Method and system for assisting ground user communication by multi-antenna unmanned aerial vehicle

Abstract

The invention relates to a method for assisting ground user communication by a multi-antenna unmanned aerial vehicle, belongs to the field of unmanned aerial vehicle assisted communication, and solves the limitation of a single-antenna unmanned aerial vehicle in the aspects of signal concentration and directional transmission. Comprising the following steps: constructing a multi-antenna unmanned aerial vehicle assisted multi-ground user communication system, and calculating a steering vector from an unmanned aerial vehicle to each ground user; calculating a channel gain from the unmanned aerial vehicle to each ground user based on the steering vector; calculating a communication rate between the unmanned aerial vehicle and each ground user based on the channel gain; constructing an unmanned aerial vehicle assisted ground user communication optimization model which takes maximization of the average communication rate between the unmanned aerial vehicle and multiple ground users as an optimization objective function and meets user scheduling constraints, unmanned aerial vehicle communication performance constraints, unmanned aerial vehicle transmitting power constraints and unmanned aerial vehicle flight trajectory constraints; and solving the unmanned aerial vehicle assisted ground user communication optimization model to obtain an optimal communication solution of the unmanned aerial vehicle and the multiple ground users. And the signal can accurately reach the ground user.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) assisted communication technology, and in particular to a method and system for multi-antenna UAV assisted ground user communication. Background Art

[0002] With the rapid development of modern communication technology and the increasingly diversified and demanding needs of ground users for communication services, traditional ground communication infrastructure has gradually exposed its limitations in certain scenarios. As an emerging aerial communication platform, Unmanned Aerial Vehicle (UAV) has great potential in assisting ground user communications due to its high mobility, flexible deployment characteristics, and ability to quickly respond to communication needs in different geographical areas. Compared with traditional ground base stations, UAVs are not restricted by fixed geographical locations and can be deployed in a short period of time to areas with weak communication coverage or sudden communication needs, such as remote mountainous areas, natural disaster sites, and temporary large-scale event venues. UAVs can hover at a specific height and build a communication link with ground users from the air, effectively filling the ground communication blind spots, expanding the communication coverage, and providing emergency communication guarantees for ground users or enhancing the quality of conventional communication services.

[0003] However, in the field of UAV-assisted ground user communications, the currently widely used single-antenna UAVs have some obvious limitations. Single-antenna UAVs are limited by only one antenna and cannot effectively concentrate and direct the signal, resulting in a more divergent state of signal propagation and serious energy loss. In this way, a large amount of energy is wasted during the propagation process, making it difficult to ensure that the signal reaches the target ground user accurately, and ultimately the signal strength actually received by the ground user is at a low level, seriously affecting the communication quality and stability. In multi-user scenarios, the disadvantages of single-antenna UAVs are more prominent. Faced with limited communication resources, there is a lack of sufficient capacity to flexibly and efficiently respond to the communication needs of multiple target ground users. Relying solely on a single antenna, it is impossible to build targeted and optimized communication links for different target ground users. These defects highlight the shortcomings of existing single-antenna UAV configurations in meeting the growing demand of ground users for communication quality and speed. Summary of the invention

[0004] In view of the above analysis, the embodiments of the present invention aim to provide a method and system for multi-antenna UAVs to assist ground user communications, so as to solve the technical problems that the existing methods of single-antenna UAVs have limitations in signal concentration and directional transmission, resulting in serious signal energy loss, making it difficult to ensure that the signal accurately reaches the target user, and affecting the communication quality and stability.

[0005] The purpose of the present invention is mainly achieved through the following technical solutions:

[0006] The present invention provides a method for multi-antenna unmanned aerial vehicle to assist ground user communication, comprising the following steps:

[0007] Step S1, constructing a simulation model of a multi-antenna UAV-assisted multi-ground user communication system, calculating a steering vector from the UAV to each of the ground users; calculating a channel gain from the UAV to each ground user based on the steering vector; and calculating a communication rate between the UAV and each ground user based on the channel gain;

[0008] Step S2: constructing a UAV-assisted ground user communication optimization model that takes maximizing the average communication rate between the UAV and multiple ground users as the optimization objective function, while satisfying user scheduling constraints, UAV communication performance constraints, UAV transmission power constraints, and UAV flight trajectory constraints;

[0009] Step S3: Solve the UAV-assisted ground user communication optimization model to obtain the optimal solution for communication between the UAV and multiple ground users.

[0010] Furthermore, the UAV is equipped with a uniform planar antenna array, which is deployed parallel to the ground. The total number of antennas and the distance between antenna units of the uniform planar antenna array are divided along the x-axis and y-axis of the three-dimensional Cartesian coordinate system to obtain the total number of antenna arrays M=M x ×M y and the distance d between the antennas along the coordinate system axis x =d y =λ / 2;

[0011] Among them, M x 、M y are the number of antennas arranged on the x-axis and y-axis respectively; d x ,d y are the spacing of the antennas along the x-axis and y-axis respectively; λ is the carrier wavelength.

[0012] Furthermore, based on M x 、M y and d x ,d y Calculate the steering vector α(l(n),l from the UAV to the kth ground user k ), calculated as follows:

[0013]

[0014] Where k = 1, 2, ..., K, K is the number of ground users; l(n) is the position of the UAV in the nth time slot, l k is the position of the kth ground user, θ(l(n),l k )、Φ(l(n),l k) are the AoD elevation angle and AoD azimuth angle when the UAV transmits the signal to the kth ground user.

[0015] Furthermore, based on α(l(n),l k ) Calculate the channel gain h from the UAV to the kth ground user k,com (l(n),l k ), calculated as follows:

[0016]

[0017] Where n = 1, 2, ... N, N is the number of time slots in the UAV flight cycle; G t is the antenna gain of the UAV transmitter, G b is the antenna gain received by the ground user, d(l(n),l k )=||l(n)-l k || represents the Euclidean distance between the UAV and the kth ground user.

[0018] Furthermore, under unit bandwidth, based on h k,com (l(n),l k ) Calculate the communication rate between the UAV and the kth ground user The calculation is as follows:

[0019]

[0020] Among them, c k (n) is the user scheduling of the UAV in the nth time slot, is the communication beamforming vector of the UAV in the nth time slot, is the noise that obeys Gaussian random distribution, H is the channel gain h k,com (l(n),l k )'s conjugate transpose; c k (n) is a binary discrete variable;

[0021] When the UAV transmits a communication beam in time slot n to perform a communication task for the kth ground user, c k (n)=1, otherwise c k (n)=0.

[0022] Furthermore, the UAV-assisted ground user communication optimization model is as follows:

[0023]

[0024] in, is the communication rate between the UAV and the kth ground user; K is the number of ground users; n=1,2,...N, N is the number of time slots in the UAV flight cycle; C, W, L are the user scheduling to be optimized, the beamforming vector transmitted by the UAV, and the UAV trajectory variable, respectively; C1 and C2 represent user scheduling constraints, C3 represents UAV communication performance constraints, is the preset communication rate threshold between the UAV and the base station, C4 represents the UAV transmission power constraint, C5 and C6 represent the UAV flight trajectory constraints, and P max is the maximum transmission power of the UAV, D max is the maximum flight distance of the UAV in each time slot, l I is the starting position of the drone.

[0025] Furthermore, the step S3 comprises:

[0026] The UAV-assisted ground user communication optimization model includes three nonlinear coupling variables: user scheduling, beamforming vectors emitted by the UAV, and UAV trajectory. The model is solved as a non-convex optimization problem.

[0027] Decomposing the non-convex optimization problem into three convex optimization sub-problems of user scheduling, beamforming vectors emitted by the UAV, and UAV trajectory;

[0028] Alternately iterate and optimize three convex optimization sub-problems to gradually approach the global optimal solution;

[0029] When the objective function converges, the global optimal solution obtained is the optimal solution for communication between the UAV and multiple ground users; wherein the global optimal solution includes user scheduling, beamforming vectors emitted by the UAV, and UAV trajectories.

[0030] Furthermore, the non-convex optimization problem includes user scheduling Beamforming vectors transmitted by drones Drone tracks Three nonlinear coupled variables.

[0031] Furthermore, the three convex optimization sub-problems of user scheduling, the beamforming vector of the UAV transmission and the trajectory of the UAV are alternately optimized to gradually approach the global optimal solution, including:

[0032] In the mth iteration, when solving the user scheduling, given the preset UAV-transmitted beamforming vector and the initial UAV trajectory, the UAV-assisted ground user communication optimization model is solved to obtain the user scheduling C (m) ;

[0033] When solving the beamforming vector emitted by the UAV, based on C(m) , the initial UAV trajectory, solving the UAV-assisted ground user communication optimization model to obtain the beamforming vector W (m) ;

[0034] When solving the trajectory of the UAV, based on W (m) , C (m) , solve the UAV-assisted ground user communication optimization model to obtain L (m) ;

[0035] The solution is solved in an alternating cycle until the optimization objective function converges, and the user scheduling, the beamforming vector emitted by the UAV, and the trajectory of the UAV in the current round are the global optimal solution.

[0036] The present invention also provides a system for multi-antenna unmanned aerial vehicle to assist ground user communication, comprising the following modules:

[0037] The simulation model building module M1 is used to build a simulation model of a multi-antenna UAV-assisted multi-ground user communication system; the simulation model includes a multi-antenna UAV model and multiple ground user models; the multi-antenna UAV is provided with a uniform planar antenna array, and when performing a circular flight mission, transmits communication beams to multiple ground users based on the communication optimal solution; the ground user is used to send a request to the UAV based on the received directional beam signal;

[0038] The communication link analysis module M2 is used to construct a multi-antenna UAV-assisted multi-ground user communication system, calculate the steering vector from the UAV to each ground user; calculate the channel gain from the UAV to each ground user based on the steering vector; calculate the communication rate between the UAV and each ground user based on the channel gain;

[0039] The optimization model building module M3 is used to build a UAV-assisted ground user communication optimization model that takes maximizing the average communication rate between the UAV and multiple ground users as the optimization objective function, while satisfying user scheduling constraints, UAV communication performance constraints, UAV transmission power constraints, and UAV flight trajectory constraints;

[0040] The model solving module M4 is used to solve the UAV-assisted ground user communication optimization model to obtain the optimal solution for communication between the UAV and multiple ground users.

[0041] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0042] 1. The present invention comprehensively considers user scheduling, the communication beamforming vector emitted by the UAV, and the flight trajectory of the UAV, and realizes the joint optimization of the three. Compared with the traditional UAV-assisted communication method, the present invention effectively improves the average communication rate of the system by combining beamforming technology, provides high-quality and efficient communication services for ground users, and has strong application value;

[0043] 2. Through multi-antenna technology, drones can transmit signals more accurately and directionally, reduce energy loss, and improve signal strength and communication quality, especially in multi-user environments. Multi-antenna drones can build optimized communication links for different users, flexibly and efficiently meet the communication needs of multiple ground users, and improve the overall service capabilities of the system.

[0044] 3. By constructing and solving the optimization model, the optimal configuration of user scheduling, beamforming and UAV trajectory is achieved, the average communication rate is maximized, and the utilization efficiency of communication resources is improved.

[0045] In the present invention, the above-mentioned technical solutions can also be combined with each other to achieve more preferred combination solutions. Other features and advantages of the present invention will be described in the subsequent description, and some advantages can become obvious from the description, or can be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like components throughout the drawings.

[0047] Figure 1 This is a flow chart of a method for multi-antenna UAV to assist ground user communication in an embodiment of the present invention;

[0048] Figure 2 Schematic diagram of a simulation model of a multi-antenna UAV-assisted ground user communication system in an embodiment of the present invention;

[0049] Figure 3 A schematic diagram of trajectory optimization of a multi-antenna UAV in an embodiment of the present invention;

[0050] Figure 4 This is a schematic diagram of the multi-antenna UAV dispatching ground users in an embodiment of the present invention;

[0051] Figure 5 Schematic diagram of the plane beam of the signal-to-noise ratio at the user receiving position of the UAV in a randomly selected time slot in an embodiment of the present invention;

[0052] Figure 6A three-dimensional beam schematic diagram of the signal-to-noise ratio at the user receiving position of the UAV in a randomly selected time slot in an embodiment of the present invention;

[0053] Figure 7 It is a schematic diagram of comparing the average communication rate of the system of the present invention and the preset trajectory solution under different maximum transmission powers of the UAV in the embodiment of the present invention;

[0054] Figure 8 This is a module schematic diagram of a simulation model of a multi-antenna UAV-assisted ground user communication system in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0056] The emergence of beamforming technology provides an effective way to solve the above technical problems. Beamforming technology is based on antenna arrays. By accurately adjusting the phase and amplitude of the signal transmitted by each antenna unit, the transmitted electromagnetic waves can form a specific radiation pattern in space, and concentrate the signal energy on the target ground user. In the scenario of drone-assisted ground user communication, beamforming technology can significantly enhance the performance of the communication link between drones and ground users. By accurately aiming a narrower communication beam at the target ground user, the diffusion and waste of the signal in space is effectively reduced, and the strength of the signal reaching the ground user end is increased, thereby improving the signal-to-noise ratio and reliability, solving the problems of communication quality and low efficiency of the above-mentioned single antenna of the drone, and providing a better solution for drone-assisted ground user communications.

[0057] The present invention builds a UAV-assisted communication network based on the growing demand of ground users for communication quality and rate, equips UAVs with multi-antenna arrays, and uses beamforming technology to enhance signal strength and anti-interference. By jointly optimizing ground user scheduling, beamforming vectors emitted by UAVs, and UAV flight trajectories, accurate coverage of the communication network is achieved, thereby effectively improving the average communication rate to meet ground users' expectations for communication quality in various complex scenarios, providing strong support for the in-depth application of UAV communication technology.

[0058] Embodiment 1:

[0059] A specific embodiment of the present invention, as Figure 1 As shown, a method for multi-antenna UAV to assist ground user communication is disclosed, comprising the following steps:

[0060] Step S1, constructing a simulation model of a multi-antenna UAV-assisted multi-ground user communication system, calculating a steering vector from the UAV to each of the ground users; calculating a channel gain from the UAV to each ground user based on the steering vector; and calculating a communication rate between the UAV and each ground user based on the channel gain;

[0061] Step S2: constructing a UAV-assisted ground user communication optimization model that takes maximizing the average communication rate between the UAV and multiple ground users as the optimization objective function, while satisfying user scheduling constraints, UAV communication performance constraints, UAV transmission power constraints, and UAV flight trajectory constraints;

[0062] Step S3: Solve the UAV-assisted ground user communication optimization model to obtain the optimal solution for communication between the UAV and multiple ground users.

[0063] Step S1, specifically.

[0064] (1) Construct a multi-antenna UAV-assisted multi-ground user communication system.

[0065] Based on the needs of actual application scenarios, a simulation model of a system in which multi-antenna UAVs assist ground user communications is constructed, covering the channel model of the communication link and the UAV flight motion model. By adjusting the radiation direction of the antenna array, the signal energy can be focused and transmitted directionally to the target ground user end. At the same time, combined with the optimized design of the UAV flight trajectory, it is ensured that ground users in different geographical locations can receive stable communication signals.

[0066] The multi-antenna UAV-assisted multi-ground user communication system includes a UAV and multiple target ground users distributed in different locations. The UAV is equipped with a uniform planar antenna array (UPA) and acts as an aerial information collection and communication platform.

[0067] When performing a circular flight mission, the UAV transmits a communication beam to a single-antenna user on the ground at a constant altitude, thereby collecting and transmitting information from the ground user. To ensure that the position of the UAV in each time slot can be regarded as relatively stationary, its flight cycle is divided into N equal-length time slots.

[0068] The communication channel model is specifically as follows: the UAV is equipped with a uniform planar antenna array, which is deployed parallel to the ground. The total number of antennas in the uniform planar antenna array and the distance between antenna units are divided along the x-axis and y-axis of the three-dimensional Cartesian coordinate system, and the total number of antenna arrays M = M x ×M y and the distance d between the antennas along the coordinate system axis x =d y =λ / 2;

[0069] Among them, M x 、M y are the number of antennas arranged on the x-axis and y-axis respectively; d x ,d y are the spacing of the antennas along the x-axis and y-axis respectively; λ is the carrier wavelength.

[0070] (2) Calculate the steering vector α(l(n),l) from the drone to each of the ground users k ).

[0071] Based on M x 、M y and d x ,d y Calculate the steering vector α(l(n),l from the UAV to the kth ground user k ), calculated as follows:

[0072]

[0073] Where k = 1, 2, ..., K, K is the number of ground users; l(n) is the number of drones in the nth time slot

[0074] The location of k is the position of the kth ground user, θ(l(n),l k )、Φ(l(n),l k ) are the AoD elevation angle and AoD azimuth angle when the UAV transmits the signal to the kth ground user.

[0075] j is the imaginary unit; T is the transpose of the matrix; θ(l(n),l k ) is the Angle of Departure (AoD) elevation angle of the UAV’s transmitted signal to the kth ground user, reflecting the change of the signal’s transmission angle in the vertical direction relative to the UAV; Φ(l(n),l k ) is the AoD azimuth of the UAV’s transmitted signal to the kth ground user, describing the transmitting pointing angle of the signal in the horizontal direction relative to the UAV.

[0076] (3) Calculate the channel gain h from the drone to each ground user based on the steering vector k,com (l(n),l k ).

[0077] Considering that the downlink communication link between the UAV and the user is a line-of-sight channel, the free space fading model is used to simulate the line-of-sight channel gain.

[0078] Based on α(l(n),l k ) Calculate the channel gain h from the UAV to the kth ground userk,com (l(n),l k ), calculated as follows:

[0079]

[0080] Where n = 1, 2, ... N, N is the number of time slots in the UAV flight cycle; G t is the antenna gain of the UAV transmitter, G b is the antenna gain received by the ground user, d(l(n),l k )=||l(n)-l k || represents the Euclidean distance between the UAV and the kth ground user.

[0081] (4) Calculating the communication rate between the UAV and each ground user based on the channel gain

[0082] Under unit bandwidth, based on h k,com (l(n),l k ) Calculate the communication rate between the UAV and the kth ground user The calculation is as follows:

[0083]

[0084] Among them, c k (n) is the user scheduling of the UAV in the nth time slot, is the communication beamforming vector of the UAV in the nth time slot, is the noise that obeys Gaussian random distribution, H is the channel gain h k,com (l(n),l k )'s conjugate transpose; c k (n) is a binary discrete variable;

[0085] When the UAV transmits a communication beam in time slot n to perform a communication task for the kth ground user, c k (n)=1, otherwise c k (n)=0.

[0086] c k (n) is a binary discrete variable used to describe the dispatch of UAVs to ground users.

[0087] The noise in the communication rate is the noise at the ground user receiving point, which has a mean of 0 and a variance of Gaussian white noise, describes the power distribution of the noise signal.

[0088] The motion model of the drone includes kinematic constraints and flight position constraints. Considering the physical characteristics of the drone and the flight safety requirements, the flight speed of the drone will always be controlled at the preset maximum speed v during its flight. max the following.

[0089] For example, the preset maximum speed v max It is 40m / s.

[0090] The drone has the following distance constraints in each time slot:

[0091]

[0092] Among them, l(n) is the coordinate of the UAV at the nth time slot, l(n-1) is the coordinate of the UAV at the n-1th time slot, and D max =v max δ T is the maximum flight distance of the UAV in each time slot, δ T is the length of each time slot in seconds;

[0093] When the UAV performs ground user communication services, in order to ensure that its flight trajectory in a specific area has clear starting and ending settings, the following flight constraints must be followed:

[0094] l(1)=l(N)=l I Formula (5)

[0095] Among them, l I represents the starting position of the UAV, and N is the total number of time slots in the UAV flight cycle. The physical meaning of formula (5) is that the UAV flies back to the starting position at the end of the flight mission.

[0096] The power constraint of the UAV is as follows: P(n) is defined as the average power transmitted by the UAV in the nth time slot, then:

[0097]

[0098] Wherein, N is a set including all time slot numbers.

[0099] Since the average transmission power of the drone is limited by the maximum power, the following constraints are met:

[0100] P(n)≤P max Formula (7)

[0101] Among them, P max Indicates the maximum transmission power of the drone, which is the parameter of the drone itself.

[0102] The function of step S1 is to establish a multi-antenna UAV-assisted multi-ground user communication system between a multi-antenna UAV and multiple ground users, and calculate the key parameters of the communication link, including the steering vector α(l(n),l between the UAV and each of the ground users. k ), channel gain h k,com (l(n),l k ) and communication rate Provide basic support for subsequent communication optimization.

[0103] Step S2, specifically.

[0104] Design an optimization problem to maximize the average communication rate of the UAV while satisfying the user scheduling constraints, transmission power constraints, user minimum communication rate constraints, and UAV flight kinematic constraints;

[0105] In order to maximize the average communication rate of the UAV, the UAV-assisted ground user communication optimization model is as follows:

[0106]

[0107] in, is the communication rate between the UAV and the kth ground user; K is the number of ground users; n=1,2,...N, N is the number of time slots in the UAV flight cycle; C, W, L are the user scheduling to be optimized, the beamforming vector transmitted by the UAV, and the UAV trajectory variable, respectively;

[0108]

[0109] C1 and C2 represent user scheduling constraints, C3 represents UAV communication performance constraints, is the preset communication rate threshold between the UAV and the base station, C4 represents the UAV transmission power constraint, C5 and C6 represent the UAV flight trajectory constraints, and P max is the maximum transmission power of the UAV, D max is the maximum flight distance of the UAV in each time slot, l I is the starting position of the drone.

[0110] C1 and C2 user scheduling constraints indicate that the drone focuses on providing services to a single user in each time slot; C3 drone communication performance constraint requires that the communication rate between the drone and the base station must exceed a preset threshold Ensure the quality of signal data transmission; C4 UAV transmission power constraint requires that the UAV's transmission power does not exceed the upper limit of the transmission power to ensure equipment safety; C5 and C6 UAV flight trajectory constraints require the starting point and the end point to coincide to ensure the safety and stability of the UAV's flight.

[0111] Exemplarily, the preset communication rate threshold Set to 16bps / Hz.

[0112] The purpose of step S2 is to design an optimization problem, which aims to maximize the average communication rate between the UAV and the ground users by adjusting the user scheduling, beamforming vectors and UAV trajectory variables, while ensuring that the user scheduling, communication performance, transmission power and flight trajectory constraints are met to achieve efficient, stable and safe UAV-assisted communication.

[0113] The step S3 comprises:

[0114] The UAV-assisted ground user communication optimization model includes three nonlinear coupling variables: user scheduling, beamforming vectors emitted by the UAV, and UAV trajectory. The model is solved as a non-convex optimization problem.

[0115] Decomposing the non-convex optimization problem into three convex optimization sub-problems of user scheduling, beamforming vectors emitted by the UAV, and UAV trajectory;

[0116] Alternately iterate and optimize three convex optimization sub-problems to gradually approach the global optimal solution;

[0117] When the objective function converges, the global optimal solution obtained is the optimal solution for communication between the UAV and multiple ground users; wherein the global optimal solution includes user scheduling, beamforming vectors emitted by the UAV, and UAV trajectories.

[0118] This optimization problem is a mixed integer non-convex optimization problem and is difficult to solve directly.

[0119] The non-convex optimization problem includes user scheduling Beamforming vectors transmitted by drones Drone tracks Three nonlinear coupled variables.

[0120] The non-convex optimization problem is decomposed into three sub-problems, namely, the user scheduling optimization problem, the beamforming vector optimization problem of UAV transmission and the UAV trajectory optimization problem. In the process of solving each sub-problem, the Successive Convex Approximation (SCA) method and the Semidefinite Relaxation (SDR) technique are used to transform the non-convex problem into a convex problem.

[0121] The details are as follows:

[0122] (1) For the user scheduling optimization subproblem, the binary scheduling variables are relaxed into continuous variables with a value range between 0 and 1, so that the problem is transformed into a convex optimization problem. Then, the CVX toolbox in Matlab can be used to directly solve the transformed convex problem.

[0123] (2) For the subproblem of optimizing the beamforming vector emitted by the UAV, the SDR technology is used to transform the beamforming vector into a semi-positive definite matrix of rank 1. At this time, the problem is initially transformed into a convex problem. Then, the relaxed rank 1 constraint with strong non-convexity is added to the objective function as a penalty factor, so that it can be directly solved using the CVX toolbox;

[0124] (3) For the UAV trajectory optimization subproblem, the SCA method is used to perform Taylor first-order expansion on the non-convex parts of the objective function and constraints, and the problem is converted into a convex problem that can be directly solved by CVX.

[0125] After solving each sub-problem separately, an alternating optimization strategy is adopted to switch optimization between different sub-problems through cyclic iteration, so that the solutions of the three sub-problems can influence and evolve co-evolved with each other, gradually approaching the global optimal solution, thereby achieving a joint optimization solution to the entire complex optimization problem.

[0126] The three convex optimization sub-problems of alternately optimizing user scheduling, the beamforming vectors emitted by the UAV, and the trajectory of the UAV gradually approach the global optimal solution, including:

[0127] In the mth iteration, when solving the user scheduling, given the preset UAV-transmitted beamforming vector and the initial UAV trajectory, the UAV-assisted ground user communication optimization model is solved to obtain the user scheduling C (m) ;

[0128] When solving the beamforming vector emitted by the UAV, based on C (m) , the initial UAV trajectory, solving the UAV-assisted ground user communication optimization model to obtain the beamforming vector W (m) ;

[0129] When solving the trajectory of the UAV, based on W (m) , C (m) , solve the UAV-assisted ground user communication optimization model to obtain L (m) ;

[0130] The solution is solved in an alternating cycle until the optimization objective function converges, and the user scheduling, the beamforming vector emitted by the UAV, and the trajectory of the UAV in the current round are the global optimal solution.

[0131] Among them, the mth round is the iteration round, m≥1 round, and it is the iteration round before the objective function converges.

[0132] The optimization steps are explained in detail as follows:

[0133] There are three nonlinear coupling variables in the original optimization problem (user scheduling Beamforming vectors transmitted by drones Drone tracks Therefore, the optimization problem is a non-convex problem. To solve this problem, the original problem is decomposed into three sub-problems, and each sub-problem is solved iteratively until the objective function value converges;

[0134] Method for determining convergence of objective function: When the objective function in the current number of iterations minus the objective function in the previous number of iterations is less than a given threshold or reaches the set maximum number of iterations, it can be considered that the objective value remains basically unchanged and has reached the optimal value. At this time, the solution (C, W, L) is an approximate global optimal solution.

[0135] Exemplarily, the maximum number of iterations is set to 20 times.

[0136] It should be noted that in each round of solving, each sub-problem is solved in sequence, including the user scheduling problem, the beamforming vector problem of the drone launch, and the drone trajectory optimization problem, and the current approximate optimal solution of the variables in each sub-problem is found. This solution is used in solving the next sub-problem, and finally the purpose of joint solution is achieved.

[0137] For example, in the mth iteration, when solving the user scheduling C, W and L are first given. Here, the given means that the W value of all time slots is pre-set during the simulation. L is the position where the circle is divided into N time slots on the initial circular trajectory with a radius r set to r = 250m, which is the initial trajectory of the drone (given trajectory L). The C obtained after solving (m) ;

[0138] Similarly, when solving the beamforming vector W emitted by the drone, given C (m) , L, where C is replaced by the solution C obtained above (m) , L is still the initial trajectory (because the trajectory subproblem has not been solved yet, it will not be replaced yet), and the solved W (m) ;

[0139] When solving the UAV trajectory L, given W (m) , C (m) Solve and finally get C (m) , W (m) , L (m) , these three values ​​are the solutions of the current round, and then the next round begins, still following the solution order of C, W, and L until the maximum iteration round is reached and finally the global optimal solution is approached.

[0140] In step S3, the original problem is first divided into the user scheduling optimization problem, the UAV emission beamforming vector optimization problem and the non-convex problem of UAV trajectory optimization. The non-convex problem is converted into a convex problem that can be directly solved by the CVX toolbox, and finally the global optimal solution of the original optimization problem is approached through an alternating iterative optimization algorithm.

[0141] The optimal solution consists of the following three decision variables:

[0142] (1) User scheduling: which user should be selected for communication in each time slot;

[0143] (2) UAV transmission beamforming vector: The UAV transmission beamforming vector is designed for each time slot to ensure that the corresponding ground user can receive the strongest signal;

[0144] (3) Drone trajectory: The flight path of the drone in each time slot to ensure the optimal communication rate.

[0145] These three decision variables are iteratively optimized under the constraints to maximize the average communication rate of the drone. That is, after multiple iterations and adjustments, the average communication rate between the drone and the user is maximized under all constraints.

[0146] The purpose of step S3 is to decompose the complex non-convex optimization problem into three solvable convex optimization sub-problems, and adopt an alternating iterative optimization strategy to gradually approach the global optimal solution, and finally determine the user scheduling of the UAV, the beamforming vector emitted by the UAV, and the flight trajectory of the UAV, so as to maximize the average communication rate between the UAV and the ground users while satisfying all constraints.

[0147] The following is a simulation experiment to illustrate: Figure 2 In the network model shown, G is set t =15dBi, G b =10dBi,σ k 2 =-110dBm, M x =M y =4, K=10, H=90m, T=75s, δ t =0.5s, P max =10w,v max =40m / s.

[0148] σ k 2 = -110dBm converted to watts is 10 -14 W is the noise power value.

[0149] The center of the area where the UAV performs the mission (0, 0) is used as the center of the UAV's circular initial trajectory, and the radius is set to r = 250m. In this way, when the UAV starts the communication mission, its initial trajectory can contact the surrounding target ground users in a relatively balanced manner. In the actual flight process, the UAV further dynamically adjusts its flight trajectory according to the real-time channel state information and the user's communication needs to complete the task of effectively assisting ground users in communication.

[0150] Figure 3 The optimized trajectory of the drone presented in Figure 1 clearly demonstrates this adaptive adjustment process. Compared with the initial trajectory, the optimized trajectory shows that the drone actively moves closer to the ground user. Because under the goal of maximizing the communication rate, the shortened distance between the drone and the target ground user means that the path loss during signal propagation will be significantly reduced, so that the target ground user can obtain higher signal strength at its receiving end.

[0151] The optimization goal is to maximize the average communication rate between the drone and the user, that is, the sum of the communication rates of all time slots is maximized. According to formula (2) and formula (3), the closer the distance between the drone and the ground user is, the greater the channel gain h k,com (l(n),l k ) is larger, so the communication rate Therefore, after the algorithm is optimized, the drone will serve the ground users as close as possible. When the set flight time and speed are sufficient, the drone will serve right above each user, and then continue to fly forward while serving. Figure 3 It is a two-dimensional plane diagram of the drone's flight trajectory. From a two-dimensional perspective, the drone's flight trajectory is connected to multiple users. In three-dimensional space, the drone itself will have a certain height, and it is actually flying above the ground users with such a trajectory.

[0152] Figure 4 The figure shows the scheduling of ground users by drones in different time slots. Starting from the starting position, the drone gives priority to serving the users closest to it, while ensuring that every user is served. From the perspective of channel quality, the drone gives priority to users with better channel conditions for communication scheduling. This is because in a good channel environment, limited communication resources can be used more efficiently and the data transmission rate per unit bandwidth can be increased. From the perspective of user service quality assurance, the scheduling algorithm avoids the situation where over-serving some users leads to a lack of communication resources for other users. Through the tightly coupled scheduling and trajectory optimization mechanism, the drone can make the most reasonable user scheduling decision in each time slot in a complex and changeable communication environment, thereby effectively improving the communication efficiency of the entire drone-assisted communication network and providing ground users with continuous, stable and efficient communication services. Figure 4The horizontal axis refers to the number of time slots N in the UAV flight cycle, and the vertical axis refers to the serial number of the target ground user from 1 to 10. Figure 4 This indicates that the drone dispatches corresponding users at corresponding time slots of flight.

[0153] Figure 5 and Figure 6 The plane beam schematic diagram and three-dimensional beam schematic diagram of the signal-to-noise ratio at the user receiving point in a randomly selected time slot are respectively shown. The narrower communication beam is concentrated at the user scheduled in the current time slot, indicating that by optimizing the communication beamforming vector of the drone, the uniform planar antenna array it is equipped with can accurately control the phase and amplitude of the transmitted signal. In the communication process for a specific user in the current time slot, based on the user's location information and channel characteristics, the transmitted signal energy is highly concentrated in the direction of the user, which is reflected in the significant improvement of the user's signal-to-noise ratio. When beamforming improves the signal-to-noise ratio, according to Shannon's theorem, the communication rate will be significantly increased under unit bandwidth.

[0154] Figure 5 In the simulation, an area of ​​1000 meters * 1000 meters is simulated, and 10 target ground user positions are arranged in this area. The innovation of the present invention is that the UAV is combined with beamforming technology to assist communication, so the advantages of beamforming technology need to be reflected through simulation results. The present invention optimizes the beamforming vector emitted by the UAV, which is a complex matrix containing amplitude and phase. By optimizing (adjusting the amplitude and phase of the matrix), the signals emitted by the antenna array on the UAV are all directed to one user, so combined with formula (3), the communication rate is enhanced to meet the optimization goal. In each time slot, the UAV will only provide service to one user. Therefore, in each time slot, there will be a beam concentrated at a corresponding user, which means that only this position has a communication rate. Therefore, a time slot is randomly selected for observation ( Figure 5 and Figure 6 The corresponding time slot is 55, combined with Figure 3 It can be seen that the drone is serving the fifth target ground user at this time), so the narrow beam is concentrated on the fifth user position in the figure. A narrow beam means high energy. The height value of the beam is the size of the signal-to-noise ratio.

[0155] Figure 6 For a two-dimensional graph Figure 5 The three-dimensional perspective of the simulation area is shown in Figure 3, where the x and y axes represent the simulation area (meters). The communication rate in formula (3) is In order to display the beam diagram linearly (if the communication rate is used as the vertical axis, due to the existence of log, the image will produce multiple beams, and it is not possible to intuitively see which user has the highest communication rate in this time slot), the signal-to-noise ratio is used as the vertical axis, and the larger the signal-to-noise ratio, the larger the communication rate. In general, using the signal-to-noise ratio as the vertical axis makes the image more intuitive and also satisfies the optimization problem.

[0156] Figure 7 The comparison of the communication rate between the preset trajectory scheme and the optimized trajectory scheme is shown. As the maximum transmission power of the drone increases, the communication rates of both schemes are improved, and the communication rate of the optimized trajectory scheme is always higher than that of the preset trajectory scheme. This is because the optimized trajectory scheme does not rely solely on the increase in transmission power to improve the communication conditions. The drone will dynamically adjust the flight path based on multiple factors such as the distribution of ground users and channel status, so that it can be closer to users with better signal transmission quality, thereby reducing the path loss and interference during signal propagation. Under the same transmission power, the optimized trajectory scheme can effectively transmit more signal energy to the receiving end, improving the communication quality and rate. However, due to the lack of such flexibility, the preset trajectory scheme is relatively limited in terms of communication rate improvement. In addition, it can be observed from the figure that the number of antenna units also has an impact on the communication rate of the system. The more antenna units there are, the more significantly the gain of beamforming will be improved, and the higher the communication rate that the system can achieve.

[0157] The preset trajectory plan refers to the fixed drone along Figure 3 The scheme of initial trajectory flight is that the flight trajectory of the drone is not optimized. Obviously, compared with the scheme of optimizing the trajectory in the present invention (adjusting the flight path to approach the user), the flight trajectory of the drone that is not optimized is farther away from the user, so the communication rate is low under the same transmission power. With the increase of the maximum transmission power of the drone, the communication rate of both schemes is improved. Combined with formulas (6) and (7), this is because the beamforming vector w of the drone c (n) can have a larger amplitude at a higher transmission power, thus improving the communication rate between the UAV and the ground user from formula (3). Similarly, the more antennas there are, the stronger the enhanced signal can be generated at a specific ground user, and the higher the communication rate between the UAV and the ground user.

[0158] Embodiment 2:

[0159] Another embodiment of the present invention discloses a system for multi-antenna UAV-assisted ground user communication, thereby realizing a multi-antenna UAV-assisted ground user communication method in Embodiment 1. The specific implementation of each module refers to the corresponding description in Embodiment 1.

[0160] like Figure 8 As shown, a system for multi-antenna UAV to assist ground user communication in this embodiment includes the following modules:

[0161] The simulation model construction module M1 is used to construct a simulation model of a multi-antenna UAV-assisted multi-ground user communication system; the simulation model includes a multi-antenna UAV model and multiple ground user models; the multi-antenna UAV is provided with a uniform planar antenna array, and when performing a circular flight mission, it transmits communication beams to multiple ground users based on the communication optimal solution; the ground user is used to send a request to the UAV based on the received directional beam signal;

[0162] The communication link analysis module M2 is used to calculate the steering vector from the drone to each ground user; calculate the channel gain from the drone to each ground user based on the steering vector; and calculate the communication rate between the drone and each ground user based on the channel gain;

[0163] The optimization model building module M3 is used to build a UAV-assisted ground user communication optimization model that takes maximizing the average communication rate between the UAV and multiple ground users as the optimization objective function, while satisfying user scheduling constraints, UAV communication performance constraints, UAV transmission power constraints, and UAV flight trajectory constraints;

[0164] The model solving module M4 is used to solve the UAV-assisted ground user communication optimization model to obtain the optimal solution for communication between the UAV and multiple ground users.

[0165] The ground user receives the directional beam signal transmitted by the drone, converts the received analog signal into a digital signal, and extracts the transmitted data from it. The received data is decoded to restore the original information. According to actual needs, the ground user sends a confirmation signal or feedback information to the drone, as well as a request to achieve two-way communication. Through this two-way communication mechanism, the drone can achieve effective data exchange with the ground user, supporting various applications such as remote monitoring, emergency rescue, data collection and distribution, etc.

[0166] In summary, the method and system for multi-antenna UAV-assisted ground user communication according to the embodiment of the present invention have the following beneficial effects:

[0167] 1. The present invention comprehensively considers user scheduling, the communication beamforming vector emitted by the UAV, and the flight trajectory of the UAV, and realizes the joint optimization of the three. Compared with the traditional UAV-assisted communication method, the present invention effectively improves the average communication rate of the system by combining beamforming technology, provides high-quality and efficient communication services for ground users, and has strong application value;

[0168] 2. Through multi-antenna technology, drones can transmit signals more accurately and directionally, reduce energy loss, and improve signal strength and communication quality, especially in multi-user environments. Multi-antenna drones can build optimized communication links for different users, flexibly and efficiently meet the communication needs of multiple ground users, and improve the overall service capabilities of the system.

[0169] 3. By constructing and solving the optimization model, the optimal configuration of user scheduling, beamforming and UAV trajectory is achieved, the average communication rate is maximized, and the utilization efficiency of communication resources is improved.

[0170] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for multi-antenna UAV to assist ground user communication, characterized in that: The steps include: Step S1, constructing a simulation model of a multi-antenna UAV-assisted multi-ground user communication system, and calculating a steering vector from the UAV to each of the ground users; Calculate the channel gain from the drone to each ground user based on the steering vector; calculate the communication rate between the drone and each ground user based on the channel gain; Step S2: constructing a UAV-assisted ground user communication optimization model that takes maximizing the average communication rate between the UAV and multiple ground users as the optimization objective function, while satisfying user scheduling constraints, UAV communication performance constraints, UAV transmission power constraints, and UAV flight trajectory constraints; Step S3: Solve the UAV-assisted ground user communication optimization model to obtain the optimal solution for communication between the UAV and multiple ground users.

2. The method according to claim 1, characterized in that: The UAV is equipped with a uniform planar antenna array, which is deployed parallel to the ground. The total number of antennas in the uniform planar antenna array and the distance between antenna units are divided along the x-axis and y-axis of the three-dimensional Cartesian coordinate system to obtain the total number of antenna arrays M=M x ×M y and the distance d between the antennas along the coordinate system axis x =d y =λ / 2; Among them, M x 、M y are the number of antennas arranged on the x-axis and y-axis respectively; d x ,d y are the spacing of the antennas along the x-axis and y-axis respectively; λ is the carrier wavelength.

3. The method according to claim 2, characterized in that: Based on M x 、M y and d x ,d y Calculate the steering vector α(l(n),l from the UAV to the kth ground user k ), calculated as follows: Where k = 1, 2, ..., K, K is the number of ground users; l(n) is the position of the UAV in the nth time slot, l k is the position of the kth ground user, θ(l(n),l k )、Φ(l(n),l k ) are the AoD elevation angle and AoD azimuth angle when the UAV transmits the signal to the kth ground user.

4. The method according to claim 3, characterized in that: Based on α(l(n),l k ) Calculate the channel gain h from the UAV to the kth ground user k,com (l(n),l k ), calculated as follows: Where n = 1, 2, ... N, N is the number of time slots in the UAV flight cycle; G t is the antenna gain of the UAV transmitter, G b is the antenna gain received by the ground user, d(l(n),l k )=||l(n)-l k || represents the Euclidean distance between the UAV and the kth ground user.

5. The method according to claim 4, characterized in that: Under unit bandwidth, based on h k,com (l(n),l k ) Calculate the communication rate between the UAV and the kth ground user The calculation is as follows: Among them, c k (n) is the user scheduling of the UAV in the nth time slot, is the communication beamforming vector of the UAV in the nth time slot, is the noise that obeys Gaussian random distribution, H is the channel gain h k,com (l(n),l k )'s conjugate transpose; c k (n) is a binary discrete variable; When the UAV transmits a communication beam in time slot n to perform a communication task for the kth ground user, c k (n)=1, otherwise c k (n)=0.

6. The method according to claim 1, characterized in that: The UAV-assisted ground user communication optimization model is as follows: C6:l(1)=l(N)=l I in, is the communication rate between the UAV and the kth ground user; K is the number of ground users; n = 1, 2, ... N, N is the number of time slots in the UAV flight cycle; C, W, L are the user scheduling to be optimized, the beamforming vector transmitted by the UAV, and the UAV trajectory variable, respectively; C1 and C2 represent user scheduling constraints, C3 represents UAV communication performance constraints, is the preset communication rate threshold between the UAV and the base station, C4 represents the UAV transmission power constraint, C5 and C6 represent the UAV flight trajectory constraints, and P max is the maximum transmission power of the UAV, D max is the maximum flight distance of the UAV in each time slot, l I is the starting position of the drone.

7. The method according to claim 6, characterized in that: The step S3 comprises: The UAV-assisted ground user communication optimization model includes three nonlinear coupling variables: user scheduling, beamforming vectors emitted by the UAV, and UAV trajectory. The model is solved as a non-convex optimization problem. Decomposing the non-convex optimization problem into three convex optimization sub-problems of user scheduling, beamforming vectors emitted by the UAV, and UAV trajectory; Alternately iterate and optimize three convex optimization sub-problems to gradually approach the global optimal solution; When the objective function converges, the global optimal solution obtained is the optimal solution for communication between the UAV and multiple ground users; wherein the global optimal solution includes user scheduling, beamforming vectors emitted by the UAV, and UAV trajectories.

8. The method according to claim 7, characterized in that: The non-convex optimization problem involves user scheduling Beamforming vectors transmitted by drones Drone tracks Three nonlinear coupled variables.

9. The method according to claim 7, characterized in that: The three convex optimization sub-problems of alternately optimizing user scheduling, the beamforming vectors emitted by the UAV, and the trajectory of the UAV gradually approach the global optimal solution, including: In the mth iteration, when solving the user scheduling, given the preset UAV-transmitted beamforming vector and the initial UAV trajectory, the UAV-assisted ground user communication optimization model is solved to obtain the user scheduling C (m) ; When solving the beamforming vector emitted by the UAV, based on C (m) , the initial UAV trajectory, solving the UAV-assisted ground user communication optimization model to obtain the beamforming vector W (m) ; When solving the trajectory of the UAV, based on W (m) , C (m) , solve the UAV-assisted ground user communication optimization model to obtain L (m) ; The solution is solved in an alternating cycle until the optimization objective function converges, and the user scheduling, the beamforming vector emitted by the UAV, and the trajectory of the UAV in the current round are the global optimal solution.

10. A system for multi-antenna UAV to assist ground user communication, characterized in that: Includes the following modules: The simulation model building module M1 is used to build a simulation model of a multi-antenna UAV-assisted multi-ground user communication system; the simulation model includes a multi-antenna UAV model and multiple ground user models; the multi-antenna UAV is provided with a uniform planar antenna array, and when performing a circular flight mission, transmits communication beams to multiple ground users based on the communication optimal solution; the ground user is used to send a request to the UAV based on the received directional beam signal; The communication link analysis module M2 is used to construct a multi-antenna UAV-assisted multi-ground user communication system and calculate the steering vector from the UAV to each ground user; Calculate the channel gain from the drone to each ground user based on the steering vector; calculate the communication rate between the drone and each ground user based on the channel gain; The optimization model building module M3 is used to build a UAV-assisted ground user communication optimization model that takes maximizing the average communication rate between the UAV and multiple ground users as the optimization objective function, while satisfying user scheduling constraints, UAV communication performance constraints, UAV transmission power constraints, and UAV flight trajectory constraints; The model solving module M4 is used to solve the UAV-assisted ground user communication optimization model to obtain the optimal solution for communication between the UAV and multiple ground users.

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