Reconfigurable holographic surface beam forming method and system based on depth expansion

Abstract

The invention provides a reconfigurable holographic surface beam forming method and system based on depth expansion, and belongs to the technical field of communication. The reconfigurable holographic surface is used for assisting a satellite to establish a communication relationship with a plurality of users, and the method comprises the following steps: obtaining a direction angle from the reconfigurable holographic surface to each user, and an antenna performance parameter, a feed source position vector and each antenna position vector of the reconfigurable holographic surface, and combining an amplitude ratio of each user to obtain a reconfigurable holographic surface; determining a beam forming model of the reconfigurable holographic surface; the method comprises the following steps: acquiring the total transmitting power of a reconfigurable holographic surface, interference noise information from a satellite to each user and at least one channel matrix, and determining an optimization problem model aiming at maximizing the total user rate by combining a beam forming model of the reconfigurable holographic surface and a phase shift receiving vector of each user, and solving by adopting a depth expansion technology so as to determine a beam forming matrix of the reconfigurable holographic surface. According to the invention, the calculation complexity of the reconfigurable holographic surface can be reduced.

Description

Technical Field

[0001] This application relates to the field of communication technologies, and particularly to a reconfigurable holographic surface beamforming method and system based on deep unfolding. Background Art

[0002] In the design of reconfigurable holographic surfaces, all antennas are arranged on waveguides with additional feeders. Electromagnetic waves are injected into the waveguides through the feeders, and each antenna controls the amplitude of the incident electromagnetic waves to generate the required beam. Through this holographic beamforming technology, the reconfigurable holographic surface can accurately adjust the direction and shape of the beam to adapt to different communication requirements, enabling the satellite communication system to effectively overcome the impact of path attenuation and provide a more reliable and stable communication connection than traditional phased array systems. However, since the computational complexity of the reconfigurable holographic surface is proportional to the cube of the number of transmitting antennas, although traditional model-driven holographic beamforming algorithms are gradually maturing, when the number of transmitting antennas on the reconfigurable holographic surface is large, there is still a problem of excessively high computational complexity. Summary of the Invention

[0003] The main objective of this application is to propose a reconfigurable holographic surface beamforming method and system based on deep unfolding, aiming to reduce the computational complexity of the reconfigurable holographic surface and effectively optimize the beamforming performance of the reconfigurable holographic surface.

[0004] To achieve the above objective, on the one hand, this application proposes a reconfigurable holographic surface beamforming method based on deep unfolding. The reconfigurable holographic surface is used to assist in establishing a communication relationship between a satellite and multiple users. The method includes:

[0005] Obtain the direction angles from the reconfigurable holographic surface to each of the users, as well as the antenna performance parameters, feed source position vectors, and each antenna position vector of the reconfigurable holographic surface, and combine the amplitude ratios of each of the unknown users to determine the beamforming model of the reconfigurable holographic surface;

[0006] Obtain the total transmission power of the reconfigurable holographic surface, the interference noise information from the satellite to each of the users, and at least one channel matrix, and combine the beamforming model of the reconfigurable holographic surface and the phase shift reception vectors of each of the unknown users to determine an optimization problem model with the goal of maximizing the total user rate;

[0007] Use deep unfolding technology to solve the optimization problem model to determine the beamforming matrix of the reconfigurable holographic surface;

[0008] Among them, the amplitude ratio of each user represents the ratio of the amplitude of the received signal of the user to the amplitude of the transmitted signal of the reconfigurable holographic surface, and the phase shift received vector of each user represents the degree of phase adjustment of the received signal of the user.

[0009] Further, the antenna performance parameters include antenna wavelength, antenna efficiency, and antenna attenuation coefficient. The beamforming model of the reconfigurable holographic surface is obtained through the following steps:

[0010] According to the antenna wavelength and the direction angles from the reconfigurable holographic surface to each user, determine the desired propagation vectors of the reconfigurable holographic surface in the directions of each user.

[0011] According to the antenna wavelength, the feed position vector of the reconfigurable holographic surface, and each antenna position vector, determine the reference wave propagation vectors of each antenna of the reconfigurable holographic surface.

[0012] According to the desired propagation vectors of the reconfigurable holographic surface in the directions of each user, the reference wave propagation vectors of each antenna of the reconfigurable holographic surface, and each antenna position vector, determine the radiation amplitudes from each antenna of the reconfigurable holographic surface to each user.

[0013] According to the amplitude ratio of each user and the radiation amplitudes from each antenna of the reconfigurable holographic surface to each user, determine the radiation amplitude models of each antenna of the reconfigurable holographic surface.

[0014] According to the antenna efficiency, the antenna attenuation coefficient, the radiation amplitude models of each antenna of the reconfigurable holographic surface, the reference wave propagation vectors of each antenna, and each antenna position vector, determine the beamforming model of the reconfigurable holographic surface.

[0015] Further, the step of determining the beamforming model of the reconfigurable holographic surface according to the antenna efficiency, the antenna attenuation coefficient, the radiation amplitude models of each antenna of the reconfigurable holographic surface, the reference wave propagation vectors of each antenna, and each antenna position vector includes:

[0016] According to the reference wave propagation vectors of each antenna of the reconfigurable holographic surface and each antenna position vector, determine the phase attenuation coefficients when the feed of the reconfigurable holographic surface propagates the corresponding reference waves to each antenna.

[0017] According to the antenna attenuation coefficient and each antenna position vector of the reconfigurable holographic surface, determine the amplitude attenuation coefficients when the feed of the reconfigurable holographic surface propagates the corresponding reference waves to each antenna.

[0018] Determine the beamforming model of the reconfigurable holographic surface according to the antenna efficiency, each antenna radiation amplitude model of the reconfigurable holographic surface, and the phase attenuation coefficient and amplitude attenuation coefficient when the feed of the reconfigurable holographic surface propagates the corresponding reference wave to each antenna.

[0019] Further, the optimization problem model is obtained in the following manner:

[0020] Determine the received signal model of each user according to the interference noise information from the satellite to each user, at least one channel matrix, the total transmit power of the reconfigurable holographic surface, and the beamforming model.

[0021] Determine the total user rate model according to the received signal model of each user and the phase shift receive vector.

[0022] Determine the optimization problem model according to the total user rate model and the preset constraints on the amplitude ratio and phase shift receive vector of each user.

[0023] Further, the method of using the deep unfolding technique to solve the optimization problem model to determine the beamforming matrix of the reconfigurable holographic surface includes:

[0024] Use the deep unfolding technique to solve the optimization problem model to obtain the optimal values of the amplitude ratios of each user.

[0025] Input the optimal values of the amplitude ratios of each user into the beamforming model of the reconfigurable holographic surface for solution to obtain the beamforming matrix of the reconfigurable holographic surface.

[0026] Further, the method of using the deep unfolding technique to solve the optimization problem model to obtain the optimal values of the amplitude ratios of each user includes:

[0027] Determine the objective function according to the optimization problem model.

[0028] Randomly initialize the amplitude ratios and phase shift receive vectors of each user, and then input the randomly initialized amplitude ratios and phase shift receive vectors of each user into a preset deep unfolding network, where the deep unfolding network is designed based on the iterative principle of the gradient projection method.

[0029] Iteratively train the deep unfolding network according to the objective function and the preset maximum number of training epochs to obtain the optimal values of the amplitude ratios of each user.

[0030] Further, each round of the training process of the deep unfolding network includes:

[0031] Obtain the current values of the amplitude ratios and phase shift receiving vectors of each of the users;

[0032] Obtain the current values of the first gradient step size and the second gradient step size, where the first gradient step size is used to assist in updating the amplitude ratios of each of the users, and the second gradient step size is used to assist in updating the phase shift receiving vectors of each of the users;

[0033] In the forward propagation stage of the deep unfolding network, use the number of layers of the deep unfolding network as the number of iterations of the gradient projection method, and update the current values of the amplitude ratios and phase shift receiving vectors of each of the users by using the gradient projection method according to the objective function and the current values of the first gradient step size and the second gradient step size;

[0034] In the backward propagation stage of the deep unfolding network, update the current values of the first gradient step size and the second gradient step size according to the objective function.

[0035] To achieve the above object, another aspect of the present application proposes a reconfigurable holographic surface beamforming system based on deep unfolding. The reconfigurable holographic surface is used to assist in establishing a communication relationship between a satellite and multiple users. The system includes:

[0036] A first determination module, configured to obtain the direction angles from the reconfigurable holographic surface to each of the users, as well as the antenna performance parameters, feed position vectors, and each antenna position vector of the reconfigurable holographic surface, and determine the beamforming model of the reconfigurable holographic surface in combination with the unknown amplitude ratios of each of the users;

[0037] A second determination module, configured to obtain the total transmission power of the reconfigurable holographic surface, as well as the interference noise information and at least one channel matrix from the satellite to each of the users, and determine an optimization problem model with the goal of maximizing the total user rate in combination with the beamforming model of the reconfigurable holographic surface and the unknown phase shift receiving vectors of each of the users;

[0038] A solution module, configured to solve the optimization problem model by using deep unfolding technology to determine the beamforming matrix of the reconfigurable holographic surface;

[0039] Wherein, the amplitude ratio of each of the users represents the ratio of the amplitude of the received signal of the user to the amplitude of the transmitted signal of the reconfigurable holographic surface, and the phase shift receiving vector of each of the users represents the degree of phase adjustment of the received signal of the user.

[0040] To achieve the above object, another aspect of the present application proposes an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the above method is implemented.

[0041] To achieve the above object, on the other hand, the present application proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above method.

[0042] The present application has at least the following beneficial effects: By using the direction angles from the reconfigurable holographic surface to each user, the amplitude ratios of each user, as well as the antenna performance parameters, feed position vectors, and each antenna position vector of the reconfigurable holographic surface, a beamforming model of the reconfigurable holographic surface is constructed. Further, by combining the interference noise information from the satellite to each user, at least one channel matrix, the total transmission power of the reconfigurable holographic surface, and the phase shift reception vectors of each user, an optimization problem model aiming to maximize the total user rate is constructed. Finally, by using the deep unfolding technique for solution, a more accurate and reliable beamforming matrix of the reconfigurable holographic surface can be obtained, and the computational complexity of the reconfigurable holographic surface can be reduced. Description of the Drawings

[0043] Figure 1 is a schematic flowchart of a method for reconfigurable holographic surface beamforming based on deep unfolding provided by an embodiment of the present application;

[0044] Figure 2 is a schematic diagram of the structural composition of a system for reconfigurable holographic surface beamforming based on deep unfolding provided by an embodiment of the present application;

[0045] Figure 3 is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present application. Detailed Embodiments

[0046] In order to make the object, technical solution, and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of systems and methods that are consistent with some aspects of the embodiments of the present application detailed in the appended claims.

[0047] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if", "when" as used herein may be interpreted as "when...", "while...", or "in response to determining".

[0048] The terms "at least one", "a plurality of", "each", "any one", etc. used in this application, at least one includes one, two or more than two, a plurality of includes two or more than two, each refers to each of the corresponding plurality, and any one refers to any one of the plurality.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0050] Before elaborating on the embodiments of this application in detail, some nouns and terms involved in the embodiments of this application are first explained, and the nouns and terms involved in the embodiments of this application are applicable to the following explanations.

[0051] The deep unfolding technology combines the traditional model-driven method with the data-driven deep learning technology, not only making full use of the structured information of the model, but also being able to optimize the performance by learning the patterns in the data. The deep unfolding technology can retain the basic theory in the communication field and, with the help of the powerful capabilities of deep learning, has good interpretability and excellent performance.

[0052] The gradient projection method is a method for solving the optimal solution of a constrained nonlinear programming problem. By using the projection technique of the gradient, it searches on the boundary of a convex constraint set and gradually approaches the optimal solution. The basic principle of the gradient projection method is to start from a basic feasible solution, determine the projection direction of the gradient through the constraint conditions, and then perform iterative updates until the accuracy requirement is met.

[0053] The Reconfigurable Holographic Surface (RHS) is a solution that effectively overcomes the path loss in satellite communication systems. It can generate multi-directional beams by superimposing holographic patterns. Its main advantage is that it can integrate thousands of antennas into a compact space, thereby achieving significant beamforming gain.

[0054] In the design of reconfigurable holographic surfaces, all antennas are arranged on waveguides with additional feeders. Electromagnetic waves are injected into the waveguides through the feeders, and each antenna is used to control the amplitude of the incident electromagnetic waves to generate the desired beam. Through this holographic beamforming technology, the reconfigurable holographic surface can accurately adjust the direction and shape of the beam to adapt to different communication requirements, enabling the satellite communication system to effectively overcome the influence of path attenuation and provide a more reliable and stable communication connection than traditional phased array systems. However, since the computational complexity of the reconfigurable holographic surface is proportional to the cube of the number of transmitting antennas, although traditional model-driven holographic beamforming algorithms are gradually maturing, when the number of transmitting antennas on the reconfigurable holographic surface is large, there is still a problem of excessively high computational complexity. In addition, traditional deep learning methods solve the holographic beamforming problem through data-driven end-to-end learning, but these methods abandon the framework of traditional models, resulting in a lack of interpretability and failing to achieve satisfactory results in some cases.

[0055] In view of this, the embodiments of the present application provide a method and system for reconfigurable holographic surface beamforming based on deep unfolding, aiming to reduce the computational complexity of the reconfigurable holographic surface and effectively optimize the beamforming performance of the reconfigurable holographic surface.

[0056] A method for reconfigurable holographic surface beamforming based on deep unfolding provided by the embodiments of the present application relates to the field of communication technologies and can be applied to terminals, servers, or software running on terminals or servers. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, a vehicle-mounted terminal, etc., but is not limited thereto; the server side can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application implementing the above-mentioned method for reconfigurable holographic surface beamforming based on deep unfolding, etc., but is not limited to the above forms.

[0057] This application can be used in numerous general-purpose or special-purpose computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This application can also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0058] Please refer to Figure 1 , Figure 1 FIG. is an alternative flowchart of a reconfigurable holographic surface beamforming method based on deep unfolding provided by an embodiment of this application. The reconfigurable holographic surface is used to assist in establishing a communication relationship between a satellite and multiple users. All transmitting antennas on the reconfigurable holographic surface are arranged in a two-dimensional array, and the wavelength of each transmitting antenna on the reconfigurable holographic surface is the same. Each user is configured with multiple receiving antennas. This method may but is not limited to include the following steps S101 to S103:

[0059] Step S101: Obtain the direction angles from the reconfigurable holographic surface to each user, as well as the antenna performance parameters, feed position vectors, and each antenna position vector of the reconfigurable holographic surface. Combine the amplitude ratios of each user that are unknown to determine the beamforming model of the reconfigurable holographic surface;

[0060] Step S102: Obtain the total transmission power of the reconfigurable holographic surface, the interference noise information from the satellite to each user, and at least one channel matrix. Combine the beamforming model of the reconfigurable holographic surface and the phase shift receiving vectors of each user that are unknown to determine an optimization problem model with the goal of maximizing the total user rate;

[0061] Step S103: Use deep unfolding technology to solve the optimization problem model to determine the beamforming matrix of the reconfigurable holographic surface.

[0062] Steps S101 to S103 provided by the embodiments of the present application construct a beamforming model of the reconfigurable holographic surface by using the direction angles from the reconfigurable holographic surface to each user, the amplitude ratios of each user, and the antenna performance parameters, feed source position vector, and each antenna position vector of the reconfigurable holographic surface. Then, further combined with the interference noise information from the satellite to each user, at least one channel matrix, the total transmission power of the reconfigurable holographic surface, and the phase shift reception vectors of each user, an optimization problem model aiming to maximize the total user rate is constructed. Finally, the deep unfolding technology is used for solving, and a more accurate and reliable beamforming matrix of the reconfigurable holographic surface can be obtained, reducing the computational complexity of the reconfigurable holographic surface.

[0063] In step S101 of some embodiments, the antenna performance parameters of the reconfigurable holographic surface include antenna wavelength, antenna efficiency, and antenna attenuation coefficient. The amplitude ratio of each user refers to the ratio of the amplitude of the received signal of the user to the amplitude of the transmitted signal of the reconfigurable holographic surface. Regarding the determination process of the beamforming model of the reconfigurable holographic surface, it may include, but is not limited to, the following steps S201 to S205:

[0064] Step S201: According to the antenna wavelength and the direction angles from the reconfigurable holographic surface to each user, determine the desired propagation vectors of the reconfigurable holographic surface in the directions of each user, which can be calculated using the following expression:

[0065]

[0066] In the formula, is the desired propagation vector of the reconfigurable holographic surface in the direction of the l-th user, is the direction angle from the reconfigurable holographic surface to the l-th user, θ l is the azimuth angle from the reconfigurable holographic surface to the l-th user, is the elevation angle from the reconfigurable holographic surface to the l-th user, λ is the antenna wavelength, and T is the transpose symbol.

[0067] Step S202: According to the antenna wavelength, the feed source position vector of the reconfigurable holographic surface, and each antenna position vector, determine the reference wave propagation vectors of each antenna of the reconfigurable holographic surface, which can be calculated using the following expression:

[0068]

[0069] In the formula, k st is the reference wave propagation vector of the t-th antenna of the reconfigurable holographic surface, r f is the position vector of the feed source of the reconfigurable holographic surface and can be defined as r f=[0,0,0] T , r nt is the position vector of the t-th antenna of the reconfigurable holographic surface and r nt =[n tx d x , n ty d y , 0] T , assuming the number of antennas on the reconfigurable holographic surface is N t =N x N y and arranged in a two-dimensional array form, N x is the number of antennas arranged along the X-axis direction on the reconfigurable holographic surface, that is, the number of antenna columns on the reconfigurable holographic surface, N y is the number of antennas arranged along the Y-axis direction on the reconfigurable holographic surface, that is, the number of antenna rows on the reconfigurable holographic surface, n tx is the column number where the t-th antenna of the reconfigurable holographic surface is located and n tx ={1, 2,..., N x}, n ty is the row number where the t-th antenna of the reconfigurable holographic surface is located and n ty ={1, 2,..., N y}, d x is the spacing between two adjacent antennas arranged along the X-axis direction on the reconfigurable holographic surface, d y is the spacing between two adjacent antennas arranged along the Y-axis direction on the reconfigurable holographic surface, ||·||2 represents the L2 norm, that is, the Euclidean norm.

[0070] Step S203, according to the expected propagation vectors of the reconfigurable holographic surface in the directions of each user, and the reference wave propagation vectors and position vectors of each antenna of the reconfigurable holographic surface, determine the radiation amplitudes from each antenna of the reconfigurable holographic surface to each user, which can be calculated using the following expression:

[0071]

[0072] In the formula, is the radiation amplitude from the t-th antenna of the reconfigurable holographic surface to the l-th user.

[0073] Step S204, according to the amplitude ratios of each user and the radiation amplitudes from each antenna of the reconfigurable holographic surface to each user, determine the radiation amplitude model of each antenna of the reconfigurable holographic surface as follows:

[0074]

[0075] In the formula, b ntis the radiation amplitude model of the t-th antenna of the reconfigurable holographic surface, L is the number of multiple users assisted by the reconfigurable holographic surface, and a l is the amplitude ratio of the l-th user.

[0076] Step S205: Determine the beamforming model of the reconfigurable holographic surface according to the antenna efficiency, the antenna attenuation coefficient, and the radiation amplitude models of each antenna of the reconfigurable holographic surface, the propagation vectors of each antenna reference wave, and the position vectors of each antenna.

[0077] In this step, first, according to the propagation vectors of each antenna reference wave and the position vectors of each antenna of the reconfigurable holographic surface, determine the phase attenuation coefficient when the feeder of the reconfigurable holographic surface propagates the corresponding reference wave to each antenna; and according to the antenna attenuation coefficient and the position vectors of each antenna of the reconfigurable holographic surface, determine the amplitude attenuation coefficient when the feeder of the reconfigurable holographic surface propagates the corresponding reference wave to each antenna; then, according to the antenna efficiency, the radiation amplitude models of each antenna of the reconfigurable holographic surface, and the phase attenuation coefficient and amplitude attenuation coefficient when the feeder of the reconfigurable holographic surface propagates the corresponding reference wave to each antenna, determine the beamforming model of the reconfigurable holographic surface as follows:

[0078]

[0079] where m nt is the beamforming coefficient of the t-th antenna of the reconfigurable holographic surface, η is the antenna efficiency, is the phase attenuation coefficient when the feeder of the reconfigurable holographic surface propagates the corresponding reference wave to the t-th antenna, j is the imaginary unit, is the amplitude attenuation coefficient when the feeder of the reconfigurable holographic surface propagates the corresponding reference wave to the t-th antenna, α is the antenna attenuation coefficient, and m represents the beamforming model of the reconfigurable holographic surface, which includes the beamforming coefficients of all antennas of the reconfigurable holographic surface.

[0080] Steps S201 to S205 provided by the embodiments of the present application, by using the desired propagation vectors of the reconfigurable holographic surface in the directions where each user is located, the propagation vectors of each antenna reference wave of the reconfigurable holographic surface, and the position vectors of each antenna to calculate the radiation amplitudes from each antenna of the reconfigurable holographic surface to each user, and then further combining the amplitude ratios of each user and relevant antenna performance parameters for modeling analysis, can obtain a more reliable beamforming model of the reconfigurable holographic surface.

[0081] In the present application, in order to facilitate subsequent parameter update using the gradient projection method in the deep unfolding network, the beamforming model m of the reconfigurable holographic surface is further expressed as m = Aa, where a is the amplitude ratio matrix and a = [a1, a2,..., aL T , which includes the amplitude ratios of multiple users, A is the first beamforming matrix of the reconfigurable holographic surface and is the beamforming coefficient from the t-th antenna of the reconfigurable holographic surface to the l-th user.

[0082] In step S102 of some embodiments, the phase shift reception vector of each user refers to the degree of phase adjustment of the received signal of the user. Regarding the determination process of the optimization problem model with the goal of maximizing the total user rate, it may but is not limited to include the following steps S301 to step S303:

[0083] Step S301, according to the interference noise information from the satellite to each user, at least one channel matrix, and the total transmission power and beamforming model of the reconfigurable holographic surface, determine the received signal model of each user as follows:

[0084]

[0085] In the formula, y l is the received signal of the l-th user, H l is the single channel matrix from the satellite to the l-th user and Q is the number of multiple receive antennas configured for a single user, P t is the total transmission power of the reconfigurable holographic surface, x is a transmission coefficient with zero mean and H represents the conjugate transpose symbol, represents the expectation operation symbol, n l is the interference noise information from the satellite to the l-th user, which is circularly symmetric complex Gaussian noise, and the corresponding distribution information is described as σ 2 is the noise power, I Q is the Q-order identity matrix.

[0086] Step S302, according to the received signal model of each user and the phase shift reception vector, determine the total user rate model.

[0087] In this step, first, according to the received signal model of each user and the phase shift reception vector, determine the data recovery model of each user as follows:

[0088]

[0089] Then decompose and transform the data recovery model of each user to obtain the total user rate model as follows:

[0090]

[0091] In the formula, w l ​is the phase shift received vector for the l-th user and R is the total rate of multiple users in the reconfigurable holographic surface assisted satellite communication system.

[0092] Step S303: According to the total rate model of this user and the preset constraint conditions on the amplitude ratio and phase shift received vector of each user, determine the following optimization problem model:

[0093]

[0094] In the formula, {w l} represents a set containing the phase shift received vectors of multiple users, and w l (q) is the phase shift received coefficient corresponding to the q-th receiving antenna configured for the l-th user; in this application, the constraint condition on the amplitude ratio of each user is to ensure that the radiation intensity of each antenna of the reconfigurable holographic surface remains within the range of [0, 1], and the constraint condition on the phase shift received vector of each user is to ensure that the phase shift received vector of this user satisfies the unit modulus constraint.

[0095] Steps S301 to S303 provided in the embodiments of this application, by comprehensively modeling and analyzing using the interference noise information from the satellite to each user, at least one channel matrix, the phase shift received vectors of each user, and the total transmission power and beamforming model of the reconfigurable holographic surface, can obtain a more reasonable optimization problem model, laying a foundation for effectively optimizing the beamforming performance of the reconfigurable holographic surface subsequently.

[0096] In step S103 of some embodiments, first, the depth unfolding technology is used to solve the optimization problem model to obtain the optimal values of the amplitude ratios of each user; then, the optimal values of the amplitude ratios of each user are input into the beamforming model of the reconfigurable holographic surface for solution to obtain the beamforming matrix of the reconfigurable holographic surface.

[0097] In some embodiments, regarding the step of using the depth unfolding technology to solve the optimization problem model to obtain the optimal values of the amplitude ratios of each user, the corresponding implementation process may but is not limited to including the following steps S401 to S403:

[0098] Step S401: According to the optimization problem model, determine the objective function as follows:

[0099]

[0100] In the formula, J is the objective function, D bis a set of channel matrices, which contains multiple different subsets of channel matrices. Each subset of channel matrices contains a single channel matrix from the satellite to each user. The second summation symbol represents the calculation of the total user rate for a single subset of channel matrices, and the first summation symbol represents the summation process for multiple total user rates calculated corresponding to multiple subsets of channel matrices.

[0101] Step S402: Randomly initialize the amplitude ratios and phase shift received vectors of each user, and then input the amplitude ratios and phase shift received vectors of each user after random initialization into a preset deep unfolding network, which is designed based on the iterative principle of the gradient projection method.

[0102] Step S403: According to the objective function and the preset maximum number of training rounds, perform iterative training on the deep unfolding network to obtain the optimal values of the amplitude ratios of each user.

[0103] In this step, perform iterative training on the deep unfolding network according to the objective function until the maximum number of training rounds is reached, and then directly output the latest values of the amplitude ratios of each user obtained after performing the last round of training on the deep unfolding network as the optimal values of the amplitude ratios of each user; in this application, it is preferably set that the maximum number of training rounds is 50.

[0104] Steps S401 to S403 provided in the embodiments of this application can greatly reduce the number of iterative operations of the gradient projection method by using a deep unfolding network integrating the gradient projection method to solve the objective optimization problem, thereby reducing the computational complexity and improving the operation speed.

[0105] In step S403 of some embodiments, each round of training process of the deep unfolding network may, but is not limited to, include the following steps S501 to S503:

[0106] Step S501: Obtain the current values of the amplitude ratios and phase shift received vectors of each user, and obtain the current values of the first gradient step size and the second gradient step size; wherein, the first gradient step size is used to assist in updating the amplitude ratios of each user, and the second gradient step size is used to assist in updating the phase shift received vectors of each user.

[0107] In this step, if the current execution is the first round of training for the deep unfolding network, the current values of the amplitude ratios and phase shift receiving vectors of each user are the randomly initialized values of the amplitude ratios and phase shift receiving vectors of each user obtained through the above step S402, and the current values of the first gradient step size and the second gradient step size are both preset values; if the current execution is other rounds of training for the deep unfolding network, the current values of the amplitude ratios and phase shift receiving vectors of each user are the latest values of the amplitude ratios and phase shift receiving vectors of each user obtained after the previous round of training for the deep unfolding network, and the current values of the first gradient step size and the second gradient step size are the latest values of the first gradient step size and the second gradient step size obtained after the previous round of training for the deep unfolding network.

[0108] Step S502: In the forward propagation stage of the deep unfolding network, use the number of layers of the deep unfolding network as the number of iterations of the gradient projection method, and update the current values of the amplitude ratios and phase shift receiving vectors of each user by using the gradient projection method according to the objective function and the current values of the first gradient step size and the second gradient step size.

[0109] In this step, set the deep unfolding network to include K network layers connected in sequence. In the forward propagation process of the k-th network layer, first obtain the current latest values of the amplitude ratios and phase shift receiving vectors of each user, the current value of the first gradient sub-step size required for updating the amplitude ratio in the k-th network layer included in the first gradient step size, and the current value of the second gradient sub-step size required for updating the phase shift receiving vector in the k-th network layer included in the second gradient step size; then update the current latest values of the amplitude ratios of each user according to the objective function, the current value of the first gradient sub-step size, and the current latest values of the phase shift receiving vectors of each user; and update the current latest values of the phase shift receiving vectors of each user according to the objective function, the current value of the second gradient sub-step size, and the current latest values of the amplitude ratios of each user.

[0110] It should be noted that the current latest values of the amplitude ratios and phase shift receiving vectors of each user obtained in the forward propagation process of the first network layer are the current values of the amplitude ratios and phase shift receiving vectors of each user obtained in the above step S501; the current latest values of the amplitude ratios and phase shift receiving vectors of each user obtained in the forward propagation process of other network layers are the current latest values of the amplitude ratios and phase shift receiving vectors of each user obtained after the forward propagation of the previous connected network layer.

[0111] Specifically, according to the objective function, the current value of the first gradient sub-step size, and the current latest values of the phase shift receiving vectors of each user, the current latest values of the amplitude ratios of each user are updated, which can be achieved by the following formula:

[0112]

[0113]

[0114] In the formula, a0 is the amplitude ratio matrix to be updated, which contains the current latest values of the amplitude ratios of each user to be updated, w l0 is the current latest value of the phase shift receiving vector of the l-th user to be updated, μ1(k) is the current value of the first gradient sub-step size required to be applied when updating the amplitude ratio during the forward propagation process of the k-th network layer, a1 is the first amplitude ratio matrix, which is the gradient update result of a0, a 1l is the first latest value of the amplitude ratio of the l-th user included in the first amplitude ratio matrix a1, a2 is the second amplitude ratio matrix, which is the projection result of a1, a 2l is the second latest value of the amplitude ratio of the l-th user included in the second amplitude ratio matrix a2, a3 is the third amplitude ratio matrix, which is the proportional conversion result of a2, and can also be understood as the final update result of a0, which contains the final update result of the current latest values of the amplitude ratios of each user. In this application, by first projecting and then proportionally converting the first amplitude ratio matrix a1, the third amplitude ratio matrix a3 can satisfy the above-mentioned constraint conditions regarding the amplitude ratios of each user.

[0115] Specifically, according to the objective function, the current value of the second gradient sub-step size, and the current latest values of the amplitude ratios of each user, the current latest values of the phase shift receiving vectors of each user are updated, which can be achieved by the following formula:

[0116]

[0117] w l1 (q) ∈ w l1 , w l2 (q) ∈ w l2

[0118]

[0119] In the formula, w l1 is the first latest value of the phase shift receiving vector of the l-th user, which is the gradient update result of w l0 , w l1(q) is the first updated value of the phase shift reception coefficient corresponding to the q-th reception antenna configured for the l-th user, μ2(k) is the current value of the second gradient sub-step required to apply when updating the phase shift reception vector during the forward propagation process of the k-th network layer, w l2 is the second updated value of the phase shift reception vector for the l-th user, which is the projection result with respect to w l1 and can also be understood as the final update result with respect to w l0 w l2 (q) is the second updated value of the phase shift reception coefficient corresponding to the q-th reception antenna configured for the l-th user. It can be understood that the phase shift reception vector of the l-th user contains multiple phase shift reception coefficients corresponding to multiple reception antennas configured for the l-th user, and the phase shift reception coefficient of each reception antenna represents the degree of phase adjustment of the received signal of that reception antenna. In this application, by projecting the first updated value w l1 of the phase shift reception vector of the l-th user, the second updated value w l2 of the phase shift reception vector of the l-th user can satisfy the constant modulus constraint.

[0120] Step S503, in the backpropagation stage of this deep unfolding network, according to this objective function, update the current values of this first gradient step and this second gradient step, which can be implemented by the following formula:

[0121]

[0122] In the formula, μ1 is the current value of this first gradient step to be updated, which includes the current values of K first gradient sub-steps required to apply when updating the amplitude ratio during the forward propagation process of K network layers, is the update result with respect to μ1, β is a preset gradient update coefficient, μ2 is the current value of this second gradient step to be updated, which includes the current values of K second gradient sub-steps required to apply when updating the phase shift reception vector during the forward propagation process of K network layers, is the update result with respect to μ2. In this application, this first gradient step and this second gradient step are hyperparameters of this deep unfolding network.

[0123] In this application, by using the deep unfolding network to flexibly update all gradient steps required during the iterative process of the gradient projection method synchronously, the convergence speed of the algorithm can be improved, and the deep unfolding network integrating the gradient projection method still uses the model-driven optimization algorithm in practical applications, which can maintain strong interpretability.

[0124] Please refer to Figure 2 , Figure 2FIG. 0 is a schematic diagram of an optional structural composition of a reconfigurable holographic surface beamforming system based on deep unfolding provided by an embodiment of the present application, which can implement the above-mentioned reconfigurable holographic surface beamforming method based on deep unfolding. The reconfigurable holographic surface is used to assist in establishing a communication relationship between a satellite and multiple users. The system may but is not limited to include the following:

[0125] The first determination module 601 is configured to obtain the direction angles from the reconfigurable holographic surface to each user, as well as the antenna performance parameters, feed position vectors, and each antenna position vector of the reconfigurable holographic surface, and determine the beamforming model of the reconfigurable holographic surface in combination with the unknown amplitude ratios of each user; wherein, the amplitude ratio of each user refers to the ratio of the amplitude of the received signal of the user to the amplitude of the transmitted signal of the reconfigurable holographic surface;

[0126] The second determination module 602 is configured to obtain the total transmission power of the reconfigurable holographic surface, the interference noise information from the satellite to each user, and at least one channel matrix, and determine an optimization problem model with the goal of maximizing the total user rate in combination with the beamforming model of the reconfigurable holographic surface and the unknown phase shift reception vectors of each user; wherein, the phase shift reception vector of each user refers to the degree of phase adjustment of the received signal of the user;

[0127] The solving module 603 is configured to solve the optimization problem model by using the deep unfolding technology to determine the beamforming matrix of the reconfigurable holographic surface.

[0128] It can be understood that the content in the above method embodiments is applicable to the system embodiments of the present application. The functions specifically implemented by the system embodiments of the present application are the same as those specifically implemented by the above method embodiments, and the beneficial effects achieved by the system embodiments of the present application are also the same as those achieved by the above method embodiments.

[0129] The embodiment of the present application further provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the above-mentioned reconfigurable holographic surface beamforming method based on deep unfolding. The electronic device may include any intelligent terminal such as a tablet computer or an in-vehicle computer.

[0130] It can be understood that the content in the above method embodiments is applicable to the device embodiments of the present application. The functions specifically implemented by the device embodiments of the present application are the same as those specifically implemented by the above method embodiments, and the beneficial effects achieved by the device embodiments of the present application are also the same as those achieved by the above method embodiments.

[0131] Please refer to Figure 3 , Figure 3 FIG. shows the hardware structure of an electronic device according to another embodiment. The electronic device includes:

[0132] The processor 701 can be implemented in the form of a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0133] The memory 702 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM), etc. The memory 702 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of the present application through software or firmware, the relevant program codes are stored in the memory 702 and are called by the processor 701 to execute the technical solutions provided in the embodiments of the present application;

[0134] The input / output interface 703 is used to implement information input and output;

[0135] The communication interface 704 is used to implement communication interaction between this device and other devices, and can implement communication through wired means (such as USB, network cable, etc.) or through wireless means (such as mobile network, WIFI, Bluetooth, etc.);

[0136] The bus 705 transmits information between various components of the device (such as the processor 701, the memory 702, the input / output interface 703, and the communication interface 704);

[0137] Among them, the processor 701, the memory 702, the input / output interface 703, and the communication interface 704 are communicatively connected to each other inside the device through the bus 705.

[0138] The embodiments of the present application also provide a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned method of reconfigurable holographic surface beamforming based on deep unfolding is implemented.

[0139] It can be understood that the content in the above method embodiments is applicable to the embodiments of this storage medium. The functions specifically implemented by the embodiments of this storage medium are the same as the functions specifically implemented by the above method embodiments, and the beneficial effects achieved by the embodiments of this storage medium are also the same as the beneficial effects achieved by the above method embodiments.

[0140] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory optionally includes a memory remotely disposed relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0141] The embodiments described in the embodiments of the present application are for more clearly illustrating the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art will know that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

[0142] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or combine certain steps, or different steps.

[0143] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0144] Those of ordinary skill in the art can understand that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices can be implemented as software, firmware, hardware, and appropriate combinations thereof.

[0145] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0146] It should be understood that in this application, "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that there can be three relationships. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist at the same time. Among them, A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after. "At least one (item) of the following" or its similar expression refers to any combination of these items, including any combination of single item (item) or plural items (items). For example, at least one (item) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0147] In several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of systems or units can be in electrical, mechanical or other forms.

[0148] The units described above as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0149] In addition, each functional unit in each embodiment of this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0150] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage medium includes: various media that can store programs, such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0151] The preferred embodiments of the embodiments of this application have been described above with reference to the accompanying drawings, and thus do not limit the scope of the rights of the embodiments of this application. Any modifications, equivalent replacements, and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of this application shall be within the scope of the rights of the embodiments of this application.

Claims

1. A reconfigurable holographic surface beamforming method based on depth expansion, characterized in that: The reconfigurable holographic surface is used to assist a satellite in establishing communication relationships with multiple users. The method comprises: Obtaining the direction angle from the reconfigurable holographic surface to each of the users, as well as the antenna performance parameters, feed source position vector and each antenna position vector of the reconfigurable holographic surface, and determining the beamforming model of the reconfigurable holographic surface in combination with the unknown amplitude ratio of each of the users; Obtaining the total transmission power of the reconfigurable holographic surface, interference noise information from the satellite to each of the users, and at least one channel matrix, combining the beamforming model of the reconfigurable holographic surface and the unknown phase shift receiving vectors of each of the users, and determining an optimization problem model with the goal of maximizing the total user rate; The optimization problem model is solved by using a depth unfolding technique to determine a beamforming matrix of the reconfigurable holographic surface; The amplitude ratio of each user represents the ratio of the amplitude of the received signal of the user to the amplitude of the transmission signal of the reconfigurable holographic surface, and the phase shift reception vector of each user represents the phase adjustment degree of the received signal of the user.

2. The reconfigurable holographic surface beamforming method based on depth expansion according to claim 1 is characterized in that: The antenna performance parameters include antenna wavelength, antenna efficiency and antenna attenuation coefficient. The beamforming model of the reconfigurable holographic surface is obtained by: Determine the expected propagation vector of the reconfigurable holographic surface in the direction of each user according to the antenna wavelength and the direction angle from the reconfigurable holographic surface to each user; Determine reference wave propagation vectors of each antenna of the reconfigurable holographic surface according to the antenna wavelength and the feed position vector and each antenna position vector of the reconfigurable holographic surface; Determine the radiation amplitude of each antenna of the reconfigurable holographic surface to each user according to the expected propagation vector of the reconfigurable holographic surface in the direction of each user and the reference wave propagation vector of each antenna of the reconfigurable holographic surface and each antenna position vector; Determine the radiation amplitude model of each antenna of the reconfigurable holographic surface according to the amplitude ratio of each user and the radiation amplitude of each antenna of the reconfigurable holographic surface to each user; The beamforming model of the reconfigurable holographic surface is determined according to the antenna efficiency, the antenna attenuation coefficient, the radiation amplitude model of each antenna of the reconfigurable holographic surface, the reference wave propagation vector of each antenna and the position vector of each antenna.

3. The reconfigurable holographic surface beamforming method based on depth expansion according to claim 2 is characterized in that: Determining the beamforming model of the reconfigurable holographic surface according to the antenna efficiency, the antenna attenuation coefficient, and each antenna radiation amplitude model of the reconfigurable holographic surface, each antenna reference wave propagation vector, and each antenna position vector comprises: Determine, according to each antenna reference wave propagation vector and each antenna position vector of the reconfigurable holographic surface, a phase attenuation coefficient when a feed source of the reconfigurable holographic surface propagates a corresponding reference wave to each antenna; According to the antenna attenuation coefficient and each antenna position vector of the reconfigurable holographic surface, determining the amplitude attenuation coefficient when the feed source of the reconfigurable holographic surface propagates the corresponding reference wave to each antenna; The beamforming model of the reconfigurable holographic surface is determined according to the antenna efficiency, the radiation amplitude model of each antenna of the reconfigurable holographic surface, and the phase attenuation coefficient and amplitude attenuation coefficient when the feed source of the reconfigurable holographic surface propagates the corresponding reference wave to each antenna.

4. The reconfigurable holographic surface beamforming method based on depth expansion according to claim 1 is characterized in that: The optimization problem model is obtained in the following way: Determine a receiving signal model of each of the users according to interference noise information from the satellite to each of the users and at least one channel matrix as well as the total transmission power and beamforming model of the reconfigurable holographic surface; Determining a user total rate model according to a received signal model and a phase shift received vector of each of the users; The optimization problem model is determined according to the user total rate model and preset constraints on the amplitude ratio and phase shift reception vector of each user.

5. The reconfigurable holographic surface beamforming method based on depth expansion according to claim 1 is characterized in that: The adopting the depth expansion technology to solve the optimization problem model to determine the beamforming matrix of the reconfigurable holographic surface includes: The optimization problem model is solved by using a deep expansion technique to obtain an optimal value of the amplitude ratio of each user; The optimal value of the amplitude ratio of each user is input into the beamforming model of the reconfigurable holographic surface for solution to obtain the beamforming matrix of the reconfigurable holographic surface.

6. The reconfigurable holographic surface beamforming method based on depth expansion according to claim 5 is characterized in that: The use of the deep expansion technology to solve the optimization problem model to obtain the optimal value of the amplitude ratio of each user includes: Determine the objective function according to the optimization problem model; Randomly initializing the amplitude ratio and phase shift reception vector of each user, and then inputting the randomly initialized amplitude ratio and phase shift reception vector of each user into a preset deep unfolding network, wherein the deep unfolding network is designed based on the iterative principle of the gradient projection method; According to the objective function and a preset maximum number of training rounds, the deep expansion network is iteratively trained to obtain an optimal value of the amplitude ratio of each of the users.

7. The reconfigurable holographic surface beamforming method based on depth expansion according to claim 6 is characterized in that: Each round of training process for the deep unfolded network includes: Acquire current values ​​of the amplitude ratio and phase shift reception vector of each of the users; Acquire current values ​​of a first gradient step and a second gradient step, wherein the first gradient step is used to assist in updating an amplitude ratio of each of the users, and the second gradient step is used to assist in updating a phase shift reception vector of each of the users; In the forward propagation stage of the deep unfolding network, the number of layers of the deep unfolding network is used as the number of iterations of the gradient projection method, and the current values ​​of the amplitude ratio and the phase shift reception vector of each of the users are updated by the gradient projection method according to the objective function and the current values ​​of the first gradient step size and the second gradient step size; In the back propagation phase of the deep expansion network, current values ​​of the first gradient step size and the second gradient step size are updated according to the objective function.

8. A reconfigurable holographic surface beamforming system based on depth expansion, characterized in that: The reconfigurable holographic surface is used to assist a satellite in establishing communication relationships with multiple users. The system comprises: A first determination module is used to obtain the direction angle from the reconfigurable holographic surface to each of the users, as well as the antenna performance parameters, feed source position vector and each antenna position vector of the reconfigurable holographic surface, and determine the beamforming model of the reconfigurable holographic surface in combination with the unknown amplitude ratio of each of the users; A second determination module is used to obtain the total transmission power of the reconfigurable holographic surface, interference noise information from the satellite to each of the users, and at least one channel matrix, and determine an optimization problem model with the goal of maximizing the total user rate in combination with the beamforming model of the reconfigurable holographic surface and the unknown phase shift receiving vectors of each of the users; A solution module, used for solving the optimization problem model by using a depth expansion technique to determine a beamforming matrix of the reconfigurable holographic surface; The amplitude ratio of each user represents the ratio of the amplitude of the received signal of the user to the amplitude of the transmission signal of the reconfigurable holographic surface, and the phase shift reception vector of each user represents the phase adjustment degree of the received signal of the user.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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