpose; I L The dimension is L The identity matrix, L This indicates the total number of antennas equipped on the drone; LP 2 indicates the maximum transmission power of the drone relay; The first direct path component matrix represents the first statistical channel state information; , and These are intermediate variables used in calculations and have no specific physical meaning. The second dual function corresponding to the second optimization problem is determined as shown in equation (10): （10）； in, Describe the second dual function; Indicates the third Lagrange multiplier; and These are intermediate variables used in calculations and have no specific physical meaning. ， ； in, T1 represents the second direct path component matrix, which represents the second statistical channel state information; T2 represents the UAV-side transmission correlation matrix; R2 represents the user terminal-side reception correlation matrix. , , and These are intermediate variables used in calculations and have no specific physical meaning. Based on the second dual function and the current transmission covariance matrix, the relay amplification matrix is ​​optimized using a nested loop algorithm to obtain the updated relay amplification matrix.

6. The satellite downlink wireless transmission method according to claim 1, characterized in that, The target statistical channel state information includes first statistical channel state information and second statistical channel state information; the acquisition of the target statistical channel state information between the satellite and the user terminal includes: Receive the detection signal sent by the user terminal; Based on the detection signal, the first average channel energy and the first spatial angle information corresponding to the satellite communication channel are determined, and the second average channel energy and the second spatial angle information corresponding to the terminal communication channel are determined. Based on the first average channel energy and the first spatial angle information, the first statistical channel state information corresponding to the satellite communication channel is determined; Based on the second average channel energy and the second spatial angle information, the second statistical signal information corresponding to the machine-side communication channel is determined; The target statistical channel state information is determined based on the first statistical channel state information and the second statistical channel state information.

7. A satellite downlink wireless transmission device, characterized in that, The device includes: The information acquisition module is used to acquire target statistical channel status information between the satellite and the user terminal; The optimization module is used to determine the target problem using the target statistical channel state information. The target problem is to maximize the traversal reachability rate of the user terminal under satellite transmit power constraints and UAV relay power constraints. Based on the target problem, the module uses an alternating optimization algorithm to update the transmit covariance matrix and the relay amplification matrix to obtain the optimized transmit covariance matrix and the optimized relay amplification matrix. Determining the target problem using the target statistical channel state information includes: Using the target statistical channel state information, establish the satellite-to-aircraft channel model and the terminal-to-aircraft channel model; Based on the satellite channel model and the terminal channel model, the initial traversal reachable rate is determined as shown in equation (1): （1）； in, Indicates the initial traversal reachable speed; This represents the expected value of a random variable. This indicates how to find the determinant of a matrix; Denotes the conjugate transpose; Q represents the transmit covariance matrix, G represents the relay amplification matrix; H1 represents the satellite-to-aircraft channel model, H2 represents the receiver-to-aircraft channel model; I N The dimension is N The identity matrix, N This indicates the total number of antennas equipped in the user terminal; Based on the initial traversal reachable rate, the traversal reachable rate is determined using the large-dimensional random matrix method, as shown in equation (2): （2）； in, Indicates the traversal reachability; I M The dimension is M The identity matrix, M I represents the total number of antennas equipped on the satellite. L The dimension is L The identity matrix, L R1 represents the total number of antennas equipped on the UAV; R2 represents the receiver correlation matrix on the user terminal side. , , , , , and These are intermediate variables used in calculations and have no specific physical meaning. ， ， ， ， ， ； in, The matrix representing the first direct path component of the first statistical channel state information. T2 represents the second direct path component matrix, which represents the second statistical channel state information; T2 represents the UAV-side transmission correlation matrix. This indicates finding the trace of a matrix; , , and These are intermediate variables used in calculations and have no specific physical meaning. ， ， ， ， ； in, and These are intermediate variables used in calculations and have no specific physical meaning. ， ； Under the constraints of satellite transmission power and UAV relay power, the target problem is determined based on the traversal reachable rate, as shown in equation (3): （3）； in, This indicates maximizing the achievable traversal rate; This indicates a constraint on satellite launch power. This indicates the relay power constraint for drones. MP 1 indicates the satellite's maximum transmission power. LP 2 indicates the maximum transmission power of the drone relay; The first direct path component matrix represents the first statistical channel state information; The signal transmission module is used to transmit downlink signals to the user terminal via UAV relay based on the optimized transmission covariance matrix and the optimized relay amplification matrix.

8. An electronic device comprising a processor and a memory, the memory storing at least one instruction and at least one program, the at least one instruction and the at least one program being loaded and executed by the processor to implement the satellite downlink wireless transmission method as claimed in any one of claims 1 to 6.

9. A computer storage medium storing at least one instruction and at least one program, the at least one instruction and the at least one program being loaded and executed by a processor to implement the satellite downlink wireless transmission method as described in any one of claims 1 to 6.

Images