Optimization method of non-orthogonal multiple access communication system based on movable antenna

Abstract

An embodiment of the present application provides a method for optimizing a non-orthogonal multiple access communication system based on a movable antenna, which is applied to a computer device. The method creates a channel model of the movable antenna communication system, creates a decoding strategy, calculates the minimum reachable rate from the base station to multiple user terminals through the decoding strategy, creates an optimization model for the minimum reachable rate, and splits the optimization problem of the minimum reachable rate into multiple sub-problems. The precoding matrix of the base station, the decoding indicator matrix, and the position vector of the movable antenna are optimized respectively. In an inner loop, the precoding matrix and the decoding indicator matrix of the base station are optimized through an alternating optimization algorithm. In an outer loop, the position vector of the movable antenna is optimized through an improved Hippo algorithm. The cyclic optimization of the sub-problems is realized through the inner and outer double loops. The optimized decoding indicator matrix, the precoding matrix of the base station, and the position vector of the movable antenna are obtained, the performance of the movable antenna communication system is optimized, and the minimum reachable rate between users is improved.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method for optimizing a non-orthogonal multiple access communication system based on a movable antenna. Background Art

[0002] The sixth generation of wireless communication networks (6G) features high data rates, high reliability, and low latency. To improve communication performance, multiple-input multiple-output (MIMO) systems are being developed to meet the requirements of 6G networks. However, traditional MIMO systems typically use fixed antenna arrays that cannot be repositioned to fully utilize spatial degrees of freedom and spatial multiplexing gain.

[0003] In the existing technology, mechanical drive components such as stepper motors or slides are used on movable antennas to achieve flexible adjustment of the position and direction of the movable antenna in three-dimensional space without being restricted by position and direction, thereby improving the benefits of spatial diversity, multiplexing and beamforming gain.

[0004] However, existing technical solutions mainly use Space Division Multiple Access (SDMA), which has low performance in multi-user communication, and traditional convex optimization methods, gradient descent or gradient ascent methods have local optimal value problems, resulting in a decrease in the minimum reachability between users. Summary of the Invention

[0005] The embodiments of the present application provide a method for optimizing a non-orthogonal multiple access communication system based on a movable antenna, so as to solve the problem of performance degradation of the movable antenna existing in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a method for optimizing a non-orthogonal multiple access communication system based on a movable antenna, which is applied to a computer device, including:

[0007] Creating a channel model of the movable antenna communication system, wherein the channel model includes a base station end and multiple user ends, and the user ends include a movable antenna and a user terminal;

[0008] Obtaining a position vector of a movable antenna and a precoding matrix of a base station in the channel model;

[0009] Creating a decoding strategy, wherein the decoding strategy includes a decoding indicator matrix;

[0010] Calculating the minimum reachability rate from the base station to the multiple user terminals according to the decoding strategy;

[0011] Creating an optimization model for the minimum reachability rate, wherein the optimization model includes an optimization problem for the minimum reachability rate;

[0012] Splitting the optimization problem of the minimum achievable rate into multiple sub-problems, wherein the multiple sub-problems include optimizing the precoding matrix of the base station, optimizing the decoding indicator matrix, and optimizing the position vector of the movable antenna;

[0013] In an inner loop, the precoding matrix and the decoding indicator matrix of the base station are optimized by an alternating optimization algorithm to obtain an optimized decoding indicator matrix, an optimized precoding matrix and a corresponding minimum achievable rate;

[0014] In an outer loop, according to the corresponding minimum reachability rate, the position vector of the movable antenna is optimized by using an improved Hippo algorithm to obtain an optimized position vector of the movable antenna;

[0015] If the optimized position vector of the movable antenna, the optimized decoding indicator matrix and the optimized precoding matrix meet the threshold, the movable antenna communication system is optimized according to the optimized position vector of the movable antenna, the optimized decoding indicator matrix and the optimized precoding matrix.

[0016] In one possible implementation, the precoding matrix and the decoding indicator matrix of the base station end are optimized by an alternating optimization algorithm in the inner loop to obtain an optimized decoding indicator matrix, an optimized precoding matrix and a corresponding minimum reachable rate, including: converting the subproblem of optimizing the precoding matrix of the base station end into a convex problem by introducing auxiliary variables, and solving the convex problem according to a solving tool to obtain a preliminary optimized precoding matrix; optimizing the decoding indicator matrix by an improved greedy algorithm to obtain a preliminary optimized decoding indicator matrix; updating the preliminary optimized decoding indicator matrix and the preliminary optimized precoding matrix by an alternating optimization algorithm to obtain an updated decoding indicator matrix and a precoding matrix; if the updated decoding indicator matrix and the precoding matrix meet the set reachable rate threshold, marking the updated decoding indicator matrix and the precoding matrix as the optimized decoding indicator matrix and the optimized precoding matrix; and calculating and generating the corresponding minimum reachable rate based on the optimized decoding indicator matrix and the optimized precoding matrix.

[0017] In one possible implementation, optimizing the decoding indicator matrix by an improved greedy algorithm to obtain a preliminary optimized decoding indicator matrix includes: initializing an alternative decoding indicator matrix and an alternative minimum reachable rate to obtain an initialized alternative decoding indicator matrix and an initialized alternative minimum reachable rate; iteratively updating the initialized alternative decoding indicator matrix to obtain an iterated alternative decoding indicator matrix, and calculating an alternative minimum reachable rate corresponding to the iterated alternative decoding indicator matrix; if the corresponding alternative minimum reachable rate is greater than the initialized alternative minimum reachable rate, updating the initialized alternative decoding matrix; updating the initialized decoding indicator matrix according to a set number of iterations to obtain multiple updated alternative decoding matrices, and calculating the minimum reachable rates of the multiple updated alternative decoding matrices; comparing and obtaining the largest alternative minimum reachable rate among the minimum reachable rates of the multiple updated alternative decoding matrices; obtaining a decoding indicator matrix corresponding to the largest alternative minimum reachable rate, and marking the decoding indicator matrix corresponding to the largest alternative minimum reachable rate as the optimized decoding indicator matrix.

[0018] In a possible implementation, the outer loop optimizes the position vector of the movable antenna by using an improved Hippo algorithm according to the corresponding minimum reachability rate to obtain the optimized position vector of the movable antenna, including: calculating the fitness of the position vector of the movable antenna by using an improved Hippo algorithm to obtain the fitness values ​​of the position vectors of multiple movable antennas; dividing the position vectors of the movable antenna into strong hippos and weak hippos according to the fitness values ​​of the position vectors of the multiple movable antennas; calculating the alternative positions and corresponding fitness values ​​of the strong hippo, and updating the position of the strong hippo by an alternating optimization algorithm according to the corresponding fitness value of the strong hippo to obtain the strong hippo. The invention relates to a method for updating the position of a hippopotamus; generating a position updating strategy for a predator; calculating the fitness value corresponding to the weak hippopotamus according to the position updating strategy of the predator, and updating the position of the weak hippopotamus through an alternating optimization algorithm according to the fitness value corresponding to the weak hippopotamus, thereby obtaining the position updating strategy of the weak hippopotamus; updating the local upper bound and the local lower bound of the local search, thereby obtaining local search range information; updating the positions of the strong hippopotamus and the weak hippopotamus according to the local search range information and the set number of iterations, thereby obtaining position vectors of multiple groups of movable antennas; calculating the precoding matrix and the decoding indicator matrix of the position vectors of the multiple groups of movable antennas, thereby obtaining the optimized position vectors of the movable antennas.

[0019] In a possible implementation, the channel model of the movable antenna communication system is:

[0020]

[0021] Where u krepresents the local position coordinates of the movable antenna equipped with user terminal k; represents the conjugate transpose of the received field response vector between the base station and user terminal k; ∑ k represents the path response matrix; G k Represents the field response matrix at the base station.

[0022] In a second aspect, an embodiment of the present application provides a non-orthogonal multiple access communication system optimization device based on a movable antenna, which is applied to a computer device, including:

[0023] A first creation module is configured to create a channel model of the movable antenna communication system, wherein the channel model includes a base station end and multiple user ends, and the user ends include a movable antenna and a user terminal;

[0024] An acquisition module, configured to acquire a position vector of a movable antenna and a precoding matrix of a base station in the channel model;

[0025] A second creation module is configured to create a decoding strategy, wherein the decoding strategy includes a decoding indication matrix;

[0026] A calculation module, configured to calculate a minimum reachability rate from a base station end to the plurality of user ends according to the decoding strategy;

[0027] A third creation module is configured to create an optimization model for the minimum reachable rate, wherein the optimization model includes an optimization problem for the minimum reachable rate;

[0028] A splitting module is used to split the optimization problem of the minimum reachable rate into multiple sub-problems, wherein the multiple sub-problems include optimizing the precoding matrix of the base station end, optimizing the decoding indicator matrix, and optimizing the position vector of the movable antenna;

[0029] A first optimization module is configured to optimize the precoding matrix and the decoding indicator matrix of the base station end by an alternating optimization algorithm in an inner loop to obtain an optimized decoding indicator matrix, an optimized precoding matrix, and a corresponding minimum achievable rate;

[0030] A second optimization module is configured to optimize the position vector of the movable antenna by using an improved Hippo algorithm according to the corresponding minimum reachability rate in an outer loop to obtain an optimized position vector of the movable antenna;

[0031] The third optimization module is used to optimize the movable antenna communication system according to the optimized position vector of the movable antenna, the optimized decoding indicator matrix and the optimized precoding matrix if the optimized position vector of the movable antenna, the optimized decoding indicator matrix and the optimized precoding matrix meet the threshold.

[0032] In one possible embodiment, the first optimization module includes: a conversion unit, which is used to convert the subproblem of optimizing the precoding matrix of the base station end into a convex problem by introducing auxiliary variables, and solve the convex problem according to a solving tool to obtain a pre-optimized precoding matrix; an optimization unit, which is used to optimize the decoding indicator matrix by an improved greedy algorithm to obtain a pre-optimized decoding indicator matrix; an updating unit, which is used to update the pre-optimized decoding indicator matrix and the pre-optimized precoding matrix by an alternating optimization algorithm to obtain an updated decoding indicator matrix and a precoding matrix; a marking unit, which is used to mark the updated decoding indicator matrix and the precoding matrix as an optimized decoding indicator matrix and an optimized precoding matrix if the updated decoding indicator matrix and the precoding matrix meet a set reachability threshold; and a calculation unit, which is used to calculate and generate the corresponding minimum reachability based on the optimized decoding indicator matrix and the optimized precoding matrix.

[0033] In a third aspect, an embodiment of the present application provides a non-orthogonal multiple access communication system optimization device based on a movable antenna, comprising:

[0034] at least one processor and memory;

[0035] The memory stores computer-executable instructions;

[0036] The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor executes the above first aspect and / or various possible implementations of the first aspect.

[0037] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementation methods of the first aspect.

[0038] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.

[0039] The embodiment of the present application provides an optimization method for a non-orthogonal multiple access communication system based on a movable antenna. By creating a channel model of the movable antenna communication system, a decoding strategy is created. The minimum reachable rate from the base station end to multiple user ends is calculated through the decoding strategy, and an optimization model of the minimum reachable rate is created. The optimization problem of the minimum reachable rate is divided into multiple sub-problems, and the precoding matrix of the base station end, the decoding indicator matrix and the position vector of the movable antenna are optimized respectively. In the inner loop, the precoding matrix and the decoding indicator matrix of the base station end are optimized by an alternating optimization algorithm. In the outer loop, the position vector of the movable antenna is optimized by an improved Hippo algorithm. The cyclic optimization of the sub-problems is realized through the inner and outer double loops, and the optimized decoding indicator matrix, the precoding matrix of the base station end and the position vector of the movable antenna are obtained. The performance of the movable antenna communication system is optimized, and the minimum reachable rate between users is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0041] Figure 1 A schematic diagram of the system structure of a computer device provided in an embodiment of the present application;

[0042] Figure 2 A flowchart of a method for optimizing a non-orthogonal multiple access communication system based on movable antennas provided in this application;

[0043] Figure 3 A schematic diagram of a movable antenna communication system scenario provided in an embodiment of the present application;

[0044] Figure 4 A schematic diagram of a double-loop algorithm flow chart provided in an embodiment of the present application;

[0045] Figure 5 A schematic diagram of the structure of the non-orthogonal multiple access communication system optimization device based on movable antennas provided in this application;

[0046] Figure 6 This is a structural diagram of the non-orthogonal multiple access communication system optimization device based on movable antennas provided in this application.

[0047] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0048] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0049] The sixth generation of wireless communication networks (6G) are characterized by high data rates, high reliability, and low latency. In order to improve communication performance, a Multiple-Input Multiple-Output (MIMO) system is used to meet the requirements of the 6G network. However, traditional MIMO systems usually use fixed-position antenna arrays, which cannot change their positions to fully utilize spatial degrees of freedom and spatial multiplexing gain. In the prior art, mechanical drive components such as stepper motors or slides are used on movable antennas to enable the movable antennas to flexibly adjust their positions and directions in three-dimensional space without being restricted by position and direction, thereby improving the benefits of spatial diversity, multiplexing, and beamforming gain. However, the existing technical solutions mainly use Space Division Multiple Access (SDMA), which has low performance in multi-user communication, and traditional convex optimization methods, gradient descent, or gradient ascent methods have local optimal value problems, resulting in a decrease in the minimum reachability between users.

[0050] In order to solve the above technical problems, the embodiments of the present application propose the following technical concepts: the inventors consider creating a channel model for a mobile antenna communication system, create a decoding strategy, calculate the minimum reachable rate from the base station to multiple user terminals through the decoding strategy, and create an optimization model for the minimum reachable rate. Considering splitting the optimization problem of the minimum reachable rate into multiple sub-problems, respectively optimizing the precoding matrix of the base station, optimizing the decoding indicator matrix, and optimizing the position vector of the mobile antenna, in the inner loop, optimizing the precoding matrix and decoding indicator matrix of the base station through an alternating optimization algorithm, and optimizing the position vector of the mobile antenna through an improved Hippo algorithm in the outer loop, realizing cyclic optimization of the sub-problems through the inner and outer double loops, obtaining the optimized decoding indicator matrix, the precoding matrix of the base station, and the position vector of the mobile antenna, and optimizing the performance of the mobile antenna communication system, thereby improving the minimum reachable rate between users. The following detailed embodiments are used for detailed explanation.

[0051] Figure 1 This is a schematic diagram of the system structure of the computer device provided in the embodiment of the present application. Figure 1 As shown, the computer device includes: a receiving device 101, a processor 102 and a display device 103.

[0052] It is understood that the structure illustrated in the embodiments of this application does not constitute a specific limitation on the object identification method. In other feasible implementations of this application, the above architecture may include more or fewer components than shown, or combine or split certain components, or arrange the components differently. The specific configuration can be determined based on the actual application scenario and is not limited here. Figure 1 The components shown can be implemented in hardware, software, or a combination of software and hardware.

[0053] In a specific implementation process, the receiving device 101 may be an input / output interface or a communication interface, and may obtain the position vector of the movable antenna in the channel model and the precoding matrix of the base station.

[0054] The processor 102 may create a channel model of the movable antenna communication system and optimize the performance of the movable antenna communication system.

[0055] The display device 103 can be used to display the channel model of the above-mentioned movable antenna communication system.

[0056] The display device may also be a touch screen display, which is used to receive user instructions while displaying the above-mentioned content to achieve operational interaction with the user.

[0057] It should be understood that the above-mentioned processor can be implemented by the processor reading instructions in the memory and executing the instructions, or it can be implemented by a chip circuit.

[0058] In addition, the network architecture and business scenarios described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Ordinary technicians in this field can know that with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0059] Figure 2 This is a flow chart of the optimization method of the non-orthogonal multiple access communication system based on movable antennas provided by this application. The execution subject of this embodiment can be a computer device, and this embodiment is not particularly limited here. Figure 2 As shown, the method includes:

[0060] S201: Create a channel model for a movable antenna communication system, wherein the channel model includes a base station end and multiple user ends, and the user ends include a movable antenna and a user terminal.

[0061] In this embodiment, the channel model of the movable antenna communication system is a channel model of a downlink NOMA (Non-Orthogonal Multiple Access) multi-user communication system.

[0062] In one embodiment of the present application, the channel model of the movable antenna communication system is:

[0063]

[0064] Where u k represents the local position coordinates of the movable antenna equipped with user terminal k; represents the conjugate transpose of the received field response vector between the base station and user terminal k; ∑ k represents the path response matrix; G k Represents the field response matrix at the base station.

[0065] Figure 3 A schematic diagram of a movable antenna communication system scenario provided in an embodiment of the present application.

[0066] like Figure 3 As shown, the base station is equipped with a fixed-position uniform planar array with the following dimensions:

[0067] N=N1×N2

[0068] Where N1 represents the number of antennas in the horizontal direction, and N2 represents the number of antennas in the vertical direction. In the figure, K users are equipped with a single movable antenna. The movable antenna equipped by the user can be in a three-dimensional local space C k Freely move within the movable area, origin O k The local position coordinates are expressed as:

[0069] u k =[x k ,y k ,z k ] T ∈C k ,1≤k≤K

[0070] The three-dimensional movable area is assumed to be a cube with dimensions:

[0071]

[0072] In this embodiment, the local position coordinates of the nth fixed position antenna on the uniform planar array are expressed as v n =[X n ,Y n ,Z n ] T ,1≤n≤N.

[0073] In this embodiment, the channel model of the movable antenna is composed of a combination of a field response model and a path response model.

[0074] In this embodiment, the channel model from the base station to user k is initially expressed as: k (u k )∈C N×1 , the received signals of all users are expressed as:

[0075]

[0076] Where, represents the channel matrix from the base station to all users, Represents the position vector of the movable antenna, which includes the antenna position coordinates of all users, W=[w1,w2,…,w K ]∈C N×K represents the digital precoding matrix at the base station, w k represents the precoding vector of user k, s=[s1,s2,…,s K ] T ∈C K×1 Represents the transmitted signal after power normalization, that is, E[ss H ]=I k , where E represents the expectation, I k Represents the k-dimensional identity matrix, n=[n1,n2,…,n K ] T ~CN(0,σ 2 ,I k ) represents the zero-mean additive white Gaussian noise at the user, σ 2 represents the average power, and CN() represents the complex Gaussian distribution operation.

[0077] In summary, the received signal of user k is expressed as:

[0078]

[0079] In this embodiment, there is a total of Stripe emission diameter and receiving paths, where 1≤k≤K. The arrival angle, azimuth and elevation angles of the jth receiving path from the base station to user k are expressed as and For the i-th transmission path from the base station to user k, the transmission angle azimuth and elevation angle are expressed as and In order to express the spatial relationship more concisely, the virtual arrival angle and virtual launch angle are introduced. The virtual arrival angle is expressed as:

[0080]

[0081] The virtual launch angle is expressed as:

[0082]

[0083] In addition, on the jth receiving path of user k, the position of the user's movable antenna is aligned with the local coordinate system reference point, that is, the origin O k The signal propagation distance difference between On the i-th transmission path of user k, the signal propagation distance difference between the position of the n-th fixed position antenna on the base station planar array and the origin of the local coordinate system is Specifically, it can be expressed as:

[0084]

[0085] Therefore, the transmit and receive field response vectors between the base station and user k are expressed as:

[0086]

[0087] Wherein, 1≤k≤K, 1≤n≤N, and λ represents the carrier wavelength.

[0088] In summary, the channel vector between the base station and user k, i.e., the channel model of the mobile antenna communication system, is expressed as:

[0089]

[0090] represents the path response matrix, [∑ k ] j,i ∑ k The element in the jth row and i-th column represents the channel complex coefficient between the i-th transmission path and the j-th receiving path of user k.

[0091] is the field response matrix at the base station.

[0092] S202: Obtain the position vector of the movable antenna in the channel model and the precoding matrix of the base station.

[0093] In this embodiment, represents the position vector of the movable antenna, W=[w1,w2,…,w K ]∈C N×K Represents the digital precoding matrix at the base station.

[0094] S203: Create a decoding strategy, where the decoding strategy includes a decoding indication matrix.

[0095] In this embodiment, the SIC (Successive Interference Cancellation) decoding order of NOMA is defined as the ascending order of user channel gain, πk represents the user with the kth highest channel gain, that is:

[0096]

[0097] In this embodiment, in the traditional SIC decoding strategy, user π k It is necessary to decode the signals of all users whose decoding order is before it. The decoding strategy created by this application is: set a decoding optimization variable of 0-1 The decoded optimization variable is used to represent the user π k Whether to decode user π j The signal, the matrix generated according to the decoding optimization variable is the decoding indicator matrix, Represents user π k Need to decode user π j signal, Represents user π k No need to decode user π j signal.

[0098] In this embodiment, the K×K dimensional decoding indicator matrix is ​​expressed as:

[0099]

[0100] Among them, the kth row of the decoding indicator matrix represents user π k In the decoding strategy, the diagonal elements of the decoding indicator matrix are all 1s, ensuring that each user decodes its own signal. The lower triangular portion of the decoding indicator matrix is ​​all 0s. Because the SIC decoding order is based on increasing channel gain, users with poor channel conditions do not need to decode the signals of users with better channel conditions.

[0101] If all elements except the diagonal elements in the decoding indicator matrix are 0, the decoding strategy is downgraded to SDMA, that is, the signals of all other users are considered interference. If the upper triangular part of the decoding indicator matrix is ​​all 1, the decoding strategy at this time is traditional NOMA decoding. Therefore, the decoding strategies provided in this embodiment include SDMA and traditional NOMA decoding strategies.

[0102] S204: Calculate the minimum reachability rate from the base station to multiple user terminals according to the decoding strategy.

[0103] In this embodiment, user π k The received SINR (Signal to Interference plus Noise Ratio) when decoding its own information can be expressed as:

[0104]

[0105] User π k Decoding User π j The received SINR when the information is expressed as:

[0106]

[0107] 1≤k≤K-1,k+1≤j≤K

[0108] Therefore, decoding user π j The reachability when the information is expressed as:

[0109]

[0110] S205: Creating an optimization model for the minimum reachability rate, wherein the optimization model includes an optimization problem for the minimum reachability rate.

[0111] In this embodiment, the optimization model of the minimum reachability is:

[0112]

[0113] sttr(W H W)≤P max ,

[0114]

[0115] m π,π ∈{0, 1}

[0116] Among them, the constraint tr(W H W)≤P max Indicates that the total transmission power of the base station does not exceed the maximum transmission power P max ,constraint Indicates that each user's movable antenna can only be in area C k Internal movement, constraint m π,π ∈{0, 1} indicates that the elements in the decoding indication matrix are 0-1 variables.

[0117] S206: Split the optimization problem of the minimum achievable rate into multiple sub-problems, where the multiple sub-problems include optimizing a precoding matrix at the base station, optimizing a decoding indicator matrix, and optimizing a position vector of a movable antenna.

[0118] In this embodiment, the position vector of the movable antenna is jointly optimized. The precoding matrix W and decoding indicator matrix M at the base station are used to maximize the minimum reachability among all users.

[0119] In this embodiment, the optimization problem of the minimum reachability rate is a non-convex problem.

[0120] S207: Optimizing the precoding matrix and the decoding indicator matrix of the base station end by an alternating optimization algorithm in an inner loop to obtain an optimized decoding indicator matrix, an optimized precoding matrix and a corresponding minimum achievable rate.

[0121] Specifically, auxiliary variables are introduced to convert the sub-problem of optimizing the precoding matrix at the base station from a non-convex problem to a convex problem. The precoding matrix is ​​initially optimized according to the solving tool, and the decoding indicator matrix is ​​optimized according to the improved greedy algorithm to obtain the pre-optimized decoding indicator matrix. In the inner loop, the precoding matrix and the decoding indicator matrix are updated by the alternating optimization algorithm to obtain the optimized decoding indicator matrix, the optimized precoding matrix and the corresponding minimum reachability rate.

[0122] S208: In the outer loop, according to the corresponding minimum reachability rate, the position vector of the movable antenna is optimized by using the improved Hippo algorithm to obtain an optimized position vector of the movable antenna.

[0123] Specifically, the fitness of the position vector of the movable antenna is calculated by improving the Hippo algorithm, and the hippos are divided into strong hippos and weak hippos according to the fitness. The position of the hippos is updated by the alternating optimization algorithm, and the precoding matrix and decoding indicator matrix are calculated according to the inner and outer loop algorithm to obtain the optimal position vector of the movable antenna.

[0124] S209: If the optimized position vector of the movable antenna, the optimized decoding indicator matrix, and the optimized precoding matrix meet the threshold, the movable antenna communication system is optimized according to the optimized position vector of the movable antenna, the optimized decoding indicator matrix, and the optimized precoding matrix.

[0125] Specifically, the planar antenna array in the movable antenna communication system and the movable antenna at the user end are configured according to the optimized position vector of the movable antenna, the optimized decoding indicator matrix and the optimized precoding matrix to achieve optimization of the movable antenna communication system.

[0126] In addition, it should be noted that traditional orthogonal multiple access (OMA) has problems such as limited spectrum efficiency, limited user capacity and complex hardware implementation. Orthogonal frequency division multiple access (NOMA) has the characteristics of improving spectrum efficiency, reducing dependence on channel state information and supporting large-scale connections.

[0127] It can be seen from the above embodiments that by creating a channel model of a movable antenna communication system, creating a decoding strategy, calculating the minimum reachable rate from the base station end to multiple user ends through the decoding strategy, and creating an optimization model for the minimum reachable rate, the optimization problem of the minimum reachable rate is divided into multiple sub-problems, and the precoding matrix of the base station end, the decoding indicator matrix and the position vector of the movable antenna are optimized respectively. In the inner loop, the precoding matrix and the decoding indicator matrix of the base station end are optimized by an alternating optimization algorithm, and in the outer loop, the position vector of the movable antenna is optimized by an improved Hippo algorithm. The cyclic optimization of the sub-problems is realized through the inner and outer double loops, and the optimized decoding indicator matrix, the precoding matrix of the base station end and the position vector of the movable antenna are obtained. The performance of the movable antenna communication system is optimized, and the minimum reachable rate between users is improved.

[0128] In one embodiment of the present application, step S207 includes:

[0129] S2071: The sub-problem of optimizing the precoding matrix at the base station is converted into a convex problem by introducing auxiliary variables, and the convex problem is solved using a solving tool to obtain a pre-optimized precoding matrix.

[0130] In this embodiment, the sub-problem of optimizing the precoding matrix at the base station is expressed as:

[0131]

[0132] sttr(W H W)≤P max

[0133] Specifically, by introducing a vector of auxiliary variables, the sub-problem of optimizing the precoding matrix at the base station is converted from a non-convex problem to a convex problem.

[0134] Among them, the vector form of auxiliary variables is expressed as: q=[q1,q2,...,q K ].

[0135] Among them, the converted sub-problem is expressed as:

[0136]

[0137] sttr(W H W)≤P max

[0138] γ j ≥q j ,1≤j≤K

[0139] Among them, γ j ≥q j ,1≤j≤K is still non-convex, so convert it into a convex difference form:

[0140]

[0141] On the right side of the inequality Using the first-order Taylor approximation, the conversion is as follows:

[0142]

[0143] in, Representing variables The value at the t-1th iteration, Represents the variable q j The value at iteration t-1.

[0144] In this embodiment, the two inequalities converted into convex difference forms are relaxed into a second-order cone form, which is expressed as follows:

[0145]

[0146] Specifically, a solving tool is used to solve the sub-problem of optimizing the precoding matrix at the base station, which is converted into a convex problem, to obtain a preliminarily optimized precoding matrix.

[0147] Among them, solving tools include but are not limited to CVXPY, PyTorch and MATLAB tools.

[0148] S2072: Optimizing the decoding indicator matrix by using an improved greedy algorithm to obtain a preliminarily optimized decoding indicator matrix.

[0149] Specifically, an alternative decoding indicator matrix and an alternative minimum reachable rate are initialized, the initialized alternative decoding indicator matrix is ​​iteratively updated, and the alternative minimum reachable rate corresponding to the iterative alternative decoding indicator matrix is ​​calculated. If the corresponding alternative minimum reachable rate is greater than the initialized alternative minimum reachable rate, the initialized alternative decoding matrix is ​​updated and updated according to a set number of iterations to obtain multiple updated alternative decoding matrices and corresponding minimum reachable rates, the maximum alternative minimum reachable rate and corresponding decoding indicator matrix are obtained, and the decoding indicator matrix corresponding to the maximum alternative minimum reachable rate is marked as the optimized decoding indicator matrix.

[0150] S2073: Update the initially optimized decoding indicator matrix and the initially optimized precoding matrix through an alternating optimization algorithm to obtain updated decoding indicator matrix and precoding matrix.

[0151] Specifically, initialize the decoding indicator matrix, namely M (0) =I K , temperature T = T0, inner loop times t = 1, initialize the precoding matrix W (0) , and the precoding matrix W (0) Perform normalization:

[0152]

[0153] Specifically, given the antenna position vector Calculate the channel matrix According to the given channel matrix and decoding indicator matrix M (t-1) Update the precoding matrix to obtain the updated precoding matrix W (t) .

[0154] Specifically, according to the given channel matrix and the precoding matrix W (t) , update the decoding indicator matrix to obtain the updated decoding indicator matrix M (t) .

[0155] S2074: If the updated decoding indicator matrix and precoding matrix meet the set reachability threshold, the updated decoding indicator matrix and precoding matrix are marked as optimized decoding indicator matrix and optimized precoding matrix.

[0156] Specifically, if the updated decoding indicator matrix and precoding matrix are less than the set reachability threshold, that is:

[0157]

[0158] Then the updated decoding indicator matrix and precoding matrix are marked as the optimized decoding indicator matrix and the optimized precoding matrix, namely W * ←W (t) , M * ←M (t) .

[0159] Specifically, if the updated decoding indicator matrix and precoding matrix are greater than or equal to the set reachability threshold, the decoding indicator matrix and precoding matrix are iteratively updated t←t+1,T←α t T0, until the temperature is less than or equal to the temperature threshold, that is, T≤∈1.

[0160] S2075: Calculate and generate a corresponding minimum achievable rate based on the optimized decoding indicator matrix and the optimized precoding matrix.

[0161] Specifically, the minimum achievable rate of the optimized decoding indicator matrix and the optimized precoding matrix is ​​calculated That is, the corresponding minimum reachability

[0162] It can be seen from the above embodiment that by introducing auxiliary variables, the sub-problem of optimizing the precoding matrix at the base station end is converted from a non-convex problem to a convex problem, the decoding indicator matrix is ​​optimized by an improved greedy algorithm, and the decoding indicator matrix and the precoding matrix are alternately optimized and updated by the inner loop algorithm, and the corresponding minimum reachability rate is calculated and generated, thereby reducing the problem of leading to local optimal values.

[0163] In one embodiment of the present application, step S2072 includes:

[0164] S301: Initialize a candidate decoding indicator matrix and a candidate minimum achievable rate, and obtain an initialized candidate decoding indicator matrix and an initialized candidate minimum achievable rate.

[0165] In this embodiment, the sub-problem of optimizing the decoding indicator matrix is ​​expressed as:

[0166]

[0167] Among them, under the conditions of given precoding matrix and decoding indicator matrix, the minimum achievable rate among users is expressed as:

[0168]

[0169] Specifically, initialize the candidate decoding indicator matrix and the alternative minimum reachability rate According to the value of t-1 iteration, that is R g =0, R b =0, and the element index k=1, j=k+1 in the decoding indication matrix, where R g represents the initial optimal value before iteration, R b Indicates the initial worst value before iteration.

[0170] S302: Iteratively update the initialized candidate decoding indicator matrix to obtain an iterated candidate decoding indicator matrix, and calculate the candidate minimum reachability rate corresponding to the iterated candidate decoding indicator matrix.

[0171] Specifically, update the candidate decoding indicator matrix And according to And calculate the alternative minimum reachability rate

[0172] S303: If the corresponding candidate minimum reachable rate is greater than the initialized candidate minimum reachable rate, then the initialized candidate decoding matrix is ​​updated.

[0173] Specifically, if the corresponding alternative minimum reachability rate is greater than the initialized alternative minimum reachability rate, Then update the initialized candidate decoding matrix:

[0174] Specifically, if the corresponding alternative minimum reachability is less than the initialized alternative minimum reachability, the probability ξT is calculated, where ξ represents the coefficient and T represents the current temperature. If rand(0,1)<ξT, the difference solution is accepted. When R temp >R b When, update If the alternative solution is not the optimal differential solution, then directly reply to the alternative decoding indicator matrix. If rand(0,1)≥ξT, then the differential solution is not accepted and execution is performed. To restore the alternative decoding indicator matrix.

[0175] S304: updating the initialized decoding indicator matrix according to the set number of iterations to obtain multiple updated candidate decoding matrices, and calculating the minimum reachability of the multiple updated candidate decoding matrices.

[0176] Specifically, according to the set number of iterations, the initialized decoding indicator matrix is ​​updated, that is:

[0177] j←j+1, until j>K.

[0178] S305: Compare and obtain the largest candidate minimum achievable rate among the minimum achievable rates of the multiple updated candidate decoding matrices.

[0179] In this embodiment, R max ←max{R g ,R b}Get the maximum alternative minimum reachability rate R max .

[0180] S306: Obtain a decoding indicator matrix corresponding to the largest candidate minimum achievable rate, and mark the decoding indicator matrix corresponding to the largest candidate minimum achievable rate as an optimized decoding indicator matrix.

[0181] Specifically, if R max >R (t-1) , then according to M←M max The decoding indicator matrix is ​​updated to obtain an optimized decoding indicator matrix M.

[0182] Specifically, R max <R (t-1) , then the probability is calculated according to the Metropolis criterion like Then receive the differential solution and update the decoding indicator matrix M←M max ;like Then the difference solution is not accepted, M←M (t-1) .

[0183] From the above embodiment, it can be seen that by giving the position vector and precoding matrix of the movable antenna, a simulated annealing algorithm is introduced to receive the differential solution with a certain probability, thereby generating an improved greedy algorithm. This improves the randomness of the greedy search, enables the proposed algorithm to escape the local optimal value, and reduces the algorithm complexity.

[0184] In one embodiment of the present application, step S208 includes:

[0185] S2081: Calculate the fitness of the position vector of the movable antenna by improving the Hippo algorithm to obtain fitness values ​​of the position vectors of multiple movable antennas.

[0186] In this embodiment, the sub-problem of optimizing the position vector of the movable antenna is expressed as:

[0187]

[0188] In this embodiment, each Hippopotamus in the improved Hippopotamus algorithm represents an alternative solution of an antenna position vector, which includes the positions of all movable antennas, namely:

[0189]

[0190] in, Represents a hippopotamus, 1≤n≤N H ,1≤i≤I max , N H Represents the number of hippos, I max Improved maximum number of iterations of the Hippo algorithm.

[0191] In this embodiment, the method for initializing the position of the nth hippopotamus is as follows:

[0192]

[0193] Where b l represents the lower bound of the hippopotamus's movable area, b u Indicates the upper bound of the hippopotamus's movable area, i.e., the lower bound Upper bound r0 represents a random vector ranging from 0 to 1, and the dimension of the random vector is 3K×1.

[0194] Specifically, initialize N H The position of the hippopotamus, set the number of iterations i, let i = 1.

[0195] In this embodiment, the improved hippopotamus algorithm is divided into three stages: Stage 1, for each dominant hippopotamus, the alternative positions are calculated according to the male hippopotamus method and the female hippopotamus method, the corresponding fitness is calculated, and the position of the dominant hippopotamus is updated; Stage 2, for each weak hippopotamus, the position of the predator is generated, the alternative positions are calculated, the corresponding fitness is calculated, and the position of the weak hippopotamus is updated; Stage 3, for all hippos, the alternative positions are calculated, the corresponding fitness is calculated, and the positions of all hippos are updated.

[0196] S2082: Divide the position vectors of the multiple movable antennas into strong hippos and weak hippos according to the fitness values ​​of the position vectors of the multiple movable antennas.

[0197] Specifically, according to the fitness function Hippos are divided into strong hippos and weak hippos.

[0198] S2083: Calculate the candidate positions and corresponding fitness values ​​of the dominant hippopotamus, and update the position of the dominant hippopotamus through an alternating optimization algorithm according to the fitness values ​​corresponding to the dominant hippopotamus to obtain a position update strategy for the dominant hippopotamus.

[0199] In this embodiment, the candidate positions of the dominant hippopotamus and the corresponding fitness values ​​are calculated according to the position calculation method of the male hippopotamus and the position calculation method of the female hippopotamus.

[0200] The position of the male hippopotamus is calculated as follows:

[0201]

[0202] in, is the candidate position calculated by the male hippopotamus method for the nth hippopotamus, All N H The position of the hippopotamus with the best fitness among the hippopotamuses, r1 is a random number ranging from 0 to 1, is a random integer ranging from 1 to 2.

[0203] Define a sequence of random events:

[0204]

[0205] in, is a random integer ranging from 1 to 2, r2, r3, and r4 are random vectors ranging from 0 to 1, and r5 is a random number ranging from 0 to 1. is a random integer ranging from 0 to 1, I F Four random vectors with different value ranges are provided, from which the algorithm can randomly select a vector to participate in the hippopotamus position calculation. In addition, the probability is defined It will gradually decay with each iteration.

[0206] In this embodiment, the method for calculating the position of the female hippopotamus is as follows:

[0207]

[0208] Among them, ⊙ represents the Hadamard product, and From I F A random vector randomly drawn from , represents the average position of a certain number of hippos randomly selected from all hippos, and r6 and r7 are random numbers ranging from 0 to 1.

[0209] In summary, the position update strategy of the strong hippopotamus is as follows:

[0210]

[0211] S2084: Generate a predator position update strategy.

[0212] In this embodiment, the method for updating the position of the predator is as follows:

[0213]

[0214] Where, represents the predator position, and r8 represents a random vector with a value ranging from 0 to 1.

[0215] The element-wise distance between the nth hippopotamus and the predator is:

[0216] S2085: Calculate the fitness value corresponding to the weak hippopotamus according to the position update strategy of the predator, and update the position of the weak hippopotamus through an alternating optimization algorithm according to the fitness value corresponding to the weak hippopotamus to obtain the position update strategy of the weak hippopotamus.

[0217] In this embodiment, the position update strategy of the weak hippopotamus is as follows:

[0218]

[0219] in, Indicates that a random variable follows the Levy distribution, which can be obtained from a Levy distribution with a dimension of 3K×N H The nth column element in the matrix is ​​obtained from

[0220] Among them, the matrix generation method that obeys Levy distribution is as follows:

[0221]

[0222] Where β is a constant, R u and R v is a random matrix with a value range of 0 to 1 and a dimension of 3K×N H . |R v | indicates R v Take the absolute value of each element, Γ is the gamma function.

[0223] In summary, the position update strategy of the weak hippopotamus is as follows:

[0224]

[0225] S2086: Update the local upper bound and local lower bound of the local search to obtain local search range information.

[0226] In this embodiment, represents the lower bound of the local search, Represents the upper bound of the local search.

[0227] Specifically, the local upper bound and the local lower bound of the local search are updated according to the number of iterations, and the bounds of the local search shrink with the iterations.

[0228] The local lower bound is calculated as follows:

[0229] The local upper bound is calculated as follows:

[0230] S2087: Update the positions of the dominant hippopotamus and the weak hippopotamus according to the local search range information and the set number of iterations to obtain position vectors of multiple groups of movable antennas.

[0231] In this embodiment, the position calculation method of the local search is as follows:

[0232]

[0233] r 12 Represents a random number ranging from 0 to 1, r 11 represents a random vector with values ​​ranging from 0 to 1, r 13 represents a random number that follows a standard normal distribution. Is a random event I local A random vector randomly drawn from .

[0234] In this embodiment, the location update strategy for all hippos is as follows:

[0235]

[0236] Specifically, the position beyond the boundary in the antenna position vector is mapped to the nearest boundary through a mapping function, and the mapping function is as follows:

[0237]

[0238] Specifically, according to the set maximum number of iterations I max The position vectors of multiple groups of movable antennas are calculated.

[0239] S2088: Calculate precoding matrices and decoding indicator matrices for position vectors of multiple groups of movable antennas to obtain optimized position vectors of the movable antennas.

[0240] Specifically, precoding matrices and decoding indicator matrices of position vectors of multiple groups of movable antennas are calculated according to an alternating optimization algorithm to obtain optimized position vectors of the movable antennas.

[0241] Figure 4 A schematic diagram of the double-loop algorithm flow provided in an embodiment of the present application.

[0242] like Figure 4 As shown, in the inner loop, given the movable antenna position, the precoding matrix and the decoding indicator matrix are alternately optimized, and in the outer loop, the improved Hippo algorithm is used to optimize the user's movable antenna position.

[0243] From the above embodiment, it can be seen that the fitness of the position vector of the movable antenna is calculated by the improved Hippo algorithm, the hippopotamus are divided into strong hippopotamus and weak hippopotamus according to the fitness, the position of the hippopotamus is updated by the alternating optimization algorithm, and the precoding matrix and the decoding indicator matrix are calculated according to the inner and outer loop algorithms to obtain the optimal position vector of the movable antenna, thereby improving the accuracy of the position vector of the movable antenna.

[0244] Figure 5 This is a schematic diagram of the structure of the non-orthogonal multiple access communication system optimization device based on movable antennas provided by this application, such as Figure 5 As shown, the non-orthogonal multiple access communication system optimization device 50 based on movable antennas provided in this embodiment includes: a first creation module 501, an acquisition module 502, a second creation module 503, a calculation module 504, a third creation module 505, a splitting module 506, a first optimization module 507, a second optimization module 508 and a third optimization module 509.

[0245] The first creation module 501 is used to create a channel model of a movable antenna communication system, wherein the channel model includes a base station end and multiple user ends, and the user ends include a movable antenna and a user terminal.

[0246] The acquisition module 502 is configured to acquire the position vector of the movable antenna in the channel model and the precoding matrix of the base station.

[0247] The second creation module 503 is configured to create a decoding strategy, wherein the decoding strategy includes a decoding indication matrix.

[0248] The calculation module 504 is configured to calculate the minimum reachability rate from the base station to multiple user terminals according to the decoding strategy.

[0249] The third creation module 505 is used to create an optimization model of the minimum reachability rate, wherein the optimization model includes the optimization problem of the minimum reachability rate.

[0250] The splitting module 506 is configured to split the minimum reachability optimization problem into multiple sub-problems, wherein the multiple sub-problems include optimizing the precoding matrix at the base station, optimizing the decoding indicator matrix, and optimizing the position vector of the movable antenna.

[0251] The first optimization module 507 is configured to optimize the precoding matrix and the decoding indicator matrix of the base station end by an alternating optimization algorithm in an inner loop to obtain an optimized decoding indicator matrix, an optimized precoding matrix and a corresponding minimum achievable rate.

[0252] The second optimization module 508 is configured to optimize the position vector of the movable antenna by using the improved Hippo algorithm according to the corresponding minimum reachability rate in the outer loop to obtain an optimized position vector of the movable antenna.

[0253] The third optimization module 509 is used to optimize the movable antenna communication system according to the optimized movable antenna position vector, the optimized decoding indicator matrix and the optimized precoding matrix if the optimized movable antenna position vector, the optimized decoding indicator matrix and the optimized precoding matrix meet the threshold.

[0254] In a possible implementation, the first optimization module 507 includes:

[0255] The conversion unit 5071 is configured to convert the sub-problem of optimizing the precoding matrix at the base station into a convex problem by introducing auxiliary variables, and solve the convex problem using a solving tool to obtain a preliminarily optimized precoding matrix.

[0256] The optimization unit 5072 is configured to optimize the decoding indicator matrix by using an improved greedy algorithm to obtain a preliminarily optimized decoding indicator matrix.

[0257] The updating unit 5073 is configured to update the initially optimized decoding indicator matrix and the initially optimized precoding matrix by using an alternating optimization algorithm to obtain an updated decoding indicator matrix and precoding matrix.

[0258] The marking unit 5074 is configured to mark the updated decoding indicator matrix and precoding matrix as the optimized decoding indicator matrix and the optimized precoding matrix if the updated decoding indicator matrix and the precoding matrix meet the set reachability threshold.

[0259] The calculation unit 5075 is configured to calculate and generate a corresponding minimum achievable rate according to the optimized decoding indicator matrix and the optimized precoding matrix.

[0260] In a possible implementation, the optimization unit 5072 includes:

[0261] The initialization subunit 721 is configured to initialize the candidate decoding indicator matrix and the candidate minimum achievable rate, and obtain the initialized candidate decoding indicator matrix and the initialized candidate minimum achievable rate.

[0262] The first updating subunit 722 is configured to iteratively update the initialized candidate decoding indicator matrix to obtain an iterated candidate decoding indicator matrix, and calculate a candidate minimum reachability rate corresponding to the iterated candidate decoding indicator matrix.

[0263] The second updating subunit 723 is configured to update the initialized candidate decoding matrix if the corresponding candidate minimum reachable rate is greater than the initialized candidate minimum reachable rate.

[0264] The third updating subunit 724 is configured to update the initialized decoding indicator matrix according to a set number of iterations, obtain multiple updated candidate decoding matrices, and calculate minimum reachability rates of the multiple updated candidate decoding matrices.

[0265] The comparison subunit 725 is configured to compare and obtain the largest candidate minimum reachable rate among the minimum reachable rates of the multiple updated candidate decoding matrices.

[0266] The acquisition subunit 726 is configured to acquire a decoding indicator matrix corresponding to the largest candidate minimum reachable rate, and mark the decoding indicator matrix corresponding to the largest candidate minimum reachable rate as an optimized decoding indicator matrix.

[0267] In a possible implementation, the second optimization module 508 includes:

[0268] The first calculation subunit 801 is configured to calculate the fitness of the position vectors of the movable antennas by using an improved Hippo algorithm to obtain fitness values ​​of the position vectors of the multiple movable antennas.

[0269] The division subunit 802 is configured to divide the position vectors of the movable antennas into strong hippos and weak hippos according to the fitness values ​​of the position vectors of the plurality of movable antennas.

[0270] The second calculation subunit 803 is used to calculate the candidate positions and corresponding fitness values ​​of the dominant hippopotamus, and update the position of the dominant hippopotamus through an alternating optimization algorithm according to the fitness values ​​corresponding to the dominant hippopotamus to obtain a position update strategy for the dominant hippopotamus.

[0271] The generating subunit 804 is configured to generate a position updating strategy of the predator.

[0272] The third calculation subunit 805 is used to calculate the fitness value corresponding to the weak hippopotamus according to the predator's position update strategy, and update the position of the weak hippopotamus through an alternating optimization algorithm according to the fitness value corresponding to the weak hippopotamus to obtain the position update strategy of the weak hippopotamus.

[0273] The fourth updating subunit 806 is configured to update the local upper bound and the local lower bound of the local search to obtain local search range information.

[0274] The fifth updating subunit 807 is configured to update the positions of the dominant hippopotamus and the weak hippopotamus according to the local search range information and the set number of iterations, and obtain position vectors of multiple groups of movable antennas.

[0275] The fourth calculation subunit 808 is configured to calculate precoding matrices and decoding indicator matrices for position vectors of multiple groups of movable antennas to obtain optimized position vectors of the movable antennas.

[0276] In one embodiment of the present application, the channel model of the movable antenna communication system in the first creation module 501 is:

[0277]

[0278] Where u k represents the local position coordinates of the movable antenna equipped with user terminal k; represents the conjugate transpose of the received field response vector between the base station and user terminal k; ∑ k represents the path response matrix; G k Represents the field response matrix at the base station.

[0279] The non-orthogonal multiple access communication system optimization device based on movable antennas provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effects are similar, and are not described in detail in this embodiment.

[0280] Figure 6 This is a schematic diagram of the structure of the non-orthogonal multiple access communication system optimization device based on movable antennas provided in this application. Figure 6 As shown, the mobile antenna-based non-orthogonal multiple access communication system optimization device 60 provided in this embodiment includes: at least one processor 601 and a memory 602. Optionally, the device 60 also includes a communication component 603. The processor 601, the memory 602, and the communication component 603 are connected via a bus 604.

[0281] In a specific implementation process, at least one processor 601 executes the computer-executable instructions stored in the memory 602, so that the at least one processor 601 executes the above-mentioned method for optimizing a non-orthogonal multiple access communication system based on movable antennas.

[0282] The specific implementation process of the processor 601 can be found in the above method embodiment. Its implementation principle and technical effects are similar and will not be repeated here in this embodiment.

[0283] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly implemented by a hardware processor or implemented by a combination of hardware and software modules in the processor.

[0284] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (NVM), such as at least one disk memory.

[0285] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be classified into address buses, data buses, and control buses. For ease of illustration, the buses in the drawings of this application are not limited to just one bus or just one type of bus.

[0286] The present application also provides a computer program product, including a computer program, which implements the above-mentioned method for optimizing a non-orthogonal multiple access communication system based on movable antennas when executed by a processor.

[0287] The present application also provides a computer-readable storage medium, which stores computer-executable instructions. When a processor executes the computer-executable instructions, the above-mentioned method for optimizing a non-orthogonal multiple access communication system based on a movable antenna is implemented.

[0288] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile memory device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0289] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be an integral part of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0290] The division of units is merely a logical functional division; actual implementations may employ alternative divisions, such as combining or integrating multiple units or components into another system, or omitting or disabling certain features. Furthermore, any direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units, either through an interface, electrical, mechanical, or other means.

[0291] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0292] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0293] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0294] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0295] Finally, it should be noted that those skilled in the art will readily identify other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art not disclosed herein. The present invention is not limited to the precise structure described above and illustrated in the accompanying drawings, and various modifications and variations may be made without departing from the scope thereof. The scope of the present invention is limited solely by the appended claims.

Claims

1. A method for optimizing a non-orthogonal multiple access communication system based on a movable antenna, characterized in that: Applicable to computer equipment, including: Creating a channel model of the movable antenna communication system, wherein the channel model includes a base station end and multiple user ends, and the user ends include a movable antenna and a user terminal; Obtaining a position vector of a movable antenna and a precoding matrix of a base station in the channel model; Creating a decoding strategy, wherein the decoding strategy includes a decoding indicator matrix; Calculating the minimum reachability rate from the base station to the multiple user terminals according to the decoding strategy; Creating an optimization model for the minimum reachability rate, wherein the optimization model includes an optimization problem for the minimum reachability rate; Splitting the optimization problem of the minimum achievable rate into multiple sub-problems, wherein the multiple sub-problems include optimizing the precoding matrix of the base station, optimizing the decoding indicator matrix, and optimizing the position vector of the movable antenna; In an inner loop, the precoding matrix and the decoding indicator matrix of the base station are optimized by an alternating optimization algorithm to obtain an optimized decoding indicator matrix, an optimized precoding matrix and a corresponding minimum achievable rate; In an outer loop, according to the corresponding minimum reachability rate, the position vector of the movable antenna is optimized by using an improved Hippo algorithm to obtain an optimized position vector of the movable antenna; If the optimized position vector of the movable antenna, the optimized decoding indicator matrix and the optimized precoding matrix meet the threshold, the movable antenna communication system is optimized according to the optimized position vector of the movable antenna, the optimized decoding indicator matrix and the optimized precoding matrix.

2. The method according to claim 1, characterized in that The step of optimizing the precoding matrix and the decoding indicator matrix of the base station end by an alternating optimization algorithm in the inner loop to obtain an optimized decoding indicator matrix, an optimized precoding matrix, and a corresponding minimum achievable rate includes: The subproblem of optimizing the precoding matrix at the base station is converted into a convex problem by introducing auxiliary variables, and the convex problem is solved according to a solving tool to obtain a pre-optimized precoding matrix; Optimizing the decoding indicator matrix by an improved greedy algorithm to obtain a preliminarily optimized decoding indicator matrix; Updating the initially optimized decoding indicator matrix and the initially optimized precoding matrix by an alternating optimization algorithm to obtain an updated decoding indicator matrix and a precoding matrix; If the updated decoding indicator matrix and precoding matrix meet the set reachability threshold, marking the updated decoding indicator matrix and precoding matrix as the optimized decoding indicator matrix and the optimized precoding matrix; A corresponding minimum achievable rate is calculated and generated according to the optimized decoding indicator matrix and the optimized precoding matrix.

3. The method according to claim 2, characterized in that The step of optimizing the decoding indicator matrix by using an improved greedy algorithm to obtain a preliminarily optimized decoding indicator matrix includes: Initializing an alternative decoding indicator matrix and an alternative minimum reachable rate, and obtaining an initialized alternative decoding indicator matrix and an initialized alternative minimum reachable rate; Iteratively updating the initialized candidate decoding indicator matrix to obtain an iterated candidate decoding indicator matrix, and calculating the candidate minimum reachability rate corresponding to the iterated candidate decoding indicator matrix; If the corresponding candidate minimum reachable rate is greater than the initialized candidate minimum reachable rate, updating the initialized candidate decoding matrix; Updating the initialized decoding indicator matrix according to a set number of iterations to obtain multiple updated candidate decoding matrices, and calculating minimum reachability rates of the multiple updated candidate decoding matrices; Comparing and obtaining the largest candidate minimum achievable rate among the minimum achievable rates of the multiple updated candidate decoding matrices; A decoding indication matrix corresponding to the maximum candidate minimum achievable rate is obtained, and the decoding indication matrix corresponding to the maximum candidate minimum achievable rate is marked as an optimized decoding indication matrix.

4. The method according to claim 1, wherein The step of optimizing the position vector of the movable antenna by using an improved Hippo algorithm according to the corresponding minimum reachability rate in the outer loop to obtain an optimized position vector of the movable antenna includes: Calculating the fitness of the position vector of the movable antenna by improving the Hippo algorithm to obtain fitness values ​​of the position vectors of the plurality of movable antennas; Dividing the position vectors of the movable antennas into strong hippos and weak hippos according to the fitness values ​​of the position vectors of the multiple movable antennas; Calculating the candidate positions and corresponding fitness values ​​of the dominant hippopotamus, and updating the position of the dominant hippopotamus by an alternating optimization algorithm according to the fitness value corresponding to the dominant hippopotamus, to obtain a position update strategy for the dominant hippopotamus; Generate a predator's position update strategy; Calculating the fitness value corresponding to the vulnerable hippopotamus according to the position update strategy of the predator, and updating the position of the vulnerable hippopotamus by an alternating optimization algorithm according to the fitness value corresponding to the vulnerable hippopotamus, to obtain the position update strategy of the vulnerable hippopotamus; Update the local upper bound and local lower bound of the local search to obtain local search range information; updating the positions of the dominant hippopotamus and the weak hippopotamus according to the local search range information and the set number of iterations to obtain position vectors of multiple groups of movable antennas; The precoding matrix and the decoding indicator matrix of the position vectors of the multiple groups of movable antennas are calculated to obtain the optimized position vectors of the movable antennas.

5. The method according to any one of claims 1 to 4, characterized in that The channel model of the movable antenna communication system is: Where u k represents the local position coordinates of the movable antenna equipped with user terminal k; represents the conjugate transpose of the received field response vector between the base station and user terminal k; ∑ k represents the path response matrix; G k Represents the field response matrix at the base station.

6. A non-orthogonal multiple access communication system optimization device based on a movable antenna, characterized in that: Applicable to computer equipment, including: A first creation module is configured to create a channel model of the movable antenna communication system, wherein the channel model includes a base station end and multiple user ends, and the user ends include a movable antenna and a user terminal; An acquisition module, configured to acquire a position vector of a movable antenna and a precoding matrix of a base station in the channel model; A second creation module is configured to create a decoding strategy, wherein the decoding strategy includes a decoding indication matrix; A calculation module, configured to calculate a minimum reachability rate from a base station end to the plurality of user ends according to the decoding strategy; A third creation module is configured to create an optimization model for the minimum reachable rate, wherein the optimization model includes an optimization problem for the minimum reachable rate; A splitting module is used to split the optimization problem of the minimum reachable rate into multiple sub-problems, wherein the multiple sub-problems include optimizing the precoding matrix of the base station end, optimizing the decoding indicator matrix, and optimizing the position vector of the movable antenna; A first optimization module is configured to optimize the precoding matrix and the decoding indicator matrix of the base station end by an alternating optimization algorithm in an inner loop to obtain an optimized decoding indicator matrix, an optimized precoding matrix, and a corresponding minimum achievable rate; A second optimization module is configured to optimize the position vector of the movable antenna by using an improved Hippo algorithm according to the corresponding minimum reachability rate in an outer loop to obtain an optimized position vector of the movable antenna; The third optimization module is used to optimize the movable antenna communication system according to the optimized position vector of the movable antenna, the optimized decoding indicator matrix and the optimized precoding matrix if the optimized position vector of the movable antenna, the optimized decoding indicator matrix and the optimized precoding matrix meet the threshold.

7. The device according to claim 6, characterized in that The first optimization module includes: a conversion unit, configured to convert the subproblem of optimizing the precoding matrix of the base station into a convex problem by introducing auxiliary variables, and solve the convex problem according to a solving tool to obtain a preliminarily optimized precoding matrix; an optimization unit, configured to optimize the decoding indicator matrix by using an improved greedy algorithm to obtain a preliminarily optimized decoding indicator matrix; An updating unit, configured to update the preliminarily optimized decoding indicator matrix and the preliminarily optimized precoding matrix by an alternating optimization algorithm to obtain an updated decoding indicator matrix and precoding matrix; a marking unit, configured to mark the updated decoding indicator matrix and precoding matrix as an optimized decoding indicator matrix and an optimized precoding matrix if the updated decoding indicator matrix and precoding matrix meet a set reachability threshold; A calculation unit is used to calculate and generate a corresponding minimum achievable rate based on the optimized decoding indicator matrix and the optimized precoding matrix.

8. A non-orthogonal multiple access communication system optimization device based on a movable antenna, characterized in that: include: at least one processor and memory; The memory stores computer-executable instructions; The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor performs the method for optimizing a non-orthogonal multiple access communication system based on movable antennas according to any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method for optimizing a non-orthogonal multiple access communication system based on a movable antenna according to any one of claims 1 to 5.

10. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the method for optimizing a non-orthogonal multiple access communication system based on a movable antenna according to any one of claims 1 to 5.

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