CN115842699A - Intelligent surface-assisted hybrid multiple access method for starry-sky convergence network - Google Patents

Abstract

The invention discloses an intelligent surface assisted hybrid multiple access method for a starry sky convergence network, which comprises the following steps: acquiring statistical channel state information of each link; according to the statistical channel state information, an optimization problem is constructed based on that a satellite communication system adopts a space division multiple access technology to serve multi-user communication, an unmanned aerial vehicle communication system adopts a space division multiple access technology to serve near user communication and simultaneously adopts an intelligent surface and non-orthogonal multiple access technology to serve far user communication; solving an optimization problem by a zero forcing method based on statistical channel information and further combining a Dinkelbach method, taylor expansion and a semi-definite planning method to obtain an optimization variable value; and according to the optimized variable value, realizing intelligent surface-assisted hybrid multiple access facing the starry-sky convergence network. The invention effectively reduces the influence of estimation quantization and feedback delay and reduces the complexity of the algorithm while realizing the high-efficiency transmission and wide-area coverage of the starry-sky fusion network.

Description

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

Technical Field

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

With the rapid development of mobile Internet technology, wireless communication equipment and sensor applications have grown exponentially, with higher requirements for communication connection, data transmission and coverage. Compared with ground wireless communication systems, satellite communication has the advantages of wide coverage, large communication capacity, and no geographical restrictions. It can provide transmission services for users in remote areas and is considered to be an important means to achieve global coverage and access wherever they are needed. However, due to the disadvantages of large transmission delay, high construction and maintenance costs, satellite communication is currently only widely used in large-capacity fixed and mobile wireless service fields. In recent years, with the development of UAV platform technology, UAV communication systems have become one of the core technologies of the 6th generation mobile communication system due to their advantages of good mobility, low cost, easy deployment and control. In view of the fact that future mobile communications need to solve the problem of low-cost access in remote areas and special users, the star-space fusion network formed by integrating satellite communication systems with UAV communication systems can not only integrate the advantages of the two communication systems, but also facilitate the realization of wide-area coverage, ubiquitous connection and access wherever they are needed, which has attracted widespread attention from the current academic and industrial circles.

Multiple access technology has always been a technical problem in the field of mobile communications. Compared with traditional orthogonal multiple access technologies such as frequency division multiple access, time division multiple access, and code division multiple access, non-orthogonal multiple access technology allows multiple users to be superimposed in the same frequency at the same time. Non-orthogonal multiple access technology in the power domain can realize the superposition of multiple users in the power domain, which can significantly increase the number of user access and improve the utilization rate of spectrum resources, becoming a hot research topic in the current mobile communication field. On the other hand, with the development of electronic material technology and wireless communication technology, smart surfaces have become a key candidate technology for the 6th generation mobile communication system. Smart surfaces can achieve real-time regulation of wireless channels/radio propagation environments by intelligently controlling the phase shift of reflectors, thereby effectively improving the communication freedom of the communication environment and increasing the communication coverage area. In addition, smart surfaces also have many advantages such as eliminating co-channel interference, enhancing useful signals, and improving service quality. Compared with commonly used RF technologies, smart surfaces do not require RF and baseband processing circuits, so they can be densely deployed at a lower cost and low energy consumption, thereby providing a strong guarantee for achieving wide-area coverage of star-to-sky fusion networks.

At present, most of the satellite communication system methods assisted by smart surfaces are designed under the condition of known instantaneous channel state information. However, in practical applications, since smart surfaces usually have a large number of reflection units, the channel parameters required to be estimated will also increase proportionally with the increase in the number of reflection units, resulting in system overload. Therefore, methods based on known instantaneous channel state information are not very suitable for practical applications.

The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the invention and should not be regarded as an acknowledgement or any form of suggestion that the information constitutes the prior art already known to ordinary technicians in this field.

Summary of the invention

The purpose of the present invention is to overcome the shortcomings in the prior art and provide an intelligent surface-assisted hybrid multiple access method for a star-space fusion network. Under the condition of known statistical channel state information, the total traversal and rate maximization of the star-space fusion network are taken 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 star-space fusion network, effectively reduce the influence of estimation quantization and feedback delay, and reduce the complexity of the algorithm.

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

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

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;

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 optimization variable value;

According to the optimized variable value, realizing intelligent surface-assisted hybrid multiple access for star-sky fusion network;

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 near and far 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.

Further, the statistical channel state information of the link is represented by the channel autocorrelation matrix of the link, which is as follows:

Wherein, R _s,l represents the channel autocorrelation matrix from the l-th earth station to the satellite, g _s,l represents the channel vector from the l-th earth station to the satellite; R _k represents the channel autocorrelation matrix from the k-th ground near user to the UAV, h _k represents the channel vector from the k-th near user to the UAV; R _R,m represents the channel autocorrelation matrix from the m-th ground far user to the smart surface, h _R,m represents the channel vector from the m-th ground far user to the smart surface; R represents the channel autocorrelation matrix from the smart surface to the UAV, H represents the channel matrix from the smart surface to the UAV; R _R,l represents the channel autocorrelation matrix from the l-th earth station to the smart surface, h _R,l represents the channel vector from the l-th earth station to the smart surface; N represents the number of estimations, (·) ⁽ⁿ⁾ represents the n-th estimation of the channel vector, (·) ^H represents the conjugate transpose operation on the vector, and E(·) represents the mathematical expectation of the vector.

Furthermore, the satellite communication system uses space division multiple access technology to serve multi-user communications, including:

When the satellite receives the signal from the lth earth station and after beamforming, the corresponding output signal-to-interference-noise ratio γ _S,l is:

为第l个地球站的噪声功率，(·)^H为对向量进行共轭转置的操作。Wherein, P _S,l is the transmit power of the l-th earth station, v _l is the receive beamforming weight vector of the satellite to the l-th ground station, g _S,l is the channel vector from the l-th earth station to the satellite, M _S is the number of earth stations, P _S,n is the transmit power of the n-th earth station, g _S,n is the channel vector from the n-th earth station to the satellite,

is the noise power of the lth earth station, and (·) ^H is the operation of conjugate transpose of the vector.

Furthermore, the UAV communication system uses space division multiple access technology to serve near user communications, and uses smart surface 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 after beamforming, the corresponding output signal-to-interference-noise ratio can be expressed as:

h_R,m为第m个地面远用户到智能表面的信道矢量；P_R,i为第i个地面远用户的发射功率；h_R,i为第i个地面远用户到智能表面的信道矢量；P_B,k为地面近用户的发射功率；h_k为第k个地面近用户到无人机的信道矢量；P_S,l为第l个地球站的发射功率；h_S,l为第l个地球站到到智能表面的信道矢量；P_B,i为第i个近用户的发射功率；w_i(i≥1)为无人机对第i个近用户的接收波束成形权矢量；h_i为第i个近用户到无人机的信道矢量；P_R,m为第m个远用户的发射功率；

为第m个远用户的噪声功率；

为第i个近用户的噪声功率；M_S为地球站的数量；K为近用户的数量；M_R为远用户的数量。Among them, γ _R,m is the signal of the mth far user received by the UAV and the corresponding output signal to noise ratio after beamforming; γ _B,i is the signal of the i-th near user received by the UAV and the corresponding output signal to noise ratio after beamforming; P _R,m is the transmit power of the ground far user; w ₀ 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 far user to the smart surface; _PR,i is the transmit power of the ith ground far user; h _R,i is the channel vector from the ith ground far user to the smart surface; _PB,k is the transmit power of the ground near user; _hk is the channel vector from the kth ground near user to the UAV; _PS,l is the transmit power of the lth earth station; _hS,l is the channel vector from the lth earth station to the smart surface; _PB,i is the transmit power of the i-th near user; w _i (i≥1) is the receive 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; _PR,m is the transmit power of the m-th far user;

is the noise power of the mth distant user;

is the noise power of the ith near user; _MS is the number of earth stations; K is the number of near users; _MR is the number of far users.

Furthermore, based on the statistical channel state information, with the goal of traversal and rate maximization 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: 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

表示由地球站发射功率和地面用户发射功率所构成的向量，P_R,max为地面远用户的最大发射功率，P_B,max为地面近用户的最大发射功率，P_S,max为地球站的最大发射功率；v_l为卫星波束成形权矢量；w_i(i≥0)为无人机波束成形权矢量；Φ为智能表面相移矩阵，具体可表示为

P_S,l为地球站发射功率；P_B,k为地面近用户发射功率；P_R,m为地面远用户发射功率。Among them, R _SUM represents the total traversal and rate of the satellite-to-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 receiving signal-to-interference-noise ratio threshold of the UAV to the distant user; γ _B,th represents the receiving signal-to-interference-noise ratio threshold of the UAV to the near user; γ _S,th represents the receiving signal-to-interference-noise ratio threshold of the satellite to the earth station;

represents the vector composed of the earth station transmit power and the ground user transmit power, _PR,max is the maximum transmit power of the ground far user, _PB,max is the maximum transmit power of the ground near user, _PS,max is the maximum transmit power of the earth station; _vl 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 is the ground near user transmission power; P _R,m is the ground far user transmission power.

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

C8: _PR,m ≤ _PR,max ,

C10: rank(Θ)＝1.

表示为地面远用户发射功率所构成的向量；

H_R,m为第m个地面远用户到智能表面的信道矢量h_Rm的对角化，即H_R,m＝diag(h_R,m)，w_ZF,0表示基于迫零的无人机对智能表面的波束成形权矢量；

H_S,l为第l个地球站到到智能表面的信道矢量h_S,l的对角化，即H_S,l＝diag(h_S,l)，H为智能表面到无人机的信道矩阵；Tr(·)表示矩阵的迹。Where θ = θθ ^H represents the Hermitian matrix,

It is represented as the vector composed of the transmission power of the distant ground users;

_HR,m is the diagonalization of the channel vector _hRm from the mth ground remote user to the smart surface, that is, _HR,m = diag(hR _,m ), _wZF,0 represents the beamforming weight vector of the UAV to the smart surface based on zero forcing;

H _S,l is the diagonalization of the channel vector h _S,l from the l-th earth station to the smart surface, that is, 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.

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

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 the first aspect.

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

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

The intelligent surface-assisted hybrid multiple access method for the star-sky fusion network proposed in the present invention can effectively overcome the influence 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

FIG1 is a flow chart of a smart surface assisted hybrid multiple access method for a star-space fusion network;

FIG2 is a schematic diagram of a star fusion network.

DETAILED DESCRIPTION

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 solution of the present invention, and cannot be used to limit the protection scope of the present invention.

Example 1

This embodiment 1 provides a smart surface assisted hybrid multiple access method for a star-sky fusion network, as shown in FIG1 , including the following steps:

Obtain statistical channel status information for each link;

According to the statistical channel state information, the 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;

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 optimization variable value;

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

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 earth stations in the satellite coverage area and near and far 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.

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 the star-space fusion network in which the satellite communication system and the UAV communication system 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, effectively reduce the impact of estimation quantization and feedback delay, and reduce the complexity of the algorithm.

As shown in Figure 2, the satellite communication system and the UAV communication system share spectrum resources to improve spectrum efficiency. In the satellite communication system, the low-orbit earth satellite serves _MS earth stations within the spot beam range, where the communication satellite is equipped with a single reflector antenna with L feed sources, and the earth station is equipped with a corresponding parabolic antenna; in the UAV communication system, the UAV serves K near 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 _MR far users, and non-orthogonal multiple access technology is used to improve the spectrum efficiency of the smart surface auxiliary link. The UAV is equipped with an N _B -element uniform linear array, the smart surface is equipped with a uniform planar array composed of _NR = N _x × N _y reflective units, and the ground near users and far users are equipped with single antennas.

As shown in Figure 1, 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-sky fusion network are used as the criteria to jointly optimize the UAV beamforming weight vector, the smart surface phase shift matrix, and the earth station and ground user transmission power, so as to achieve efficient transmission and wide-area coverage of the space-sky fusion network while meeting the service quality of the earth station and ground users. The detailed steps are as follows:

(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:

Wherein, R _s,l is the channel autocorrelation matrix from the l-th earth station to the satellite, g _s,l is the channel vector from the l-th earth station to the satellite; R _k is the channel autocorrelation matrix from the k-th near user to the UAV, h _k is the channel vector from the k-th near user to the UAV; R _R,m is the channel autocorrelation matrix from the m-th far user to the smart surface, h _R,m is the channel vector from the m-th far 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 l-th earth station to the smart surface, h _R,l is the channel vector from the l-th earth station to the smart surface; N is the number of estimates, (·) ⁽ⁿ⁾ is 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.

(2) In satellite communication systems, low-orbit earth satellites serve multiple earth stations within the coverage of spot beams. 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 performs beamforming, the corresponding output signal-to-interference-to-noise ratio γ _S,l can be expressed as

为第l个地球站的噪声功率，(·)^H为对向量进行共轭转置的操作。Wherein, P _S,l is the transmit power of the l-th earth station, v _l is the receive beamforming weight vector of the satellite to the l-th ground station, g _S,l is the channel vector from the l-th earth station to the satellite, M _S is the number of earth stations, P _S,n is the transmit power of the n-th earth station, g _S,n is the channel vector from the n-th earth station to the satellite,

is the noise power of the lth earth station, and (·) ^H is the operation of conjugate transpose of the vector.

(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-to-noise ratio can be expressed as:

h_R,m为第m个地面远用户到智能表面的信道矢量；P_R,i为第i个地面远用户的发射功率；h_R,i为第i个地面远用户到智能表面的信道矢量；P_B,k为地面近用户的发射功率；h_k为第k个近用户到无人机的信道矢量；P_S,l为第l个地球站的发射功率；h_S,l为第l个地球站到到智能表面的信道矢量；P_B,i为第i个近用户的发射功率；w_i(i≥1)为无人机对第i个近用户的接收波束成形权矢量；h_i为第i个近用户到无人机的信道矢量；P_R,m为第m个远用户的发射功率；

为第m个远用户的噪声功率；

为第i个近用户的噪声功率；M_S为地球站的数量；K为近用户的数量；M_R为远用户的数量。Among them, γ _R,m is the signal of the mth far user received by the UAV and the corresponding output signal to noise ratio after beamforming; γ _B,i is the signal of the i-th near user received by the UAV and the corresponding output signal to noise ratio after beamforming; P _R,m is the transmit power of the ground far user; w ₀ 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 far user to the smart surface; _PR,i is the transmit power of the ith ground far user; h _R,i is the channel vector from the ith ground far user to the smart surface; _PB,k is the transmit power of the ground near user; _hk is the channel vector from the kth near user to the UAV; _PS,l is the transmit power of the lth earth station; _hS,l is the channel vector from the lth earth station to the smart surface; _PB,i is the transmit power of the ith near user; w _i (i≥1) is the receive beamforming weight vector of the UAV to the ith near user; h _i is the channel vector from the ith near user to the UAV; _PR,m is the transmit power of the mth far user;

is the noise power of the mth distant user;

is the noise power of the ith near user; _MS is the number of earth stations; K is the number of near users; _MR is the number of far users.

(4) In the case where the satellite communication system and the UAV communication system share spectrum resources, the total traversal and rate maximization of the space-space fusion network is taken as the goal, and the satellite beamforming weight vector v _l , the UAV beamforming weight vector w _i (i ≥ 0), the smart surface phase shift matrix Φ, the earth station transmit power _PS,l , the ground near user transmit power _PB,k and the ground far user _PR,m are jointly optimized while satisfying the service quality of the earth station and the ground user. The specific optimization problem can be expressed as:

R_S为卫星通信系统的遍历和速率，具体可表示为

γ_R,th为无人机对远用户的接收信干噪比阈值；γ_B,th为无人机对近用户的接收信干噪比阈值；γ_S,th为卫星对地球站的接收信干噪比阈值；

表示第n_R个智能表面单元；

为由地球站发射功率和地面用户发射功率所构成的向量；P_R,max为地面远用户的最大发射功率，P_B,max为地面近用户的最大发射功率，P_S,max为地球站的最大发射功率；P_S,l为地球站发射功率；P_B,k为地面近用户发射功率；P_R,m为地面远用户发射功率。将公式、和代入到优化问题得到：Among them, R _SUM is the total traversal and 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 rate of the satellite communication system, which can be specifically expressed as

γ _R,th is the signal-to-interference-noise ratio threshold of the drone for distant users; γ _B,th is the signal-to-interference-noise ratio threshold of the drone for nearby users; γ _S,th is the signal-to-interference-noise ratio threshold of the satellite for the earth station;

represents the n _Rth smart surface unit;

is the vector composed of the earth station transmission power and the ground user transmission power; _PR,max is the maximum transmission power of the ground far user, _PB,max is the maximum transmission power of the ground near user, _PS,max is the maximum transmission power of the earth station; _PS,l is the earth station transmission power; _PB,k is the ground near user transmission power; _PR,m is the ground far user transmission power. Substituting formulas, and into the optimization problem, we get:

(5) By observing the expression of the optimization problem, it is found that the optimization variables are coupled with each other. First, the 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:

Furthermore, the eigenvalue decomposition of the channel correlation matrix _Rk and _RSn is calculated as follows:

∑_B,k是第k个用户的对角线元素为特征值的对角矩阵，即

标记最大特征值为λ_B,k,1，对应的特征向量为u_B,k,1。U_S,n为由第n个地球站的特征向量组成的酉矩阵，U_S,n＝[u_S,n,1,u_S,n,2,…,u_S,n,L]，∑_S,n是第n个地球站的对角线元素为特征值的对角矩阵，即∑_S,n＝diag(λ_S,n,1,λ_S,n,2,…,λ_S,n,L)，标记最大特征值为λ_S,n,1，对应的特征向量为u_S,n,1。从而，卫星波束成形权矢量和无人机波束成形权矢量可以分别表示为Among them, U _B,k is a unitary matrix composed of the feature vectors of the kth user,

∑ _B,k is a diagonal matrix whose diagonal elements are eigenvalues of the kth user, that is,

The maximum eigenvalue is λ _B,k,1 , and the corresponding eigenvector is u _B,k,1 . _{U S,n} is a unitary matrix composed 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 of the nth earth station are eigenvalues, that is, ∑ _S,n = diag(λ _S,n,1 ,λ _S,n,2 ,…,λ _S,n,L ), the maximum eigenvalue is λ _S,n,1 , and the corresponding eigenvector is u _S,n,1 . Therefore, the satellite beamforming weight vector and the UAV beamforming weight vector can be expressed as

为G_S,l的零空间投影矩阵，G_S,l为除第l个地球站所有地球站信道自相关矩阵最大特征值对应的特征向量集合所构成的矩阵，即

为G_B,i的零空间投影矩阵，G_B,i为除第i个用户所有用户信道自相关矩阵最大特征值对应的特征向量集合所构成的矩阵，即当i＝0时，G_B,i＝[u_B,1,1,u_B,2,1,…,u_B,K,1]；当i＞0时，G_B,i＝[u_B,0,1,u_B,1,1,u_B,2,1,…,u_B,i-1,1,u_B,i+1,1,…,u_B,K,1]。由于地球站之间和近用户之间相互没有干扰，那么地球站和近用户以最大发射功率发送信号，即P_S,l＝P_S,max，P_B,k＝P_B,max。进一步，令

表示为地面远用户发射功率所构成的向量；

H_R,m为第m个地面远用户到智能表面的信道矢量h_R,m的对角化，即H_R,m＝diag(h_R,m)，w_ZF,0表示基于迫零的无人机对智能表面的波束成形权矢量；

H_S,l为第l个地球站到到智能表面的信道矢量h_S,l的对角化，即H_S,l＝diag(h_S,l)，H为智能表面到无人机的信道矩阵；Tr(·)表示矩阵的迹。进而将优化问题简化为：in,

is the null space projection matrix of G _S,l , G _S,l is the matrix consisting 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,

is the null space projection matrix of _GB,i , _GB,i is the matrix composed of the set of eigenvectors corresponding to the maximum eigenvalues of the channel autocorrelation matrices of all users except the i-th user, that is, when i＝0, GB _,i ＝[u _B,1,1 ,u _B,2,1 ,…,u _B,K,1 ]; when i＞0, _GB,i ＝[u _B,0,1 ,u _B,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 between nearby users, the earth stations and nearby users send signals with the maximum transmission power, that is, P _S,l ＝P _S,max , P _B,k ＝P _B,max . Further, let

It is represented as the vector composed of the transmission power of the distant ground users;

_HR,m is the diagonalization of the channel vector hR _,m from the mth ground remote user to the smart surface, that is, _HR,m = diag( _hR,m ), _wZF,0 represents the beamforming weight vector of the UAV to the smart surface based on zero forcing;

H _S,l is the diagonalization of the channel vector h _S,l from the lth earth station to the smart surface, that is, 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:

并利用S-Procedure和泰勒展开方法，可得到：(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:

为对变量

进行一阶泰勒展开。除去约束条件C10，优化问题可以通过半正定规划方法求解智能表面相位矢量θ和地面远用户发射功率P_R,m。当厄密特矩阵Θ满足秩为1的要求，则智能表面相位矢量θ为优化问题(18)的解。否则，采用高斯随机方法以满足厄密特矩阵Θ秩为1的要求，从而求得智能表面相位矢量θ。整个低复杂度算法具体步骤如下：Wherein, μ ^(k-1) is the non-negative parameter of the k-1th iteration.

For variables

Perform a first-order Taylor expansion. Remove the constraint C10, and the optimization problem can be solved by the semi-positive programming method to obtain the smart surface phase vector θ and the ground remote user transmission power P _R,m . When the Hermitian matrix Θ satisfies the requirement of rank 1, the smart surface phase vector θ is the solution of the optimization problem (18). Otherwise, the Gaussian random method is used to meet the requirement of the Hermitian matrix Θ rank 1, thereby obtaining the smart surface phase vector θ. The specific steps of the entire low-complexity algorithm are as follows:

无人机波束成形权矢量

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

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

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

4. Calculate

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

满足时，迭代结束；否则返回步骤3；6. When conditions

If satisfied, the iteration ends; otherwise, return to step 3;

7. Obtain the ground far user transmission power P _R,m and use the Gaussian randomization method to solve and obtain the smart surface phase vector θ.

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

Example 2

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

The storage medium is used to store instructions;

The processor is used to operate according to the instructions to execute the steps of the method according to embodiment 1.

Example 3

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.

Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may 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 may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

The present application is described with reference to the flowchart and/or block diagram of the method, device (system) and computer program product according to the embodiment of the present application. It should be understood that each process and/or box in the flowchart and/or block diagram, and the combination of the process and/or box in the flowchart and/or block diagram can be realized 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 produce a device for realizing the function specified in one process or multiple processes in the flowchart and/or one box or multiple boxes in the block diagram.

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 a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may 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, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

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 principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims (8)

1. An intelligent surface assisted hybrid multiple access method for a starry-sky convergence network is characterized by comprising the following steps:

acquiring statistical channel state information of each link;

according to the statistical channel state information, an optimization problem is constructed based on that a satellite communication system adopts a space division multiple access technology to serve multi-user communication, an unmanned aerial vehicle communication system adopts a space division multiple access technology to serve near user communication and simultaneously adopts an intelligent surface and non-orthogonal multiple access technology to serve far user communication;

solving the optimization problem by a zero forcing method based on statistical channel information and further combining a Dinkelbach method, taylor expansion and a semi-definite planning method to obtain an optimization variable value;

according to the optimized variable value, intelligent surface-assisted hybrid multiple access facing the starry sky convergence network is achieved;

the optimization problem aims at the traversal and rate maximization of a star-space fusion network, and the constraint conditions of the optimization problem are the service quality of earth stations in a satellite coverage area and near-far users in an unmanned aerial vehicle coverage area; the optimization variable values are satellite and unmanned aerial vehicle beam forming weight vectors, intelligent surface phase shift matrixes and earth station and ground user transmitting power.

2. An intelligent surface-assisted hybrid multiple access method for a starry-sky-converged network according to claim 1, wherein the statistical channel state information of the link is represented as a channel autocorrelation matrix of the link, and specifically as follows,

wherein R is _s,l Representing the channel autocorrelation matrix, g, of the l-th earth station to the satellite _s,l A channel vector representing the l-th earth station to satellite; r _k Represents the channel autocorrelation matrix, h, of the kth ground-proximal user to the drone _k Representing the k-th near user to drone channelA vector; r _R,m Representing the channel autocorrelation matrix, h, of the mth terrestrial remote user to the smart surface _R,m Representing the channel vector of the mth terrestrial far user to the smart surface; r represents a channel autocorrelation matrix from the intelligent surface to the unmanned aerial vehicle, and H represents a channel matrix from the intelligent surface to the unmanned aerial vehicle; r _R,l A channel autocorrelation matrix, h, representing the ith earth station to the intelligent surface _R,l A channel vector representing the l-th earth station to the intelligent surface; n represents the number of estimations, (.) ⁽ⁿ⁾ Represents the n-th estimation of the channel vector, (-) ^H Represents the operation of conjugate transposing the vector, and E (·) represents the mathematical expectation for the vector.

3. The intelligent surface-assisted hybrid multiple access method for the star-space converged network as claimed in claim 2, wherein the satellite communication system uses space division multiple access technology to serve multiple user communication, comprising:

when the satellite receives the signal of the first earth station and the beam forming is carried out, the corresponding output signal-to-interference-and-noise ratio gamma _S,l Comprises the following steps:

wherein, P _S,l Is the transmission power of the l-th earth station, v _l Beamforming weight vector, g, for the satellite for reception by the ith ground station _S,l For the channel vector from the ith earth station to the satellite, M _S Number of earth stations, P _S,n Is the transmission power of the nth earth station, g _S,n For the channel vector of the nth earth station to the satellite,

the power of the noise of the first earth station, (. DEG) ^H The operation of conjugate transposing vector is performed.

4. The method as claimed in claim 3, wherein the unmanned aerial vehicle communication system uses space division multiple access technology to serve near user communication, and simultaneously uses intelligent surface and non-orthogonal multiple access technology to serve far user communication, and comprises:

when the unmanned aerial vehicle receives signals of an mth far user and an ith near user, and after beamforming, corresponding output signal-to-interference-and-noise ratios can be respectively expressed as:

wherein, γ _R,m Receiving a signal of an m-th remote user for the unmanned aerial vehicle, and correspondingly outputting a signal to interference plus noise ratio after beamforming; gamma ray _B,i Receiving the signal of the ith near user for the unmanned aerial vehicle, and correspondingly outputting a signal to interference plus noise ratio after the signal is formed by a wave beam; p _R,m The transmit power for the ground far user; w is a ₀ Forming weight vectors for the receive beams of the smart surface for the drone; h is a channel matrix from the intelligent surface to the unmanned aerial vehicle; phi is an intelligent surface phase shift matrix, which can be expressed as

h _R,m Channel vectors from the mth terrestrial far user to the intelligent surface; p _R,i The transmission power of the ith ground far user; h is _R,i Channel vectors from the ith remote terrestrial user to the intelligent surface; p _B,k Transmit power for a near-user on the ground; h is _k Channel vectors from the kth near user to the unmanned aerial vehicle; p _S,l The transmit power for the l-th earth station; h is _S,l Channel vectors for the ith earth station to the smart surface; p _B,i The transmission power of the ith near user; w is a _i (i is more than or equal to 1) forming a weight vector for the receiving beam of the ith near user by the unmanned aerial vehicle; h is _i Channel vector from ith near user to unmanned aerial vehicle；P _R,m The transmit power for the mth remote user;

noise power for the mth far user;

noise power for the ith near user; m _S Is the number of earth stations; k is the number of near users; m _R The number of far users.

5. The method of claim 4, wherein an optimization problem is constructed based on the statistical channel state information, with the objective of star-space convergence network traversal and rate maximization and the constraint of service quality of earth stations in a satellite coverage area and near-far users in an unmanned aerial vehicle coverage area, the method comprises: the optimization problem is represented as:

C4:P _R,m ≤P _R,max ,P _B,k ≤P _B,max ,P _S,l ≤P _S,max

wherein R is _SUM Representing the total traversal and rate of the satellite-ground network; r _B Representing the traversal and rate of the drone communication system; r _S Representing the traversal and rate of the satellite communication system; gamma ray _R,th Representing the receiving signal-to-interference-and-noise ratio threshold value of the unmanned aerial vehicle to the far user; gamma ray _B,th Representing the receiving signal-to-interference-and-noise ratio threshold value of the unmanned aerial vehicle to the near user; gamma ray _S,th Representing a received signal to interference plus noise ratio threshold for the satellite to the earth station;

representing a vector formed by the earth station transmission power and the ground user transmission power, P _R,max Maximum transmission power, P, for terrestrial far-users _B,max Maximum transmission power, P, for terrestrial near-users _S,max Maximum transmit power for the earth station; v. of _l Beamforming weight vectors for the satellites; w is a _i (i is more than or equal to 0) is an unmanned aerial vehicle beam forming weight vector; phi is an intelligent surface phase shift matrix expressed as

P _S,l Transmitting power for the earth station; p _B,k Transmitting power for the ground near-user; p _R,m Transmitting power for the ground remote users.

6. The intelligent surface-assisted hybrid multiple access method for the sky-convergence network as claimed in claim 5, wherein the optimization problem is simplified based on a zero forcing method for statistical channel information, and the simplified optimization problem is as follows:

C8:P _R,m ≤P _R,max ,

C10:rank(Θ)＝1.

wherein Θ = θ ^H A hermit matrix is represented which is,

representing a vector formed by transmitting power of a ground far user;

H _R,m channel vector h for mth terrestrial remote user to smart surface _R,m Diagonalization of (i), i.e. H _R,m ＝diag(h _R,m )，w _ZF,0 Representing beamforming weight vectors for the smart surface based on the zero-forcing drones;

H _S,l channel vector h for the ith earth station to the smart surface _S,l Diagonalization of (i), i.e. H _S,l ＝diag(h _S,l ) H is a channel matrix from the intelligent surface to the unmanned aerial vehicle; tr (-) denotes the trace of the matrix.

7. An intelligent surface-assisted hybrid multiple access device facing a starry sky convergence network is characterized by comprising a processor and a storage medium;

the storage medium is used for storing instructions;

the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of claims 1 to 6.

8. A storage medium having a computer program stored thereon, the computer program, when being executed by a processor, implementing the steps of the method of any one of claims 1 to 6.

Images