An intelligent surface-assisted hybrid multiple access method for star-sky fusion network

Abstract

The present invention discloses an intelligent surface-assisted hybrid multiple access method for a space-space fusion network, comprising the following steps: obtaining statistical channel state information of each link; constructing an optimization problem based on the statistical channel state information, using space division multiple access technology in a satellite communication system to serve multi-user communications, using space division multiple access technology in an unmanned aerial vehicle communication system to serve near-user communications, and simultaneously using intelligent surface and non-orthogonal multiple access technology to serve far-user communications; solving the optimization problem using a zero-forcing method based on the statistical channel information, further combining the Dinkelbach method, Taylor expansion, and semi-definite programming method to obtain optimized variable values; and implementing intelligent surface-assisted hybrid multiple access for the space-space fusion network based on the optimized variable values. While achieving efficient transmission and wide-area coverage of the space-space fusion network, the present invention effectively reduces the impact of estimation quantization and feedback delay, and reduces the complexity of the algorithm.

Description

Technical Field

[0001] The invention relates to an intelligent surface-assisted hybrid multiple access method for a star-sky fusion network, belonging to the technical field of wireless communications. Background Art

[0002] With the rapid development of mobile internet technology, the application of wireless communication devices and sensors has grown exponentially, placing higher demands on communication connectivity, data transmission, and coverage. Compared to terrestrial wireless communication systems, satellite communications offer advantages such as wide coverage, high communication capacity, and unrestricted geographical distribution. They can provide transmission services to users in remote areas and are considered a key means of achieving global coverage and ubiquitous access. However, drawbacks such as high transmission latency and high construction and maintenance costs have limited their widespread application to high-capacity fixed and mobile wireless services. In recent years, with the advancement of unmanned aerial vehicle (UAV) platform technology, UAV communication systems have become one of the core technologies of the sixth generation of mobile communication systems due to their advantages such as high maneuverability, low cost, and ease of deployment and control. Given that future mobile communications must address low-cost access in remote areas and for specialized users, the integration of satellite and UAV communication systems to create a satellite-based converged network not only combines the advantages of both communication systems but also facilitates wide-area coverage, ubiquitous connectivity, and ubiquitous access. This network has attracted widespread attention from both academia and industry.

[0003] Multiple access technology has always been a technical challenge in the mobile communications field. Compared to traditional orthogonal multiple access technologies such as frequency division multiple access, time division multiple access, and code division multiple access, non-orthogonal multiple access allows multiple users to overlap simultaneously on the same frequency. In the power domain, non-orthogonal multiple access can achieve multi-user overlap, significantly increasing the number of users and improving spectrum resource utilization, making it a hot research topic in the mobile communications field. Furthermore, with the advancement of electronic materials and wireless communication technologies, smart surfaces have become a key candidate for 6th generation mobile communication systems. Smart surfaces intelligently control the phase shift of reflectors to achieve real-time control of the wireless channel / radio propagation environment, effectively improving communication freedom and expanding coverage. Furthermore, smart surfaces offer numerous advantages, such as eliminating co-channel interference, enhancing useful signals, and improving service quality. Compared to conventional RF technologies, smart surfaces do not require RF and baseband processing circuits, allowing for dense deployment at a lower cost and with lower energy consumption, providing a strong foundation for achieving wide-area coverage in integrated satellite networks.

[0004] Currently, most approaches to smart surface-assisted satellite communication systems are designed based on the knowledge of instantaneous channel state information. However, in practical applications, because smart surfaces typically have a large number of reflective elements, the required channel parameter estimates increase proportionally with the number of reflective elements, leading to system overload. Therefore, approaches based on known instantaneous channel state information are not well-suited for practical applications.

[0005] The information disclosed in this background section is only intended to enhance understanding of the overall background of the invention and should not be considered as an admission or any form of suggestion that the information constitutes the prior art already known to a person of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide an intelligent surface-assisted hybrid multiple access method for a space-based fusion network. Under the condition of known statistical channel state information, the total traversal and rate maximization of the space-based fusion network are used as criteria to jointly optimize the satellite beamforming weight vector, the UAV beamforming weight vector, the intelligent surface phase shift matrix, and the earth station and ground user transmission power, and establish a corresponding optimization problem, so as to achieve efficient transmission and wide-area coverage of the space-based fusion network while effectively reducing the impact of estimation quantization and feedback delay and reducing the complexity of the algorithm.

[0007] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0008] The present invention discloses an intelligent surface-assisted hybrid multiple access method for a star-sky fusion network, comprising the following steps:

[0009] Obtain statistical channel status information for each link;

[0010] According to the statistical channel state information, an optimization problem is constructed based on the satellite communication system using space division multiple access technology to serve multi-user communication, the unmanned aerial vehicle communication system using space division multiple access technology to serve near user communication, and the smart surface and non-orthogonal multiple access technology to serve far user communication;

[0011] The optimization problem is solved by using a zero-forcing method based on statistical channel information, and further combined with a Dinkelbach method, a Taylor expansion, and a semi-positive definite programming method to obtain optimized variable values;

[0012] According to the optimized variable values, intelligent surface-assisted hybrid multiple access for star-sky fusion network is realized;

[0013] Among them, the goal of the optimization problem is to maximize the traversal and rate of the star-sky fusion network, and the constraints of the optimization problem are the service quality of the earth stations in the satellite coverage area and the long and short users in the drone coverage area; the optimization variable values are the satellite and drone beamforming weight vectors, the smart surface phase shift matrix, and the earth station and ground user transmission power.

[0014] Furthermore, the statistical channel state information of the link is represented by the channel autocorrelation matrix of the link, which is as follows:

[0015]

[0016]

[0017]

[0018]

[0019]

[0020] Among them, R s,l represents the channel autocorrelation matrix from the lth earth station to the satellite, g s,l represents the channel vector from the lth earth station to the satellite; R k represents the channel autocorrelation matrix from the kth ground user to the UAV, h k represents the channel vector from the kth nearest user to the UAV; R R,m represents the channel autocorrelation matrix from the mth ground remote user to the smart surface, h R,m represents the channel vector from the mth ground remote user to the smart surface; R represents the channel autocorrelation matrix from the smart surface to the UAV, and H represents the channel matrix from the smart surface to the UAV; R R,l represents the channel autocorrelation matrix from the lth earth station to the smart surface, h R,l represents the channel vector from the lth earth station to the smart surface; N represents the number of estimations, (·) (n) Indicates the nth estimation of the channel vector, (·) H represents the conjugate transpose operation of a vector, and E(·) represents the mathematical expectation of the vector.

[0021] Furthermore, satellite communication systems use space division multiple access technology to serve multi-user communications, including:

[0022] When the satellite receives the signal from the lth earth station and forms the beam, the corresponding output signal-to-interference-and-noise ratio γ S,l for:

[0023]

[0024] Among them, P S,l is the transmission power of the lth earth station, v l is the satellite's receiving beamforming weight vector for the lth ground station, g S,l is the channel vector from the lth earth station to the satellite, M S is the number of earth stations, P S,n is the transmission power of the nth earth station, g S,n is the channel vector from the nth earth station to the satellite, is the noise power of the lth earth station, (·) H Performs the conjugate transpose operation on a vector.

[0025] Furthermore, the UAV communication system uses space division multiple access technology to serve near-user communications, while using smart surfaces and non-orthogonal multiple access technology to serve far-user communications, including:

[0026] When the UAV receives the signals of the mth far user and the ith near user and performs beamforming, the corresponding output signal-to-interference-and-noise ratio can be expressed as:

[0027]

[0028]

[0029] Among them, γ R,m γ is the signal from the mth far user received by the UAV and the corresponding output signal to interference and noise ratio after beamforming; B,i P is the corresponding output signal-to-interference-and-noise ratio after the UAV receives the signal of the i-th near user and performs beamforming; R,m is the transmission power of the ground remote user; w0 is the receiving beamforming weight vector of the UAV to the smart surface; H is the channel matrix from the smart surface to the UAV; Φ is the phase shift matrix of the smart surface, which can be specifically expressed as h R,m is the channel vector from the mth ground remote user to the smart surface; P R,i is the transmission power of the i-th ground remote user; h R,i is the channel vector from the i-th ground remote user to the smart surface; P B,k h is the transmission power of the user on the ground; k is the channel vector from the kth ground user to the UAV; P S,l is the transmission power of the lth earth station; h S,l is the channel vector from the lth earth station to the smart surface; P B,i is the transmission power of the i-th nearest user; w i (i≥1) is the receiving beamforming weight vector of the UAV to the i-th near user; h iis the channel vector from the i-th near user to the UAV; P R,m is the transmission power of the mth far user; is the noise power of the mth distant user; is the noise power of the i-th near user; M S is the number of earth stations; K is the number of near users; M R is the number of remote users.

[0030] Furthermore, based on the statistical channel state information, with the goal of maximizing the traversal and rate of the star-sky fusion network, and with the service quality of the earth stations in the satellite coverage area and the near and far users in the drone coverage area as constraints, an optimization problem is constructed, including:

[0031]

[0032]

[0033]

[0034]

[0035] C4:P R,m ≤P R,max ,P B,k ≤P B,max ,P S,l ≤P S,max

[0036]

[0037]

[0038] Among them, R SUM represents the total traversal and rate of the satellite-ground network; R B represents the traversal and rate of the UAV communication system; R S represents the traversal and rate of the satellite communication system; γ R,th represents the signal-to-interference-and-noise ratio threshold of the drone’s reception of the distant user; γ B,th represents the signal-to-interference-and-noise ratio threshold of the drone’s reception of the nearby user; γ S,th Indicates the signal-to-interference-and-noise ratio threshold of the satellite to the earth station; represents the vector consisting of the earth station transmission power and the ground user transmission power, P R,max is the maximum transmission power of the ground remote user, P B,max is the maximum transmission power of the ground user, P S,max is the maximum transmitting power of the earth station; v l is the satellite beamforming weight vector; w i(i≥0) is the UAV beamforming weight vector; Φ is the smart surface phase shift matrix, which can be specifically expressed as P S,l is the earth station transmission power; P B,k P is the transmission power of the ground near user; R,m Transmit power for remote users on the ground.

[0039] Furthermore, based on the zero-forcing method of statistical channel information, the optimization problem is simplified. The simplified optimization problem is as follows:

[0040]

[0041]

[0042] C8:P R,m ≤P R,max ,

[0043]

[0044] C10:rank(Θ)=1.

[0045] Where Θ = θθ H represents the Hermitian matrix, It is represented by the vector consisting of the transmission power of the ground remote users; H R,m is the channel vector h from the mth remote ground user to the smart surface Rm The diagonalization of H R,m =diag(h R,m ), w ZF,0 represents the beamforming weight vector of the UAV to the smart surface based on zero-forcing; H S,l is the channel vector h from the lth earth station to the smart surface S,l The diagonalization of H S,l =diag(h S,l ), H is the channel matrix from the smart surface to the UAV; Tr(·) represents the trace of the matrix.

[0046] In a second aspect, the present invention discloses an intelligent surface-assisted hybrid multiple access device for a star-sky fusion network, comprising a processor and a storage medium;

[0047] The storage medium is used to store instructions;

[0048] The processor is configured to operate according to the instructions to execute the steps of the method according to the first aspect.

[0049] In a third aspect, the present invention discloses a storage medium having a computer program stored thereon, which implements the steps of the method described in the first aspect when executed by a processor.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The intelligent surface-assisted hybrid multiple access method for the star-sky fusion network proposed in the present invention can effectively overcome the effects of estimation quantization and feedback delay through a low-complexity algorithm based on statistical channel state information, while achieving wide-area coverage and seamless connection, thereby reducing the load of the central control unit and the complexity of the algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flow chart of an intelligent surface-assisted hybrid multiple access method for a star-space fusion network;

[0053] Figure 2 It is a schematic diagram of the Star Fusion Network. DETAILED DESCRIPTION

[0054] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0055] Example 1

[0056] This embodiment 1 provides an intelligent surface-assisted hybrid multiple access method for a star-sky fusion network. Figure 1 As shown, the following steps are included:

[0057] Obtain statistical channel status information for each link;

[0058] Based on the statistical channel state information, an optimization problem is constructed based on the satellite communication system using space division multiple access technology to serve multi-user communication, the UAV communication system using space division multiple access technology to serve near-user communication, and the smart surface and non-orthogonal multiple access technology to serve far-user communication.

[0059] Based on the zero-forcing method of statistical channel information, and further combined with the Dinkelbach method, Taylor expansion and semi-positive definite programming method, the optimization problem is solved to obtain the optimized variable value;

[0060] According to the optimized variable values, intelligent surface-assisted hybrid multiple access for star-space fusion network is realized;

[0061] Among them, the goal of the optimization problem is to maximize the traversal and rate of the star-sky fusion network. The constraints of the optimization problem are the service quality of the earth stations in the satellite coverage area and the long and short users in the drone coverage area. The optimization variables are the satellite and drone beamforming weight vectors, the smart surface phase shift matrix, and the earth station and ground user transmission power.

[0062] The technical concept of the present invention is to jointly optimize the satellite beamforming weight vector, the UAV beamforming weight vector, the smart surface phase shift matrix, and the earth station and ground user transmission power for a star-space fusion network in which satellite communication systems and UAV communication systems share spectrum resources, under the condition of known statistical channel state information, with the total traversal and rate maximization of the star-space fusion network as the criteria, so as to achieve efficient transmission and wide-area coverage of the star-space fusion network while effectively reducing the impact of estimation quantization and feedback delay, and reducing the complexity of the algorithm.

[0063] like Figure 2 As shown in the figure, the satellite communication system and the UAV communication system share spectrum resources to improve spectrum efficiency. In the satellite communication system, low-orbit earth satellites serve M within the range of spot beams. S The communication satellite is equipped with a single reflector antenna with L feeds, and the earth station is equipped with a corresponding parabolic antenna. In the UAV communication system, the UAV serves K nearby users through space division multiplexing technology. In order to further expand the coverage of the UAV, the smart surface installed on the high-rise building assists the UAV to communicate with M R The UAVs are configured with N B Element uniform linear array, intelligent surface configuration N R =N x ×N y A uniform planar array is composed of reflection units, and single antennas are configured for ground near users and far users.

[0064] like Figure 1 As shown in the figure, this method first obtains the statistical channel state information of each link through multiple channel estimations in the central control unit. Based on the obtained statistical channel state information, the total traversal and rate maximization of the space-space fusion network are used as the criteria to jointly optimize the design of the UAV beamforming weight vector, the smart surface phase shift matrix, and the earth station and ground user transmit power. While meeting the service quality of the earth station and ground users, efficient transmission and wide-area coverage of the space-space fusion network are achieved. The detailed steps are as follows:

[0065] (1) The central control unit of the system obtains the statistical channel state information of each link through multiple channel estimations, and expresses the statistical channel state information of each link as the channel autocorrelation matrix of each link, which can be specifically expressed as:

[0066]

[0067]

[0068]

[0069]

[0070]

[0071] Among them, R s,l is the channel autocorrelation matrix from the lth earth station to the satellite, g s,l is the channel vector from the lth earth station to the satellite; R k is the channel autocorrelation matrix from the kth nearest user to the UAV, h k is the channel vector from the kth nearest user to the UAV; R R,m is the channel autocorrelation matrix from the mth remote user to the smart surface, h R,m is the channel vector from the mth remote user to the smart surface; R is the channel autocorrelation matrix from the smart surface to the UAV, H is the channel matrix from the smart surface to the UAV; R R,l is the channel autocorrelation matrix from the lth earth station to the smart surface, h R,l is the channel vector from the lth earth station to the smart surface; N is the number of estimations, (·) (n) To make the n-th estimation of the channel vector, (·) H is the conjugate transpose operation of the vector, and E(·) is the mathematical expectation of the vector.

[0072] (2) In satellite communication systems, low-orbit earth satellites serve multiple earth stations within the coverage area of a spot beam. To serve multiple earth stations simultaneously, low-orbit earth satellites use space division multiple access technology. When the satellite receives the signal from the lth earth station and forms the beam, the corresponding output signal-to-interference-and-noise ratio γ S,l It can be expressed as

[0073]

[0074] Among them, P S,l is the transmission power of the lth earth station, v l is the satellite's receiving beamforming weight vector for the lth ground station, g S,l is the channel vector from the lth earth station to the satellite, M S is the number of earth stations, P S,n is the transmission power of the nth earth station, g S,n is the channel vector from the nth earth station to the satellite, is the noise power of the lth earth station, (·) H Performs the conjugate transpose operation on a vector.

[0075] (3) In the UAV communication system, the UAV uses space division multiple access technology to serve the ground near users, and by placing smart surfaces in appropriate positions and using non-orthogonal multiple access technology, it serves the ground far users that are blocked from the UAV. When the UAV receives the signals of the mth far user and the ith near user and performs beamforming, the corresponding output signal-to-interference-noise ratio can be expressed as:

[0076]

[0077]

[0078] Among them, γ R,m γ is the signal from the mth far user received by the UAV and the corresponding output signal to interference and noise ratio after beamforming; B,i P is the corresponding output signal-to-interference-and-noise ratio after the UAV receives the signal of the i-th near user and performs beamforming; R,m is the transmission power of the ground remote user; w0 is the receiving beamforming weight vector of the UAV to the smart surface; H is the channel matrix from the smart surface to the UAV; Φ is the phase shift matrix of the smart surface, which can be specifically expressed as h R,m is the channel vector from the mth ground remote user to the smart surface; P R,i is the transmission power of the i-th ground remote user; h R,i is the channel vector from the i-th ground remote user to the smart surface; P B,k h is the transmission power of the user on the ground; k is the channel vector from the kth nearest user to the UAV; P S,l is the transmission power of the lth earth station; h S,l is the channel vector from the lth earth station to the smart surface; P B,i is the transmission power of the i-th nearest user; w i (i≥1) is the receiving beamforming weight vector of the UAV to the i-th near user; h i is the channel vector from the i-th near user to the UAV; P R,m is the transmission power of the mth far user; is the noise power of the mth distant user; is the noise power of the i-th near user; M S is the number of earth stations; K is the number of near users; M R is the number of remote users.

[0079] (4) In the case where the satellite communication system and the UAV communication system share spectrum resources, the satellite beamforming weight vector v is optimized with the goal of maximizing the total traversal and rate of the star-space fusion network while meeting the service quality of the earth station and ground users. l , UAV beamforming weight vector w i (i≥0), the smart surface phase shift matrix Φ and the earth station transmission power P S,l , Ground near user transmission power P B,k and terrestrial remote user P R,m For joint optimization design, the specific optimization problem can be expressed as:

[0080]

[0081] Among them, R SUM is the total traversal rate of the satellite-to-ground network; R B is the traversal and rate of the UAV communication system, which can be specifically expressed as R S is the traversal and rate of the satellite communication system, which can be expressed as γ R,th is the signal-to-interference-and-noise ratio threshold of the drone receiving the distant user; γ B,th is the signal-to-interference-and-noise ratio threshold of the drone receiving the nearby user; γ S,th is the signal-to-interference-and-noise ratio threshold of the satellite to the earth station; Indicates the nth R Smart surface units; is the vector consisting of the earth station transmission power and the ground user transmission power; P R,max is the maximum transmission power of the ground remote user, P B,max is the maximum transmission power of the ground user, P S,max is the maximum transmit power of the earth station; P S,l is the earth station transmission power; P B,k P is the transmission power of the ground near user; R,m is the transmission power of the ground remote user. Substituting formulas , and into the optimization problem, we get:

[0082]

[0083] (5) By observing the optimization problem expression, it is found that the optimization variables are coupled with each other. First, a zero-forcing method based on statistical channel information is used to make the user channels of each link orthogonal to each other to eliminate interference between users. That is, the denominator in the optimization objective meets the following requirements:

[0084]

[0085]

[0086] Furthermore, the channel correlation matrix R k and R S,n Perform eigenvalue decomposition and calculate as follows:

[0087]

[0088]

[0089] Among them, U B,k is a unitary matrix composed of the eigenvectors of the kth user, ∑ B,k is a diagonal matrix whose diagonal elements are eigenvalues of the k-th user, that is, Mark the largest eigenvalue as λ B,k,1 , the corresponding eigenvector is u B,k,1 .U S,n is a unitary matrix consisting of the eigenvectors of the nth earth station, U S,n =[u S,n,1 ,u S,n,2 ,…,u S,n,L ],∑ S,n is a diagonal matrix whose diagonal elements are eigenvalues of the nth earth station, that is, ∑ S,n =diag(λ S,n,1 ,λ S,n,2 ,…,λ S,n,L ), marking the maximum eigenvalue as λ S,n,1 , the corresponding eigenvector is u S,n,1 Therefore, the satellite beamforming weight vector and the UAV beamforming weight vector can be expressed as

[0090]

[0091]

[0092] in, G S,l The null space projection matrix, G S,l is the matrix composed of the set of eigenvectors corresponding to the maximum eigenvalue of the channel autocorrelation matrix of all earth stations except the lth earth station, that is, G B,i The null space projection matrix, G B,i is the matrix composed of the set of eigenvectors corresponding to the maximum eigenvalue of the channel autocorrelation matrix of all users except the i-th user, that is, when i=0, G B,i =[u B,1,1 ,u B,2,1 ,…,u B,K,1 ]; when i>0, G B,i =[u B,0,1 ,uB,1,1 ,u B,2,1 ,…,u B,i-1,1 ,u B,i+1,1 ,…,u B,K,1 Since there is no interference between earth stations and near users, the earth stations and near users send signals at the maximum transmission power, that is, P S,l =P S,max , P B,k =P B,max . Further, let It is represented by the vector consisting of the transmission power of the ground remote users; H R,m is the channel vector h from the mth remote ground user to the smart surface R,m The diagonalization of H R,m =diag(h R,m ), w ZF,0 represents the beamforming weight vector of the UAV to the smart surface based on zero-forcing; H S,l is the channel vector h from the lth earth station to the smart surface S,l The diagonalization of H S,l =diag(h S,l ), H is the channel matrix from the smart surface to the drone; Tr(·) represents the trace of the matrix. The optimization problem is then simplified to:

[0093]

[0094] (6) Since the optimization problem is a fractional problem, we first simplify it using the Dinkelbach method and then introduce the slack variable And using S-Procedure and Taylor expansion method, we can get:

[0095]

[0096] Among them, μ (k-1) is a non-negative parameter for the k-1th iteration. For variables Perform a first-order Taylor expansion. Excluding the constraint C10, the optimization problem can be solved by the semi-positive programming method to find the smart surface phase vector θ and the ground remote user transmission power P. R,m When the Hermitian matrix Θ satisfies the rank requirement of 1, the smart surface phase vector θ is the solution to the optimization problem (18). Otherwise, the Gaussian random method is used to satisfy the rank requirement of the Hermitian matrix Θ, thereby obtaining the smart surface phase vector θ. The specific steps of the entire low-complexity algorithm are as follows:

[0097] 1. According to the optimization problem, the satellite beamforming weight vector is obtained by using the zero-forcing method based on statistical channel information UAV beamforming weight vector

[0098] 2. Initialize the calculation accuracy τ, the number of iterations k = 0, and the Dinkelbach factor μ (0) =0;

[0099] 3. Solve the optimization problem ignoring the rank 1 constraint, and the optimal solution is recorded as

[0100] 4. Calculate

[0101] 5. Update the number of iterations k = k + 1;

[0102] 6. When the conditions If satisfied, the iteration ends; otherwise, return to step 3;

[0103] 7. Obtain the ground remote user transmission power P R,m , using the Gaussian randomization method, the smart surface phase vector θ is obtained.

[0104] (7) The satellite communication system and the UAV communication system complete the design of the hybrid multiple access scheme for the entire system based on the beamforming weight vector, phase shift matrix and user's transmission power provided by the central control unit, ensuring reliable access for various users within the wide area coverage.

[0105] Example 2

[0106] This embodiment 2 provides an intelligent surface-assisted hybrid multiple access device for a star-sky fusion network, including a processor and a storage medium;

[0107] The storage medium is used to store instructions;

[0108] The processor is configured to operate according to the instructions to execute the steps of the method according to embodiment 1.

[0109] Example 3

[0110] This embodiment 3 provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in embodiment 1 are implemented.

[0111] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0112] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0113] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0114] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0115] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An intelligent surface-assisted hybrid multiple access method for a star-space fusion network, characterized in that: The steps include: Obtain statistical channel status information for each link; According to the statistical channel state information, an optimization problem is constructed based on the satellite communication system using space division multiple access technology to serve multi-user communication, the unmanned aerial vehicle communication system using space division multiple access technology to serve near user communication, and the smart surface and non-orthogonal multiple access technology to serve far user communication; The optimization problem is solved by using a zero-forcing method based on statistical channel information, and further combined with a Dinkelbach method, a Taylor expansion, and a semi-positive definite programming method to obtain optimized variable values; According to the optimized variable values, intelligent surface-assisted hybrid multiple access for star-sky fusion network is realized; The goal of the optimization problem is to maximize the traversal and rate of the star-sky fusion network. The constraints of the optimization problem are the service quality of the earth stations in the satellite coverage area and the long and short users in the drone coverage area. The optimization variables are the satellite and drone beamforming weight vectors, the smart surface phase shift matrix, and the earth station and ground user transmission power. The optimization problem is expressed as: C4:P R,m ≤P R,max ,P B,k ≤P B,max ,P S,l ≤P S,max Among them, v l is the satellite's receive beamforming weight vector for the lth ground station; w i is the receiving beamforming weight vector of the UAV to the i-th near user, i ≥ 1; Φ is the smart surface phase shift matrix, expressed as P represents the vector consisting of the earth station transmission power and the ground user transmission power, R SUM represents the total traversal and rate of the satellite-ground network; R B represents the traversal and rate of the UAV communication system; R S represents the traversal and rate of the satellite communication system; γ R,m It represents the output signal-to-interference-and-noise ratio of the UAV after receiving the signal of the mth distant user and performing beamforming; γ R,th represents the signal-to-interference-and-noise ratio threshold of the drone’s reception of the distant user; γ B,i The UAV receives the signal of the i-th near user and the corresponding output signal to interference and noise ratio after beamforming; γ B,th represents the signal-to-interference-and-noise ratio threshold of the drone’s reception of the nearby user; γ S,l It represents the output signal-to-interference-and-noise ratio when the satellite receives the signal from the lth earth station and performs beamforming; γ S,th P represents the threshold of the signal-to-interference-and-noise ratio of the satellite to the earth station; R,m is the transmission power of the mth far user; P R,max is the maximum transmission power of the ground remote user; P B,k is the transmission power of the user on the ground; P B,max is the maximum transmission power of the ground user; P S,l is the transmission power of the lth earth station; P S,max is the maximum transmit power of the earth station; M S represents the number of earth stations; K represents the number of near users; N R Indicates the number of reflective units configured on the drone's surface; Solving the optimization problem to obtain optimized variable values includes the following steps: Step 1: According to the optimization problem, the satellite beamforming weight vector is obtained by using the zero-forcing method based on statistical channel information. UAV beamforming weight vector M R is the number of remote users; Step 2: Initialize the calculation accuracy τ, the number of iterations k = 0, and the Dinkelbach factor μ (0) =0; Step 3: Solve the optimization problem ignoring the rank 1 constraint, and the optimal solution is recorded as represents the transmission power of the ground far user at the kth iteration; Θ (k) represents the Hermitian matrix of the kth iteration; Indicates the variable of the kth iteration The first-order Taylor expansion of ; Step 4: Calculate the Dinkelbach factor μ of the kth iteration (k) , the expression is as follows: Where, H R,m is the channel vector h from the mth remote ground user to the smart surface R,m The diagonalization of H R,m =diag(h R,m );w ZF,0 represents the beamforming weight vector of the UAV to the smart surface based on zero-forcing; H S,l is the channel vector h from the lth earth station to the smart surface S,l The diagonalization of H S,l =diag(h S,l ), H is the channel matrix from the smart surface to the UAV; Tr(·) represents the trace of the matrix; σ 2 represents the noise power; Step 5: Update the number of iterations k = k + 1; Step 6: When the following conditions are met, the iteration ends. The expression is as follows: Where N x Indicates the row reflection unit configured on the surface of the drone; N y Represents the column reflective units configured on the surface of the drone; represents the vector consisting of the earth station transmit power and the terrestrial user transmit power at the kth iteration; represents the vector consisting of the earth station transmit power and the terrestrial user transmit power at the k-1th iteration; Θ (k-1) represents the Hermitian matrix of the k-1th iteration; Represents the variable at the k-1th iteration The first-order Taylor expansion of ; τ represents the accuracy; Otherwise return to step 3; Step 7: Obtain the ground remote user transmission power P R,m , using the Gaussian randomization method, the smart surface phase vector θ is solved and the optimized variable value is obtained.

2. The intelligent surface-assisted hybrid multiple access method for star-sky fusion network according to claim 1 is characterized in that: The statistical channel state information of the link is represented by the channel autocorrelation matrix of the link, which is as follows: Among them, R s,l represents the channel autocorrelation matrix from the lth earth station to the satellite, g s,l represents the channel vector from the lth earth station to the satellite; R k represents the channel autocorrelation matrix from the kth ground user to the UAV, h k represents the channel vector from the kth nearest user to the UAV; R R,m represents the channel autocorrelation matrix from the mth ground remote user to the smart surface, h R,m represents the channel vector from the mth ground remote user to the smart surface; R represents the channel autocorrelation matrix from the smart surface to the UAV, and H represents the channel matrix from the smart surface to the UAV; R R,l represents the channel autocorrelation matrix from the lth earth station to the smart surface, h R,l represents the channel vector from the lth earth station to the smart surface; N represents the number of estimations, (·) (n) Indicates the nth estimation of the channel vector, (·) H represents the conjugate transpose operation of a vector, and E(·) represents the mathematical expectation of the vector.

3. The intelligent surface-assisted hybrid multiple access method for star-sky fusion network according to claim 2 is characterized in that: Satellite communication systems use space division multiple access technology to serve multi-user communications, including: When the satellite receives the signal from the lth earth station and forms the beam, the corresponding output signal-to-interference-and-noise ratio γ S,l for: Among them, P S,l is the transmission power of the lth earth station, v l is the satellite's receiving beamforming weight vector for the lth ground station, g S,l is the channel vector from the lth earth station to the satellite, M S is the number of earth stations, P S,n is the transmission power of the nth earth station, g S,n is the channel vector from the nth earth station to the satellite, is the noise power of the lth earth station, (·) H Performs the conjugate transpose operation on a vector.

4. The intelligent surface-assisted hybrid multiple access method for star-sky fusion network according to claim 3 is characterized in that: The UAV communication system uses space division multiple access technology to serve near-user communications, while using smart surfaces and non-orthogonal multiple access technology to serve far-user communications, including: When the UAV receives the signals of the mth far user and the ith near user and performs beamforming, the corresponding output signal-to-interference-and-noise ratio can be expressed as: Among them, γ R,m γ is the signal from the mth far user received by the UAV and the corresponding output signal to interference and noise ratio after beamforming; B,i P is the corresponding output signal-to-interference-and-noise ratio after the UAV receives the signal of the i-th near user and performs beamforming; R,m is the transmission power of the ground remote user; w0 is the receiving beamforming weight vector of the UAV to the smart surface; H is the channel matrix from the smart surface to the UAV; Φ is the phase shift matrix of the smart surface, which can be specifically expressed as h R,m is the channel vector from the mth ground remote user to the smart surface; P R,i is the transmission power of the i-th ground remote user; h R,i is the channel vector from the i-th ground remote user to the smart surface; P B,k h is the transmission power of the user on the ground; k is the channel vector from the kth nearest user to the UAV; P S,l is the transmission power of the lth earth station; h S,l is the channel vector from the lth earth station to the smart surface; P B,i is the transmission power of the i-th nearest user; w i h is the receiving beamforming weight vector of the UAV to the i-th near user, i ≥ 1; i is the channel vector from the i-th near user to the UAV; P R,m is the transmission power of the mth far user; is the noise power of the mth distant user; is the noise power of the i-th near user; M S is the number of earth stations; K is the number of near users; M R is the number of remote users.

5. The intelligent surface-assisted hybrid multiple access method for star-sky fusion network according to claim 1 is characterized in that: Based on the zero-forcing method of statistical channel information, the optimization problem is simplified. The simplified optimization problem is as follows: C8:P R,m ≤P R,max , C10:rank(Θ)=1. Where Θ = θθ H represents the Hermitian matrix, It is represented by the vector consisting of the transmission power of the ground remote users; H R,m is the channel vector h from the mth remote ground user to the smart surface R,m The diagonalization of H R,m =diag(h R,m ), w ZF,0 represents the beamforming weight vector of the UAV to the smart surface based on zero-forcing; H S,l is the channel vector h from the lth earth station to the smart surface S,l The diagonalization of H S,l =diag(h S,l ), H is the channel matrix from the smart surface to the UAV; Tr(·) represents the trace of the matrix.

6. An intelligent surface-assisted hybrid multiple access device for star-space fusion network, characterized in that: including processors and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 5.

7. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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