Joint optimization method for movable antenna enabled unmanned aerial vehicle mu-mimo communication and related device

Abstract

The application discloses a kind of movable antenna power supply unmanned plane MU-MIMO communication joint optimization method and related device, it is related to wireless communication technical field, the method includes: for single unmanned plane as air base station provides communication service for multiple ground users, unmanned plane is equipped with plane movable antenna array, each ground user is equipped with uniform plane array, realizes the communication scene of multiple-input multiple-output uplink communication, first construct joint optimization model, joint optimization model maximizes average uplink rate as optimization goal, with transmission power constraint, movable antenna minimum distance constraint and movable antenna position constraint as constraint condition, realize the joint optimization of movable antenna position and beam forming parameter, then solve joint optimization model, obtain joint optimization scheme, the application can carry out joint optimization to movable antenna power supply unmanned plane assisted MU-MIMO uplink communication process, improve communication performance.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to a joint optimization method and related apparatus for enabling MU-MIMO communication of unmanned aerial vehicles with a movable antenna. Background Technology

[0002] In recent years, unmanned aerial vehicle (UAV)-assisted communication has attracted widespread attention from academia and industry due to its high mobility, flexible deployment capabilities, and excellent line-of-sight propagation characteristics. By jointly designing UAV trajectories and multi-user beamforming parameters, significant improvements in spectral efficiency can be achieved compared to traditional terrestrial networks. Most related research focuses on fixed antenna array configurations, optimizing system performance through joint design of trajectory planning and resource allocation. However, the inherent limitations of fixed antenna array geometry restrict further utilization of spatial degrees of freedom.

[0003] Against this backdrop, movable antenna technology has emerged as a new paradigm. This technology allows antenna elements to move freely within a designated area, thereby freeing up additional spatial degrees of freedom. Compared with traditional fixed antenna arrays, movable antennas can adaptively reconfigure the array geometry according to channel conditions, effectively suppressing inter-user interference while enhancing received signal strength. Currently, several studies have investigated beamforming assisted by movable antennas in terrestrial communication systems, demonstrating the advantages of movable antenna arrays in UAV-to-ground communication. However, these studies focus on linear movable antennas (translated along a single straight line, adapting to one-dimensional coverage) and have not yet extended to planar movable antennas (arranged in a two-dimensional plane, supporting two-dimensional beam scanning and large-area coverage). Furthermore, these studies are limited to single-antenna ground users, and consideration of multi-antenna ground users is insufficient. Therefore, a joint optimization technique for UAV-assisted MU-MIMO (Multi-User Multiple-Input Multiple-Output) uplink communication equipped with planar movable antennas is urgently needed. Summary of the Invention

[0004] The purpose of this application is to provide a joint optimization method and related apparatus for MU-MIMO communication of UAVs with movable antennas, which can jointly optimize the MU-MIMO uplink communication process assisted by UAVs equipped with planar movable antennas and improve communication performance.

[0005] To achieve the above objectives, this application provides the following solution.

[0006] In a first aspect, this application provides a joint optimization method for MU-MIMO communication of a UAV enabled by a movable antenna, the joint optimization method for MU-MIMO communication of a UAV enabled by a movable antenna includes: A joint optimization model is constructed for a single UAV acting as an airborne base station to provide communication services to multiple ground users. The UAV flies along a preset trajectory during the mission time and is equipped with a planar movable antenna array, which includes multiple movable antennas. Each ground user is equipped with a uniform planar array. Multiple ground users send data to the UAV through the uniform planar array and the planar movable antenna array to achieve multi-input multi-output uplink communication. The joint optimization model aims to maximize the average uplink sum and rate, and uses transmit power constraints, minimum distance constraints of movable antennas, and position constraints of movable antennas as constraints to achieve joint optimization of the position of movable antennas and beamforming parameters. The beamforming parameters include a merging matrix and a precoding matrix. Solving the joint optimization model yields a joint optimization scheme. The joint optimization scheme includes the movable antenna position vector and beamforming parameters for each time slot within the mission time. The movable antenna position vector includes the position of each movable antenna.

[0007] Secondly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to implement the above-described joint optimization method for MU-MIMO communication of a UAV enabled by a movable antenna.

[0008] Thirdly, this application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the aforementioned joint optimization method for MU-MIMO communication of a UAV enabled by a movable antenna.

[0009] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned joint optimization method for MU-MIMO communication of a UAV enabled by a movable antenna.

[0010] According to the specific embodiments provided in this application, this application has the following technical effects.

[0011] This application provides a joint optimization method and related apparatus for MU-MIMO communication assisted by a mobile antenna for unmanned aerial vehicles (UAVs). The method addresses a scenario where a single UAV acts as an airborne base station providing communication services to multiple ground users. The UAV flies along a preset trajectory during the mission period and is equipped with a planar mobile antenna array comprising multiple mobile antennas. Each ground user is equipped with a uniform planar array. Multiple ground users transmit data to the UAV through the uniform planar array and the planar mobile antenna array, achieving multi-input multi-output (MIMO) uplink communication. First, a joint optimization model is constructed, with the optimization objective of maximizing the average uplink sum and rate. Constraints include transmit power constraints, minimum distance constraints for mobile antennas, and position constraints for mobile antennas. This achieves joint optimization of the position of the mobile antennas and beamforming parameters. Then, the joint optimization model is solved to obtain a joint optimization scheme. The joint optimization scheme includes the position vector of the mobile antennas and beamforming parameters for each time slot during the mission period. This allows for joint optimization of the MU-MIMO uplink communication process assisted by a UAV equipped with a planar mobile antenna, improving communication performance. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is an application environment diagram of a joint optimization method for MU-MIMO communication of unmanned aerial vehicles (UAVs) enabled by a movable antenna, as provided in Embodiment 1 of this application.

[0014] Figure 2 This is a flowchart illustrating a joint optimization method for enabling MU-MIMO communication of a UAV with a movable antenna, as provided in Embodiment 1 of this application.

[0015] Figure 3 This is a schematic diagram of a MU-MIMO communication scenario provided in Embodiment 1 of this application.

[0016] Figure 4 This is a schematic diagram illustrating the calculation of pitch and azimuth angles provided in Embodiment 1 of this application.

[0017] Figure 5 This is a schematic diagram of the drone trajectory and ground user location provided in Embodiment 1 of this application.

[0018] Figure 6 This is a schematic diagram showing the changes in uplink speed and rate over time for different methods provided in Embodiment 1 of this application.

[0019] Figure 7 This is a schematic diagram showing the variation curves of average uplink speed and rate with maximum transmit power for different methods provided in Embodiment 1 of this application.

[0020] Figure 8 A schematic diagram showing the variation of the average uplink speed and rate of different methods provided in Embodiment 1 of this application with the number of movable antennas equipped on the UAV.

[0021] Figure 9 This is a schematic diagram of the structure of a computer device provided in Embodiment 2 of this application. Detailed Implementation

[0022] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0023] Example 1 The joint optimization method for enabling MU-MIMO communication of UAVs with a movable antenna provided in this application embodiment can be applied to, for example... Figure 1 The application environment shown illustrates this. The terminal communicates with the server via a network. A data storage system stores the data the server needs to process. This system can be set up independently, integrated into the server, or located in the cloud or on another server. The terminal can send a joint optimization request to the server. Upon receiving the request, the server constructs a joint optimization model for a single UAV acting as an aerial base station providing communication services to multiple ground users. This model aims to maximize the average uplink speed and data rate, using transmit power constraints, minimum distance constraints for movable antennas, and position constraints for movable antennas as constraints. It achieves joint optimization of the movable antenna position and beamforming parameters, including a merging matrix and a precoding matrix. The joint optimization model is solved to obtain a joint optimization scheme. This scheme includes the movable antenna position vector and beamforming parameters for each time slot within the task time. The movable antenna position vector includes the position of each movable antenna. The server can then feed back this joint optimization result—the joint optimization scheme for the joint optimization request—to the terminal.

[0024] Furthermore, in some embodiments, the joint optimization method for MU-MIMO communication of UAVs enabled by movable antennas can also be implemented by a server or a terminal independently. For example, the terminal can directly process the joint optimization request to be processed, or the server can obtain the joint optimization request to be processed from the data storage system and process it.

[0025] The terminals can be, but are not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices, while portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. Servers can be implemented using independent servers, server clusters composed of multiple servers, or cloud servers.

[0026] In one exemplary embodiment, such as Figure 2 As shown, a joint optimization method for enabling MU-MIMO communication of UAVs with a movable antenna is provided. This method is executed by a computer device, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 The following steps, S1 to S2, are used as an example to illustrate the process of using a server in the example.

[0027] Step S1: Construct a joint optimization model for a single UAV acting as an airborne base station to provide communication services to multiple ground users. The UAV flies along a preset trajectory during the mission time. The UAV is equipped with a planar movable antenna array, which includes multiple movable antennas. Each ground user is equipped with a uniform planar array. Multiple ground users send data to the UAV through the uniform planar array and the planar movable antenna array to achieve multi-input multi-output uplink communication. The joint optimization model aims to maximize the average uplink sum and rate. It uses transmit power constraints, minimum distance constraints of movable antennas, and position constraints of movable antennas as constraints to achieve joint optimization of the position of movable antennas and beamforming parameters. The beamforming parameters include a merging matrix and a precoding matrix.

[0028] Step S2: Solve the joint optimization model to obtain the joint optimization scheme; the joint optimization scheme includes the movable antenna position vector and beamforming parameters for each time slot within the mission time, and the movable antenna position vector includes the position of each movable antenna.

[0029] By implementing steps S1 to S2 above, this embodiment can jointly optimize the MU-MIMO uplink communication process assisted by a drone equipped with a planar movable antenna, thereby improving communication performance.

[0030] The following is a detailed description of a joint optimization method for enabling MU-MIMO communication of UAVs with a movable antenna, which is used in this embodiment.

[0031] (I) Communication Scenarios This embodiment addresses a UAV-assisted MU-MIMO (Multi-User Multiple-Input Multiple-Output) uplink communication scenario powered by a movable antenna, such as... Figure 3 As shown, a single UAV acts as an airborne base station to provide communication services to multiple ground users. The UAV flies along a preset trajectory during the mission time. The UAV is equipped with a planar movable antenna array, which includes multiple movable antennas. Each ground user is equipped with a uniform planar array. Multiple ground users send data to the UAV through the uniform planar array and the planar movable antenna array. The UAV then sends the data to the ground base station, realizing multiple-input multiple-output uplink communication.

[0032] Specifically, such as Figure 3 As shown, a drone acts as an aerial base station during the mission. The drone flies along a pre-planned trajectory, with its flight altitude fixed at [altitude missing]. Task time Classified as A series of consecutive time slots, each time slot having a length of [missing information]. In each time slot ( , When the number of time slots is given, the location of the drone is... , For drones in time slots Location, For drones in time slots x-coordinate, For drones in time slots The y-coordinate is given, and based on this, the preset trajectory for the drone's flight is... .

[0033] The drone is equipped with a planar movable antenna array, which includes... A movable antenna, a planar movable antenna array, flies with the drone, and the movable antennas within it constantly change their positions. ( , (Number of movable antennas) in time slots The position is , For movable antennas In the time slot Location, For movable antennas In the time slot x-coordinate, For movable antennas In the time slot y-coordinate, For size The area The value is a constant, representing the placement area for movable antennas. All movable antennas must be located within this area. Inside. To avoid coupling, any two movable antennas... and movable antenna The minimum interval between them satisfies , For movable antennas In the time slot Location, The minimum distance.

[0034] Ground users ( , The location of (the number of ground users) , For ground users Location, For ground users x-coordinate, For ground users The y-coordinate is known.

[0035] Each ground user is equipped with a uniform planar array, which includes... 1 antenna, each antenna spaced apart , The carrier wavelength.

[0036] In the time slot Each ground user sends to the drone A data stream, the signal received by the drone ( (For the set of complex numbers) is: ; in, For drones in time slots Received signal; For ground users In the time slot Channel matrix to the drone This represents the number of antennas in a uniform planar array. For ground users In the time slot The precoding matrix, The number of data streams; For ground users In the time slot The data sent; For ground users In the time slot Channel matrix to the UAV; For ground users In the time slot The precoding matrix; For ground users In the time slot The data sent; To obey Distributed Gaussian additive white noise, For noise power, for OK The identity matrix of columns.

[0037] In the time slot The signals received by the drone come from ground users. The desired signal consists of interference from other ground users. By treating the interference as noise and employing a linear receive beamforming strategy, the ground user... In the time slot Estimated signal for: ; in, For ground users In the time slot The merged matrix, i.e., the drone in the time slot Receiving ground users The merging matrix of transmitted signals.

[0038] Therefore, ground users In the time slot Uplink speed The formula for calculating (i.e., the transmission rate between the drone and the drone) is: ; ; in, for OK The identity matrix of columns, This refers to the number of movable antennas; For ground users In the time slot Channel matrix to the UAV; For ground users In the time slot The precoding matrix; For ground users In the time slot The noise interference received; For ground users In the time slot Channel matrix to the UAV; For ground users In the time slot The precoding matrix; This represents noise power.

[0039] This embodiment calculates the channel matrix based on the following channel model, assuming the air-to-ground communication link is a line-of-sight channel and the ground user... In the time slot Channel matrix to UAV The formula for calculating the channel matrix (i.e., the channel matrix between the UAV and the UAV) is: ; in, It is a constant. , and These are the speed of light and the carrier frequency, respectively. For ground users In the time slot Distance to the drone (i.e., the distance between you and the drone). ; For ground users In the time slot Antenna array response with the drone.

[0040] Ground users In the time slot Antenna array response with drone Represented as: ; Depend on Decide, Depend on Therefore, This can be further expressed as: ; in, Antenna array response vector of a planar movable antenna array for a drone; For ground users In the time slot To the drone's pitch angle; For ground users In the time slot To the azimuth angle of the drone; For time slots The position vector of the movable antenna, ; For ground users The antenna array response vector of the equipped uniform planar array; For drones in time slots to ground users The pitch angle; For drones in time slots to ground users The azimuth angle.

[0041] Represented as: ; ; in, The carrier wavelength; For ground users In the time slot To movable antenna The signal propagation phase difference, based on , and Calculated; For movable antennas In the time slot The x-coordinate; For movable antennas In the time slot The y-coordinate.

[0042] Represented as: ; The first square bracket contains numbers from 0 to... -1, the second set of square brackets contains values ​​from 0 to... -1; This represents the number of antennas in the x-direction of a uniform planar array. This represents the number of antennas in the y-direction of a uniform planar array.

[0043] like Figure 4 As shown, and The calculation formula is: ; in, This refers to the flight altitude of the drone; That is For ground users In the time slot Distance to the drone; For ground users In the time slot The y-coordinate of the drone in the time slot The absolute value of the difference between the y-coordinates; For ground users In the time slot The x-coordinate of the drone in the time slot The absolute value of the difference between the x-coordinates.

[0044] at this time, and The calculation formula is: ; .

[0045] (II) Model Construction This embodiment constructs a joint optimization model for a single UAV acting as an airborne base station to provide communication services to multiple ground users. The joint optimization model aims to maximize the average uplink sum and rate, and uses transmit power constraints, minimum distance constraints for movable antennas, and position constraints for movable antennas as constraints to achieve joint optimization of the position of movable antennas and beamforming parameters. The beamforming parameters include a merging matrix and a precoding matrix.

[0046] The objective of this embodiment is to jointly optimize the location of the movable antenna. Merging matrices and precoding matrix To maximize the average uplink sum and rate of MU-MIMO uplink communication throughout the entire task time, the joint optimization model is as follows: ; in, For time slots The movable antenna position vector; For ground users In the time slot The precoding matrix; For ground users In the time slot The merged matrix; The number of time slots; The number of ground users; For ground users In the time slot Uplink speed, For uplink and speed, For average uplink and rate; The transmit power constraint means that the transmit power of a ground user must be less than or equal to the maximum transmit power. This is the maximum transmission power; The minimum distance constraint for movable antennas means that the distance between movable antennas must be greater than or equal to the minimum distance to avoid coupling effects. For movable antennas In the time slot Location; For movable antennas In the time slot Location; Minimum distance; For movable antenna position constraints, it means that the movable antenna can only move within the specified area; This is the area for placing a movable antenna.

[0047] (III) Model Solving This embodiment solves the joint optimization model to obtain a joint optimization scheme. The joint optimization scheme includes the movable antenna position vector and beamforming parameters for each time slot within the mission time. The movable antenna position vector includes the position of each movable antenna.

[0048] It should be noted that any method can be used to solve the above joint optimization model.

[0049] As an example, since the optimization variables include the position of the movable antenna and the beamforming parameters, and the constraints include the minimum distance constraint of the non-convex movable antenna, solving this optimization problem by combining the optimization model is an NP-hard problem (a class of problems that are extremely difficult to solve, and it is very difficult to find the optimal solution; only approximate solutions are possible). Therefore, this embodiment uses the PSO (Particle Swarm Optimization)-WMMSE (Weighted Minimum Mean Square Error) joint optimization algorithm to solve the problem.

[0050] The PSO-WMMSE joint optimization algorithm will be described in detail below.

[0051] To address the optimization problem of solving the joint optimization model, this embodiment proposes a PSO-WMMSE joint optimization algorithm to effectively decouple the optimization variable of the movable antenna position from the optimization variable of the beamforming parameter. Specifically, the WMMSE algorithm is applied to iteratively solve the beamforming parameter and optimize the beamforming parameter and transmit power, while the PSO algorithm is applied to solve the movable antenna position and optimize the antenna position.

[0052] (1) WMMSE algorithm For a given movable antenna location Solving the joint optimization model, this optimization problem can be decomposed into: Each sub-problem corresponds to a time slot. Each time slot The subproblem (i.e., the beamforming parameter optimization sub-model) can be expressed as: ; The above subproblem can be equivalently transformed into a sum-mean-square error minimization problem (i.e., the sub-model after the first transformation): ; in, For ground users In the time slot The mean square error matrix.

[0053] The expression is: ; in, It is an identity matrix.

[0054] We can introduce an auxiliary weight matrix that is positive semidefinite. Thus, the sub-model after the first transformation can be equivalently transformed into the sub-model after the second transformation: ; in, For ground users In the time slot The auxiliary weight matrix.

[0055] After obtaining the second transformed sub-model, an alternating iterative method is used to optimize it sequentially. , , These three variables.

[0056] 1) Optimize the merge matrix fixed The second transformed sub-model can be simplified to the first simplified sub-model: ; The first simplified sub-model is actually an MMSE (Minimum Mean Square Error) receiver problem. Applying the first simplified sub-model to... Taking the derivative directly and setting it to 0, we get: ; Solving the above equation yields the optimal solution: ; in, for The optimal solution.

[0057] 2) Optimize the auxiliary weight matrix fixed The second transformed sub-model can be simplified to a second simplified sub-model: ; At this point, the second simplified sub-model regarding It is convex, and the second simplified sub-model is for Taking the derivative directly and setting it to 0, we get: ; Solving the above equation yields the optimal solution: ; in, for The optimal solution.

[0058] 3) Optimize the precoding matrix fixed The second transformed sub-model can be simplified into a third simplified sub-model: ; Because the third simplified sub-model contains a precoding matrix To address the transmit power constraint and facilitate processing, Lagrange multipliers are introduced. The transmit power constraint in the third simplified sub-model is incorporated into the objective function. In this process, the Lagrange function is obtained. for: ; in, For ground users In the time slot Lagrange multipliers.

[0059] Substituting into the Lagrange function and ignore with Irrelevant terms, we get: ; in, To simplify the Lagrange function; For time slots The intermediate matrix, , For ground users In the time slot Channel matrix to the drone For ground users In the time slot The merged matrix, For ground users In the time slot The auxiliary weight matrix; for OK The identity matrix of columns.

[0060] The simplified Lagrange function for Taking the derivative directly and setting it to 0, we get: ; Solving the above equation, we obtain the optimal solution as follows: ; in, for The optimal solution; Solving using the bisection method satisfies the complementary relaxation condition, thus... , The representation approaches.

[0061] Based on the above process, the specific steps of the WMMSE algorithm are as follows: 1) Input: First maximum number of iterations Maximum transmission power ; 2) Output: Optimized precoding matrix Merging matrices Auxiliary weight matrix ; 3) Initialization: Non-zero and satisfy ; 4) Number of iterations in the main loop ( From 1 to ): 5) Update the merge matrix ; 6) Update the auxiliary weight matrix ; 7) Update the precoding matrix ; 8) End the loop.

[0062] (2) PSO algorithm For a given beamforming parameter and Solving the joint optimization model can simplify this optimization problem into a subproblem of optimizing the position of the movable antenna, which can be expressed as: ; Obviously, the position of the movable antenna varies in different time slots. These are independent of each other; therefore, the location of the movable antenna can be optimized separately in different time slots. The above sub-problems can be further decomposed into: Each sub-problem corresponds to a time slot. Each time slot The subproblem (i.e., the sub-model for optimizing the position of the movable antenna) can be expressed as: ; Due to the objective function Through the channel matrix The optimization variable of the movable antenna position is highly nonlinearly coupled, and the minimum distance constraint of the movable antenna is non-convex. Therefore, the PSO algorithm is used to solve the problem.

[0063] Let the particle population size (i.e., the number of particles in the particle population) be... Each particle ( The optimization variable is the movable antenna position vector consisting of the positions of all movable antennas. The velocity and position of each particle are initialized, and the position of each particle represents a value of the position vector of the movable antenna.

[0064] The PSO algorithm is typically used for maximization problems, and considering the penalty for constraint violation, the fitness function (i.e., the formula for calculating the fitness value) is defined as follows: ; ; in, For particles fitness value, For particles Location; The number of ground users; For particle-based Position and particles Beamforming parameters (in determining particle) After determining the location, the WMMSE algorithm is used to calculate the ground user's location. In the time slot Uplink speed; For particles The penalty value is used to handle the minimum distance constraint of the movable antenna; As a penalty factor; This refers to the number of movable antennas; Minimum distance; For movable antennas In the time slot Location; For movable antennas In the time slot The location.

[0065] After calculating the fitness value of each particle, the particles... The individual optimal position and the global optimal position of the particle population are updated. The higher the fitness value, the better the position. Fitness value and particle Compare the fitness value of the individual at its optimal position in the previous iteration. If the particle... The fitness value is greater than that of the particle The fitness value of the individual at its optimal position in the previous iteration will then determine the particle's fitness value. Position updated to particle At the optimal position of the individual in the current iteration , to particles The fitness value is updated to the particle At the optimal position of the individual in the current iteration The fitness value is set; otherwise, it remains unchanged, and the particle is... The optimal position of the individual in the previous iteration is updated to that of the particle. At the optimal position of the individual in the current iteration , to particles The fitness value of the individual at the optimal position in the previous iteration is updated to the particle's fitness value. At the optimal position of the individual in the current iteration The fitness value is then used. Similarly, the particle with the highest fitness value in the particle population is taken as the optimal particle. The fitness value of the optimal particle is compared with the fitness value of the global optimal position in the previous iteration. If the fitness value of the optimal particle is greater than the fitness value of the global optimal position in the previous iteration, then the position of the optimal particle is updated to the global optimal position of the current iteration. Update the fitness value of the best particle to the global best position of the current iteration. If the fitness value is not specified, then the fitness value is updated; otherwise, it remains unchanged, and the global optimum position from the previous iteration is updated to the global optimum position from the current iteration. Update the fitness value of the global optimum position from the previous iteration to the global optimum position of the current iteration. The fitness value.

[0066] For particles In its first The speed of the next iteration is The location is ,particle Based on its own optimal position The global optimal position of the population (i.e., the particle swarm). Update its speed and position: ; in, For particles In the The speed of the next iteration (current iteration); For the first The inertia weight of the next iteration (the previous iteration); For particles In the The speed of each iteration; As the first learning factor; The first random number, ; For particles In the The optimal position of the individual in the next iteration; For particles In the The position of the next iteration; As the second learning factor; The second random number, ; For the group in the first The global optimal position in the next iteration; For particles In the The position of the next iteration.

[0067] When a particle's position exceeds the boundary, i.e., when the particle's position goes beyond the search space (i.e., the area where the antenna is placed), a boundary reflection strategy is employed: the particle's position is reflected back into the search space, that is, the particle's position components are projected onto the corresponding boundaries of the search space. Specifically, if the x-coordinate of a particle's position component exceeds the left boundary of the search space (the minimum x-coordinate of the search space), then the x-coordinate of that particle's position component is placed at the left boundary of the search space, which is equivalent to making the x-coordinate of that particle's position component equal to the minimum x-coordinate of the search space. If the x-coordinate of a particle's position component exceeds the right boundary of the search space (the maximum x-coordinate of the search space), then the x-coordinate of that particle's position component is placed at the right boundary of the search space, which is equivalent to making the x-coordinate of that particle's position component equal to the minimum x-coordinate of the search space. If the y-coordinate of a particle's position component exceeds the upper boundary of the search space (the maximum value of the y-coordinate), then the y-coordinate of that particle's position component is placed at the upper boundary of the search space, which is equivalent to making the y-coordinate of that particle's position component equal to the maximum value of the y-coordinate of the search space. If the y-coordinate of a particle's position component exceeds the lower boundary of the search space (the minimum value of the y-coordinate), then the y-coordinate of that particle's position component is placed at the lower boundary of the search space, which is equivalent to making the y-coordinate of that particle's position component equal to the minimum value of the y-coordinate of the search space. At the same time, the particle's velocity is attenuated in the reverse direction, that is, the particle's velocity component is halved in the reverse direction. This reflection boundary strategy can maintain population diversity and prevent particles from stagnating near the boundary.

[0068] Based on the above process, the specific steps of the PSO-WMMSE joint optimization algorithm are as follows: 1) Input: Particle population size Second maximum number of iterations Inertial weight Learning factor and Punishment factor ; 2) Output: Optimized movable antenna position and beamforming parameters and ; 3) Initialization: Randomly initialize the velocity and position of the particles within the search space; 4) Main loop time slot ( From 1 to ): 5) Number of iterations in the main loop ( From 1 to ): 6) For each particle ( From 1 to ): 7) Calculate the channel matrix ; 8) Execute the WMMSE algorithm to obtain the optimized beamforming parameters, and calculate the uplink and rate; 9) Calculate the penalty value and fitness value; 10) Update the individual's optimal position ; 11) End the particle cycle; 12) Update the global optimal position ; 13) Update particle velocity and position, and perform boundary processing (using the reflection boundary strategy). 14) End the iteration loop; 15) End the gap cycle.

[0069] The complexity of the PSO-WMMSE joint optimization algorithm is analyzed below.

[0070] In the PSO-WMMSE joint optimization algorithm proposed in this embodiment, the WMMSE algorithm is executed once for each particle during each PSO iteration within a given time period to calculate the fitness value. The main computational cost of a single WMMSE algorithm comes from the matrix inversion operation. Specifically, the complexity of updating the merged matrix is ​​O(n log n). The time complexity of updating the precoding matrix is ​​O(n). These operations will target This was conducted for individual ground users, taking into account... One time slot, second maximum number of iterations Particle population size and the first maximum number of iterations The overall computational complexity of the proposed PSO-WMMSE joint optimization algorithm can be expressed as: .

[0071] Based on the aforementioned PSO-WMMSE joint optimization algorithm, the joint optimization model is solved to obtain the joint optimization scheme, specifically including the following steps: (1) The joint optimization model is decomposed to obtain the movable antenna position optimization sub-model and beamforming parameter optimization sub-model for each time slot within the mission time. The movable antenna position optimization sub-model takes maximizing uplink sum and rate as the optimization objective and the movable antenna minimum distance constraint and movable antenna position constraint as the constraint conditions. The beamforming parameter optimization sub-model takes maximizing uplink sum and rate as the optimization objective and the transmit power constraint as the constraint conditions.

[0072] (2) For each time slot, solve the movable antenna position optimization sub-model and beamforming parameter optimization sub-model of the time slot respectively to obtain the movable antenna position vector and beamforming parameters of the time slot.

[0073] (3) Combine the movable antenna position vector and beamforming parameters of each time slot to obtain a joint optimization scheme.

[0074] Specifically, the movable antenna position optimization sub-model and beamforming parameter optimization sub-model for the time slot are solved to obtain the movable antenna position vector and beamforming parameters for the time slot. The steps include: (1) For each particle in the particle population, the beamforming parameter optimization sub-model of the time slot is solved with the particle position as input to obtain the particle beamforming parameter. The fitness value of the particle is calculated based on the movable antenna position optimization sub-model of the time slot with the particle position and the particle beamforming parameter as input. The particle position represents a value of the movable antenna position vector.

[0075] Solving the beamforming parameter optimization sub-model for the time slot involves specifically using the WMMSE algorithm to solve the beamforming parameter optimization sub-model for the time slot.

[0076] The WMMSE algorithm is used to solve the beamforming parameter optimization sub-model for time slots. Specifically, this involves: transforming the beamforming parameter optimization sub-model to obtain a first transformed sub-model; introducing an auxiliary weight matrix into the first transformed sub-model to obtain a second transformed sub-model; simplifying the second transformed sub-model into a first simplified sub-model, a second simplified sub-model, and a third simplified sub-model; using the auxiliary weight matrix and precoding matrix from the previous iteration as input, solving the first simplified sub-model to obtain the merge matrix for the current iteration; and using the merge matrix for the current iteration and the precoding matrix from the previous iteration as input, solving the second simplified sub-model to obtain the auxiliary weight matrix for the current iteration. The algorithm iterates through the following steps: First, it uses the merge matrix and auxiliary weight matrix of the current iteration as input to solve the third simplified sub-model, obtaining the precoding matrix of the current iteration. Second, it checks if the first maximum iteration count has been reached. If yes, it uses the merge matrix and precoding matrix of the current iteration as the optimized merge matrix and precoding matrix to obtain the beamforming parameters. If no, it uses the merge matrix, auxiliary weight matrix, and precoding matrix of the current iteration as the merge matrix, auxiliary weight matrix, and precoding matrix of the previous iteration, increments the current iteration count by 1, and returns to the step of "using the auxiliary weight matrix and precoding matrix of the previous iteration as input to solve the first simplified sub-model, obtaining the merge matrix of the current iteration."

[0077] In the first iteration, the merge matrix, auxiliary weight matrix and precoding matrix from the previous iteration are all randomly assigned values.

[0078] (2) Based on the fitness value of each particle in the particle population, determine the individual optimal position of each particle and the global optimal position of the particle population.

[0079] (3) Determine whether the iteration termination condition has been met; if yes, determine the global optimal position of the particle population as the movable antenna position vector of the time slot, use the movable antenna position vector of the time slot as input, solve the beamforming parameter optimization sub-model of the time slot, and obtain the beamforming parameters of the time slot; if no, update the particle population, obtain the updated particle population, use the updated particle population as the particle population for the next iteration, and return to the step of "for each particle in the particle population, use the position of the particle as input, solve the beamforming parameter optimization sub-model of the time slot, and obtain the beamforming parameters of the particle".

[0080] The particle population is updated to obtain the updated particle population. Specifically, this includes updating the velocity and position of each particle in the particle population to obtain the updated velocity and position of each particle in the particle population, and using the updated velocity and position of each particle in the particle population as the velocity and position of each particle in the updated particle population to obtain the updated particle population.

[0081] The process involves updating the velocity and position of each particle in the particle swarm to obtain updated velocity and position for each particle. Specifically, this includes: using a particle swarm optimization algorithm to update the velocity and position of each particle in the particle swarm to obtain initial updated velocity and initial updated position for each particle; for each particle in the particle swarm, if the initial updated position of the particle is not within the movable antenna placement area and does not meet the movable antenna position constraint, then the initial updated velocity of the particle is reduced by a preset percentage (specifically 50%, or other values ​​can be selected according to user needs), and the initial updated position of the particle is adjusted to be on the boundary of the movable antenna placement area, thus obtaining the updated velocity and updated position of the particle; otherwise, the initial updated velocity and initial updated position of the particle are used as the updated velocity and updated position of the particle, thus obtaining the updated velocity and updated position for each particle in the particle swarm.

[0082] The iteration termination condition can be reaching the second maximum number of iterations.

[0083] This embodiment provides a joint optimization method for MU-MIMO communication of UAVs enabled by movable antennas. The UAV equipped with a planar movable antenna array serves as an airborne base station, serving multiple ground users equipped with uniform planar arrays within a limited mission time. For this communication scenario, a joint optimization model is first proposed. By optimizing the movable antenna position, merging matrix, and precoding matrix in all time slots, the average uplink sum and rate of the system's uplink transmission are maximized. Then, for the non-convex problem solved by the joint optimization model, a particle swarm optimization-weighted minimum mean square error (PSO-WMMSE) joint optimization algorithm is designed. The inner layer uses the WMMSE algorithm to iteratively solve the closed-form solution of the beamforming parameters given the movable antenna position, while the outer layer uses the PSO algorithm to search for the optimal movable antenna position, thereby improving communication performance.

[0084] The performance of the joint optimization method in this embodiment will be verified through simulation.

[0085] The simulation scenario considered in this embodiment is as follows: Figure 5 As shown, the drone is in The task is executed within a time slot, and the task time is... The drone flies along a horizontal sine trajectory from the starting point (0m, 0m) to the ending point (200m, 0m) in seconds, with a fixed altitude. For 4 ground users (i.e.) Figure 5 The drone is equipped with communication services provided to users 1, 2, 3, and 4. A movable antenna, the movable antenna can be... Within the area, the minimum distance between antennas can move freely. The locations of the ground users are set at (30m, 30m), (170m, 30m), (60m, 150m), and (140m, 150m), respectively. Each ground user is equipped with [equipment / facilities]. A uniform planar array of antennas, carrier frequency noise power PSO algorithm configures particle population size Second maximum number of iterations The WWWSE algorithm is configured with a first maximum number of iterations. .

[0086] Baseline methods include UPA+WMMSE and UPA+FB (Fixed Beamforming). UPA refers to a uniform antenna array composed of antennas with fixed positions that is equipped on a UAV.

[0087] Figure 6 Shown in and At that time, the uplink and rate of the three methods in each time slot (in Figure 6 The uplink sum rate (hereinafter referred to as the sum rate) changes. It can be seen that the joint optimization method proposed in this embodiment outperforms the baseline method in all time slots, indicating that deploying a movable antenna can effectively improve communication performance. It is worth noting that the uplink sum rate of the joint optimization method proposed in this embodiment continuously increases in the middle time slots, while the uplink sum rate of the baseline method decreases. This is because the UAV is close to the user cluster, resulting in lower path loss, but the channel is highly correlated, and multi-user interference is severe. The baseline method cannot effectively separate user channels, while the movable antenna can reduce channel correlation by optimizing antenna position.

[0088] Figure 7 The impact of maximum transmit power on the three methods was evaluated. The range is -10 to 20 dBm, and the number of movable antennas of the drone. Let's set it to 4. It can be seen that the average uplink speed and rate of all methods (in...) Figure 7 Both the average and rate (hereinafter referred to as average and rate) increase with the increase of maximum transmit power, and the joint optimization method proposed in this embodiment achieves a significant performance improvement compared with the baseline method. For example, at a maximum transmit power of 20 dBm, the joint optimization method proposed in this embodiment achieves a performance improvement of approximately 30.49% and 66.10% compared with UPA+WMMSE and UPA+FB, respectively. The performance improvement becomes more significant with the increase of maximum transmit power, indicating that the spatial freedom brought by the reconfiguration of the antenna position by the movable antenna becomes more valuable.

[0089] Figure 8 The number of movable antennas of the drone was evaluated (in Figure 8 The impact of the number of UAV antennas (referred to as the number of UAV antennas in Chinese) on the three methods. It can be seen that the average uplink speed and rate of all methods (in) Figure 8 Both the average and rate (hereinafter referred to as average and rate) increase with the increase of the number of movable antennas, and the joint optimization method proposed in this embodiment consistently outperforms the baseline method. When the number of movable antennas is 4, the performance of the joint optimization method proposed in this embodiment is improved by approximately 39.74% and 183.70% compared with UPA+WMMSE and UPA+FB, respectively. Furthermore, when the number of movable antennas is only 4, the performance of the joint optimization method proposed in this embodiment is comparable to the UPA+WMMSE scheme equipped with 8 antennas. This result shows that the joint optimization method proposed in this embodiment fully utilizes spatial degrees of freedom by optimizing antenna positions, thereby achieving communication rate performance comparable to traditional schemes with fewer antenna configurations.

[0090] Simulation results show that, compared with the two traditional schemes that use fixed antenna positions, the joint optimization method proposed in this embodiment achieves a significant improvement in average uplink speed and data rate.

[0091] This embodiment studies a MU-MIMO communication scenario in which a drone equipped with a planar movable antenna array provides communication services to multiple ground users.

[0092] (1) First, a mobile antenna-enabled UAV MU-MIMO communication scenario was established. For this communication scenario, a joint optimization model that maximizes the average uplink and rate was constructed by jointly designing the mobile antenna position, merging matrix and precoding matrix.

[0093] (2) Subsequently, a joint optimization algorithm of particle swarm optimization weighted minimum mean square error (PSO-WMMSE) was proposed. This joint optimization algorithm effectively decouples the beamforming parameter variables and the movable antenna position variables. Specifically, the WMMSE algorithm iteratively solves the inner beamforming parameter optimization sub-problem, while the PSO algorithm searches for the optimal movable antenna position in the outer layer.

[0094] (3) Finally, simulation results show that under different parameter settings (such as maximum transmit power and number of movable antennas), the joint optimization method proposed in this embodiment can achieve significant performance improvement compared with the traditional fixed antenna scheme. These results confirm the application value of movable antenna technology in UAV-assisted communication scenarios.

[0095] This application also provides an application scenario in which the aforementioned joint optimization method for MU-MIMO communication of UAVs enabled by a movable antenna is applied. Specifically, the joint optimization method for MU-MIMO communication of UAVs enabled by a movable antenna provided in this embodiment can be applied in wireless communication scenarios. Wireless communication scenarios include a joint optimization stage and a communication stage. The joint optimization stage is used to determine a joint optimization scheme, and the communication stage is used to perform communication based on the joint optimization scheme. The joint optimization method for MU-MIMO communication of UAVs enabled by a movable antenna provided in this embodiment belongs to the joint optimization stage.

[0096] Example 2 In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 9As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network. When executed by the processor, the computer program implements a joint optimization method for MU-MIMO communication of a UAV powered by a movable antenna.

[0097] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0098] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the joint optimization method for MU-MIMO communication of a UAV enabled by a movable antenna in Embodiment 1.

[0099] Example 3 In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the joint optimization method for mobile antenna-enabled UAV MU-MIMO communication in Embodiment 1.

[0100] Example 4 In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the joint optimization method for mobile antenna-enabled UAV MU-MIMO communication in Embodiment 1.

[0101] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Moreover, the collection, use and processing of the relevant data are carried out in compliance with the relevant data protection laws and policies of the country where the location is located, and with the authorization granted by the owner of the corresponding device.

[0102] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0103] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A joint optimization method for enabling MU-MIMO communication of unmanned aerial vehicles with a movable antenna, characterized in that, The joint optimization method for enabling UAV MU-MIMO communication with a movable antenna includes: A joint optimization model is constructed for a single UAV acting as an airborne base station to provide communication services to multiple ground users. The UAV flies along a preset trajectory during the mission time and is equipped with a planar movable antenna array, which includes multiple movable antennas. Each ground user is equipped with a uniform planar array. Multiple ground users send data to the UAV through the uniform planar array and the planar movable antenna array to achieve multi-input multi-output uplink communication. The joint optimization model aims to maximize the average uplink sum and rate, and uses transmit power constraints, minimum distance constraints of movable antennas, and position constraints of movable antennas as constraints to achieve joint optimization of the position of movable antennas and beamforming parameters. The beamforming parameters include a merging matrix and a precoding matrix. Solving the joint optimization model yields a joint optimization scheme. The joint optimization scheme includes the movable antenna position vector and beamforming parameters for each time slot within the mission time. The movable antenna position vector includes the position of each movable antenna.

2. The joint optimization method for MU-MIMO communication of UAVs empowered by a movable antenna according to claim 1, characterized in that, The joint optimization model is as follows: ； in, For time slots The movable antenna position vector; For ground users In the time slot The precoding matrix; For ground users In the time slot The merged matrix; The number of time slots; The number of ground users; For ground users In the time slot Uplink speed; For transmit power constraints; This is the maximum transmission power; Minimum distance constraint for movable antennas; For movable antennas In the time slot Location; For movable antennas In the time slot Location; Minimum distance; Position constraints for movable antennas; This is the area for placing a movable antenna.

3. The joint optimization method for MU-MIMO communication of UAVs empowered by a movable antenna according to claim 2, characterized in that, Ground users In the time slot The formula for calculating the uplink speed is: ； ； in, for OK The identity matrix of columns, This refers to the number of movable antennas; For ground users In the time slot Channel matrix to the UAV; For ground users In the time slot The noise interference received; For ground users In the time slot Channel matrix to the UAV; For ground users In the time slot The precoding matrix; Noise power; Ground users In the time slot The formula for calculating the channel matrix to the UAV is: ； ； ； ； ； in, It is a constant; For ground users In the time slot Distance to the drone; For ground users In the time slot Antenna array response with the drone; Antenna array response vector of a planar movable antenna array for a drone; For ground users The antenna array response vector of the equipped uniform planar array; The carrier wavelength; For ground users In the time slot To movable antenna The signal propagation phase difference; For movable antennas In the time slot The x-coordinate; For ground users In the time slot To the drone's pitch angle; For ground users In the time slot To the azimuth angle of the drone; For movable antennas In the time slot The y-coordinate; For drones in time slots to ground users The pitch angle; For drones in time slots to ground users The azimuth angle; This represents the number of antennas in the x-direction of a uniform planar array. This represents the number of antennas in the y-direction of a uniform planar array.

4. The joint optimization method for enabling UAV MU-MIMO communication with a movable antenna according to claim 1, characterized in that, Solving the joint optimization model yields the joint optimization scheme, which includes: The joint optimization model is decomposed to obtain the movable antenna position optimization sub-model and beamforming parameter optimization sub-model for each time slot within the mission time. The movable antenna position optimization sub-model takes maximizing uplink sum and rate as the optimization objective and the movable antenna minimum distance constraint and movable antenna position constraint as the constraint conditions. The beamforming parameter optimization sub-model takes maximizing uplink sum and rate as the optimization objective and the transmit power constraint as the constraint condition. For each time slot, the movable antenna position optimization sub-model and beamforming parameter optimization sub-model of the time slot are solved separately to obtain the movable antenna position vector and beamforming parameters of the time slot. The movable antenna position vector and beamforming parameters of each time slot are combined to obtain a joint optimization scheme.

5. The joint optimization method for MU-MIMO communication of UAVs empowered by a movable antenna according to claim 4, characterized in that, The movable antenna position optimization sub-model and beamforming parameter optimization sub-model for the time slot are solved separately to obtain the movable antenna position vector and beamforming parameters for the time slot, specifically including: For each particle in the particle swarm, the beamforming parameter optimization sub-model of the time slot is solved using the particle's position as input to obtain the particle's beamforming parameters. The fitness value of the particle is calculated based on the movable antenna position optimization sub-model of the time slot using the particle's position and the particle's beamforming parameters as input. The particle's position represents one value of the movable antenna position vector. Based on the fitness value of each particle in the particle swarm, determine the individual optimal position of each particle and the global optimal position of the particle swarm. Determine if the iteration termination condition has been met; if yes, determine the global optimal position of the particle swarm as the position vector of the movable antenna in the time slot, and use the position vector of the movable antenna in the time slot as input to solve the beamforming parameter optimization sub-model of the time slot to obtain the beamforming parameters of the time slot; if no, update the particle swarm to obtain the updated particle swarm, use the updated particle swarm as the particle swarm for the next iteration, and return to the step of "for each particle in the particle swarm, use the position of the particle as input to solve the beamforming parameter optimization sub-model of the time slot to obtain the beamforming parameters of the particle".

6. The joint optimization method for enabling UAV MU-MIMO communication with a movable antenna according to claim 5, characterized in that, Solving the beamforming parameter optimization sub-model for the time slot involves specifically using the WMMSE algorithm to solve the beamforming parameter optimization sub-model for the time slot.

7. The joint optimization method for enabling UAV MU-MIMO communication with a movable antenna according to claim 5, characterized in that, The formula for calculating fitness value is: ； ； in, For particles fitness value, For particles Location; The number of ground users; For particle-based Position and particles The beamforming parameters of the ground users are calculated In the time slot Uplink speed; For particles The penalty value; As a penalty factor; This refers to the number of movable antennas; Minimum distance; For movable antennas In the time slot Location; For movable antennas In the time slot Location; The particle population is updated to obtain the updated particle population. Specifically, this includes updating the velocity and position of each particle in the particle population to obtain the updated velocity and position of each particle in the particle population. The updated velocity and position of each particle in the particle population are used as the velocity and position of each particle in the updated particle population to obtain the updated particle population. Specifically, updating the velocity and position of each particle in the particle swarm to obtain the updated velocity and position of each particle in the particle swarm includes: using a particle swarm optimization algorithm to update the velocity and position of each particle in the particle swarm to obtain the initial updated velocity and initial updated position of each particle in the particle swarm; for each particle in the particle swarm, if the initial updated position of the particle is not within the movable antenna placement area and does not meet the movable antenna position constraint, then the initial updated velocity of the particle is reduced by a preset ratio, and the initial updated position of the particle is adjusted to be on the boundary of the movable antenna placement area, thus obtaining the updated velocity and updated position of the particle; otherwise, the initial updated velocity and initial updated position of the particle are used as the updated velocity and updated position of the particle, thus obtaining the updated velocity and updated position of each particle in the particle swarm.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes the computer program to implement the joint optimization method for MU-MIMO communication of a UAV enabled by a movable antenna as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the joint optimization method for enabling MU-MIMO communication of a UAV with a movable antenna as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the joint optimization method for enabling MU-MIMO communication of a UAV with a movable antenna as described in any one of claims 1-7.

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