Track and double-hop beam forming joint optimization method for unmanned aerial vehicle relay network

Abstract

The invention discloses a track and double-hop beam forming joint optimization method for an unmanned aerial vehicle relay network. The method comprises the following steps: S1, initializing an unmanned aerial vehicle system and setting parameter configuration; wherein the parameter configuration comprises an initial position, a flight parameter, a communication system parameter, a user position and a demand of the unmanned aerial vehicle; s2, constructing a comprehensive energy consumption model, and introducing constraint conditions to ensure that the flight path and communication performance of the unmanned aerial vehicle meet actual requirements; s3, based on the comprehensive energy consumption model, iteratively optimizing the trajectory of the unmanned aerial vehicle, the beam forming vector from the first hop base station to the unmanned aerial vehicle and the beam forming vector from the second hop unmanned aerial vehicle to the user by adopting a snake-egret optimization algorithm, and minimizing the total energy consumption of the system formed by the flight energy of the unmanned aerial vehicle and the double-hop communication energy; according to the method, the problem of non-convex optimization caused by coupling between the mobility of the unmanned aerial vehicle and the double-hop beam forming vector is solved by utilizing a snake-egret optimization algorithm.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of unmanned aerial vehicle communication network and wireless relay technology, in particular to a trajectory and double-hop beamforming joint optimization method for unmanned aerial vehicle relay network. BACKGROUND

[0002] Unmanned aerial vehicle (UAV) as an aerial relay node has shown significant potential in the field of wireless communication, and its reconfigurable deployment capability can effectively enhance the coverage range of cellular network, especially in scenarios where ground infrastructure is unavailable or overloaded (such as vehicle networking, emergency communication, remote area communication). The existing technology mainly improves the performance of unmanned aerial vehicle relay network through trajectory optimization and beamforming. In the aspect of beamforming, the research focuses on the design of static scenarios, such as adopting a three-dimensional beamforming framework in millimeter wave unmanned aerial vehicle communication to improve the flexibility of coverage, or developing an adaptive hybrid beamforming strategy for the problem of beam misalignment caused by unmanned aerial vehicle jitter. However, such methods usually assume that the position of unmanned aerial vehicle is fixed, and cannot adapt to dynamic channel changes. In the aspect of trajectory optimization, the existing methods usually assume static beamforming, ignoring the mutual dependence between UAV positioning and beamforming gain.

[0003] The position of unmanned aerial vehicle directly affects the channel state and beamforming gain, and the beam direction needs to be dynamically adjusted with the position. If only one dimension is optimized, such as fixed trajectory optimization beam or fixed beam design trajectory, it will cause energy waste. Moreover, existing research generally assumes static user distribution or ignores kinematic constraints, so it cannot adapt to the needs of random user movement, strict energy consumption limit and other requirements in actual scenarios. In addition, the communication energy consumption of double-hop relay structure (base station (BS) → unmanned aerial vehicle → user) is not completely modeled, and most researches analyze single-hop link in isolation, without considering the flight energy consumption and double-hop communication energy consumption. Therefore, it is urgent to develop a framework that jointly optimizes the trajectory of unmanned aerial vehicle, the beamforming of base station-unmanned aerial vehicle in the first hop and the beamforming of unmanned aerial vehicle-user in the second hop, in order to cooperatively reduce the total energy consumption of the system and meet the dynamic communication needs. SUMMARY

[0004] The purpose of the present application is to provide a trajectory and double-hop beamforming joint optimization method for unmanned aerial vehicle relay network, which uses the snake optimization algorithm to solve the non-convex optimization problem caused by the coupling between the mobility of unmanned aerial vehicle and the double-hop beamforming vector.

[0005] To achieve the above purpose, the present application provides the following technical scheme: The trajectory and double-hop beamforming joint optimization method for unmanned aerial vehicle relay network comprises the following steps: S1, initializing the unmanned aerial vehicle system and setting parameter configurations; wherein the parameter configurations include: initial position of the unmanned aerial vehicle, flight parameters, communication system parameters, user position and requirements; S2, constructing a comprehensive energy consumption model and introducing constraint conditions to ensure that the flight trajectory of the unmanned aerial vehicle and the communication performance meet the actual requirements; wherein the comprehensive energy consumption model includes: flight energy function of the unmanned aerial vehicle, base station communication energy function and unmanned aerial vehicle communication energy function; the constraint conditions include: communication distance constraint, communication rate constraint and kinematic constraint; S3, based on the comprehensive energy consumption model, using the snake egret optimization algorithm to iteratively optimize the unmanned aerial vehicle trajectory, the base station to unmanned aerial vehicle beamforming vector and the unmanned aerial vehicle to user beamforming vector, minimizing the system total energy consumption composed of unmanned aerial vehicle flight energy and double-hop communication energy; wherein the air transfer from the base station to the unmanned aerial vehicle is the first hop, and the ground transfer from the unmanned aerial vehicle to the user is the second hop.

[0006] Further, in the S1, initializing the unmanned aerial vehicle system and setting parameter configurations, specifically: The flight height of the unmanned aerial vehicle system is set to , the initial horizontal position is , the base station position is , and the user position is ; the running time of the unmanned aerial vehicle system is divided into time slots, and the time slot length is ; the base station is equipped with a uniform linear array of G antennas, and the unmanned aerial vehicle is equipped with a uniform linear array of antennas, and the distance between each antenna is ; the unmanned aerial vehicle system uses a line-of-sight channel model, and the channel gain of the reference distance in the line-of-sight channel model is , and the flight energy constant is .

[0007] Further, in the S2, the construction process of the comprehensive energy consumption model, specifically: Construct a system total energy function, the system total energy function includes: flight energy function of the unmanned aerial vehicle wherein is the flight time of the unmanned aerial vehicle, is the speed of the unmanned aerial vehicle at time slot n, the communication energy function of the first hop and the communication energy function of the beamforming vector of the unmanned aerial vehicle to the user in the second hop ; wherein, ; is the beamforming vector of the unmanned aerial vehicle to the user.

[0008] Further, in the S2, the communication distance constraint, specifically: the distance between the user and the unmanned aerial vehicle is Distance between base station and UAV Position of UAV at time slot n Position of base station Position of user at time slot n

[0009] Further, in S2, the communication rate constraint is specifically: user rate Channel vector of second hop Relay capacity constraint , Communication rate of first hop

[0010] Further, in S2, the kinematic constraint is specifically: speed of UAV Acceleration of UAV Displacement ; wherein Position of UAV at time slot n

[0011] Further, in S3, based on the comprehensive energy consumption model, the snake heron optimization algorithm is used to iteratively optimize the UAV trajectory and double-hop beamforming vector, and the system total energy consumption composed of the UAV flight energy, the communication energy of the first hop and the communication energy of the second hop is minimized, specifically:

[0012] S31, initialize parameters and population: set the population size and the maximum number of iterations ; randomly generate an initial population , each initial population individual contains decision variables: position of UAV at time slot n , first-hop beamforming vector , second-hop beamforming vector The objective function is the total energy consumption

[0013] S32, when the number of iterations : update the position by random difference to enhance the global search ability; When the number of iterations is : combine the historical optimal position x best and Brownian motion disturbance to realize local fine search; When the number of iterations is : introduce weighted Levy flight to improve convergence precision and avoid local optimum; S33, select the environment camouflage or fast escape strategy with a probability , the environment camouflage approximates the historical optimal solution by disturbing the current solution, and the fast escape expands the solution space diversity by random vector amplification; S34, updating the optimal solution of the non-convex coupling problem of the output unmanned aerial vehicle flight trajectory: calculating a new position fitness value, updating the individual if it is better than the original value; outputting the optimal solution after iteration, including the trajectory and beamforming vectors .

[0014] According to the specific embodiments provided by the application, the following technical effects are disclosed: The application can dynamically adjust the position and communication parameters of the unmanned aerial vehicle by jointly optimizing the flight trajectory and double-hop beamforming of the unmanned aerial vehicle, thereby improving the stability and transmission rate of the communication link; the establishment and optimization of the comprehensive energy consumption model enable the unmanned aerial vehicle to fly in a more energy-saving manner when performing tasks, thereby prolonging the endurance time; the introduction of communication demand constraints and kinematic constraints ensures that the unmanned aerial vehicle can meet the requirements of communication performance and comply with the limitation conditions of physical flight during flight; the non-convexity of the flight trajectory and the dynamic adjustment requirement of the beamforming usually lead to difficulty in solving the optimization problem. The snake heron optimization algorithm, combined with differential mutation, Brownian motion, and Levy flight strategies, can effectively handle such complex optimization problems and find a better solution; the three stages (differential mutation, Brownian motion, and Levy flight) of the snake heron optimization algorithm combine the advantages of global search and local search, can efficiently find the optimal solution in a complex search space, and avoid falling into local optimum; the total energy consumption is reduced by at least 20% under the condition of meeting multiple constraints, especially in high user density or high communication rate demand scenarios, significantly improving dynamic adaptability. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, brief descriptions will be given below of the drawings needed in the embodiments or prior art descriptions of the present application. Obviously, the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of the provided drawings.

[0016] The trajectory and double-hop beamforming joint optimization method of the unmanned aerial vehicle relay network of the present application will be further described below in combination with the drawings; Figure 1 is the scene diagram of the trajectory and double-hop beamforming joint optimization method of the unmanned aerial vehicle relay network in the present application; Figure 2 is the total energy consumption comparison diagram under different communication rate requirements in the present application; Figure 3 is the total energy consumption comparison diagram under different user quantities in the present application. DETAILED DESCRIPTION

[0017] The specific embodiments of the present application are described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate the present application, but are not intended to limit the scope of the present application.

[0018] In order to better understand the purpose, structure and function of the present application, the present application is described in further detail below with reference to the accompanying drawings.

[0019] As Figure 1 shown, the present application provides a trajectory and double-hop beamforming joint optimization method for a UAV relay network, which minimizes the total energy consumption of the system (including the UAV flight energy, base station communication energy and UAV communication energy) composed of the UAV flight energy and double-hop communication energy by cooperatively optimizing the UAV trajectory, the first-hop base station to UAV beamforming vector and the second-hop UAV to user beamforming vector, including the following steps: S1, initialize the UAV system and set the parameter configuration; wherein the parameter configuration includes: the initial position of the UAV, the flight parameter, the communication system parameter, the user position and the demand; In the S1, the UAV system is initialized and the parameter configuration is set, specifically: The UAV flight height is set to be fixed , the initial horizontal position , the base station position , the user position changes dynamically . The system running time is divided into slots, and the slot duration . The base station is equipped with a uniform linear array of G antennas, and the UAV is equipped with a uniform linear array (ULA) of antennas, and the antenna spacing . The channel model adopts line-of-sight (LoS) transmission, and the reference distance channel gain . The flight energy constant , , and the noise power .

[0020] The UAV flight height is set to be fixed , the initial position of the UAV , the position of the UAV at slot n , the base station position , the position of the user at slot n . The system running time is divided into slots, and the slot duration . The base station is equipped with ULA of root antennas, the UAV is also equipped with a ULA of root antennas, the antenna spacing . The channel model adopts line-of-sight (LoS) transmission, and the reference distance channel gain . Flight energy constant 、 , noise power .

[0021] S2, a comprehensive energy consumption model is constructed, and a constraint condition is introduced to ensure that the flight trajectory and communication performance of the UAV meet the actual requirements; wherein the comprehensive energy consumption model includes: flight energy function of the UAV, base station communication energy function and UAV communication energy function; the constraint condition includes: communication distance constraint, communication rate constraint and kinematics constraint, as shown in Figure 2 and Figure 3 ; In the S2, the construction process of the flight energy consumption model is as follows: The system total energy is constructed, and the system total energy is respectively: the flight energy consumption of the UAV is:

[0022] Wherein is the flight time of the UAV, is the speed of the UAV at time slot n. The first hop communication energy function and the second hop communication energy function ; wherein, is the beamforming vector from the base station to the UAV; is the beamforming vector from the UAV to the user. In the S2, the communication demand constraint includes: communication distance constraint and communication rate constraint; Wherein, the communication distance constraint is: the distance between the user and the UAV ; the distance between the base station and the UAV ; The communication rate constraint is: the user rate , is the second hop channel vector, is the minimum communication rate of the second hop; the relay capacity limit , is the first hop communication rate.

[0023] In the S2, the kinematics constraint is specifically: the speed , the acceleration , the displacement .

[0024] To solve the non-convex coupling problem of trajectory and double-hop beamforming, the Secretary Bird Optimization Algorithm (SBOA) is adopted. The algorithm simulates the predatory behavior in stages, such as step S3: in the early iteration ( ), a new solution is generated using differential mutation operation to avoid local optimum; in the balance stage ( ), local search is performed by combining Brownian motion and historical optimal solution; in the development stage ( ), weighted Lévy flight is introduced to enhance global convergence. In addition, the hiding and displacement behaviors are dynamically switched through the escape strategy to ensure the balance between exploration and development.

[0025] S3, based on the comprehensive energy consumption model, the Secretary Bird Optimization Algorithm is used to iteratively optimize the UAV trajectory and double-hop beamforming vector, minimizing the total energy consumption of the system composed of UAV flight energy, base station communication energy and UAV communication energy.

[0026] In S3, the process of solving the non-convex optimization problem of UAV flight trajectory is as follows: S31, initialize parameters and population: set the population size , the maximum number of iterations . Randomly generate the initial population , each individual contains decision variables: the position of the UAV at time slot n , the first-hop beamforming vector , the second-hop beamforming vector . The objective function is the total energy consumption ; S32, stage-based exploration strategy (predatory behavior), including the following three stages: Stage 1 (iteration number ): update the position by random difference to enhance global search ability; Stage 2 (iteration number ): combine the historical optimal position x best and Brownian motion disturbance to realize local fine search; Stage 3 (iteration number ): introduce Lévy flight to improve convergence accuracy and avoid local optimum.

[0027] S33, development strategy: select the environment camouflage or fast escape strategy with probability , the environment camouflage approximates the historical optimal solution by perturbing the current solution, and the fast escape expands the solution space diversity by random vector.

[0028] S34, update and output optimal solution: calculate the fitness value of the new position, and update the individual if it is better than the original value; output the optimal solution after iteration , comprising a trajectory and a beamforming vector .

[0029] When the user moves, the channel matrix changes dynamically, and the system triggers joint optimization: the UAV dynamically adjusts the position according to the real-time position of the user , while updating the first-hop beamforming vector according to the change of the first-hop channel vector , updating the second-hop beamforming vector according to the change of the second-hop channel vector , and smoothing the trajectory through acceleration constraints , and verifying that the total energy consumption is reduced by at least 20% compared with the fixed trajectory benchmark method. The method in the application cooperates trajectory optimization with double-hop beamforming design, significantly improves energy efficiency (simulation verification total energy consumption is reduced by at least 20%), and meets the requirements of multi-user mobility and beamforming time-varying. In summary, the application establishes a comprehensive energy consumption model, systematically describes the propulsion energy of the UAV and the double-hop communication energy, so that the UAV relay network can be completely evaluated in performance, instead of focusing on isolated communication stages or single components. Moreover, the application proposes a unified framework to optimize the UAV trajectory and double-hop beamforming at the same time, so as to minimize the total energy consumption. This overall method captures the interdependence between mobility and double-hop beamforming, significantly reduces the total energy consumption while maintaining reliable communication.

[0030]

[0031] In summary, the application establishes a comprehensive energy consumption model, systematically describes the propulsion energy of the UAV and the double-hop communication energy, so that the UAV relay network can be completely evaluated in performance, instead of focusing on isolated communication stages or single components. Moreover, the application proposes a unified framework to optimize the UAV trajectory and double-hop beamforming at the same time, so as to minimize the total energy consumption. This overall method captures the interdependence between mobility and double-hop beamforming, significantly reduces the total energy consumption while maintaining reliable communication.

[0032] ​​The energy optimization method of joint dual-hop beamforming and trajectory design in the unmanned aerial vehicle relay network in the application includes cooperative optimization of unmanned aerial vehicle trajectory and dual-hop beamforming vector to minimize the total energy consumption of the system (covering unmanned aerial vehicle flight energy, base station communication energy and unmanned aerial vehicle communication energy). The method first establishes an unmanned aerial vehicle flight energy model and a dual-hop communication energy model containing kinematic constraints, and defines communication distance constraints, communication rate constraints and kinematic constraints. Then the snake heron optimization algorithm (SBOA) is used to solve the joint optimization problem of trajectory and beamforming vector: initialize the population and set the objective function as the total energy consumption; update the decision variable through the phased exploration strategy (random differential enhancement global search, historical optimal solution combined with Brown motion to realize local refinement, Lévy flight to improve convergence) and development strategy (environmental camouflage or rapid escape); finally output the optimal trajectory and beamforming vector. Compared with the trajectory planning method based on the center point, the application realizes at least 20% reduction of the total energy consumption under the condition of meeting multiple constraints, especially significantly improves the dynamic adaptability in high user density or high communication rate demand scenarios.

[0033] The above description of the disclosed embodiments enables one skilled in the art to make or use the application. Numerous modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Therefore, the application is not to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. The present application discloses a trajectory and double-hop beamforming joint optimization method for unmanned aerial vehicle relay network, comprising the following steps: S1, initialize the unmanned aerial vehicle system and set the parameter configuration; Wherein the parameter configuration includes: the initial position of the unmanned aerial vehicle, flight parameters, communication system parameters, user location and demand; S2, build a comprehensive energy consumption model, and introduce constraint conditions to ensure that the flight trajectory and communication performance of the unmanned aerial vehicle meet the actual requirements; Wherein the comprehensive energy consumption model includes: the flight energy function of the unmanned aerial vehicle, the base station communication energy function and the unmanned aerial vehicle communication energy function; The constraint conditions include: communication distance constraint, communication rate constraint and kinematic constraint; S3, based on the comprehensive energy consumption model, the snake heron optimization algorithm is used to iteratively optimize the unmanned aerial vehicle trajectory, the beamforming vector from the base station to the unmanned aerial vehicle and the beamforming vector from the unmanned aerial vehicle to the user, and the system total energy consumption composed of the flight energy of the unmanned aerial vehicle and the double-hop communication energy is minimized; Wherein the air transfer from the base station to the unmanned aerial vehicle is the first hop, and the ground transfer from the unmanned aerial vehicle to the user is the second hop.

2. The method of claim 1, wherein, In the S1, the unmanned aerial vehicle system is initialized and the parameter configuration is set, specifically: The flight height of the unmanned aerial vehicle system is set to be fixed , the initial horizontal position is , the base station position is , and the user position is ; the running time of the unmanned aerial vehicle system is divided into time slots, and the time slot length is ; the base station is equipped with a uniform linear array of G antennas, and the unmanned aerial vehicle is equipped with a uniform linear array of antennas, and the spacing between each antenna is ; The UAV system adopts a line-of-sight channel model, the channel gain of the reference distance in the line-of-sight channel model is , and the flight energy constants are .

3. The method of claim 1, wherein, In the S2, the construction process of the comprehensive energy consumption model is as follows: constructing a system total energy function, the system total energy function including a flight energy function of the unmanned aerial vehicle wherein is a flight time of the unmanned aerial vehicle, is a velocity of the unmanned aerial vehicle at time slot n, a communication energy function of the first hop and a communication energy function of the second hop beamforming vector of the unmanned aerial vehicle to the user ; wherein is a beamforming vector of the base station to the unmanned aerial vehicle; is a beamforming vector of the unmanned aerial vehicle to the user.

4. The method of claim 1, wherein, In the S2, the communication distance constraint is specifically: the distance between the user and the UAV is ; the distance between the base station and the UAV is ; wherein is the position of the UAV at time slot n, is the base station position, is the position of the user at time slot n.

5. The method of claim 1, wherein, The communication rate constraint in S2 is specifically: user rate , is the channel vector for the second hop, ; relay capacity limit , is the communication rate for the first hop.

6. The method of claim 1, wherein, In S2, the kinematic constraint is specifically: the velocity of the UAV , the acceleration of the UAV , the displacement ; wherein is the position of the UAV at time slot n.

7. The method of claim 1, wherein, In the S3, based on the comprehensive energy consumption model, the snake heron optimization algorithm is used to iteratively optimize the unmanned aerial vehicle trajectory and double-hop beamforming vector, and the system total energy consumption composed of the flight energy of the unmanned aerial vehicle, the communication energy of the first hop and the communication energy of the second hop is minimized, specifically: S31, initialize parameters and population: set population size and maximum number of iterations ; randomly generate initial population each initial population individual contains decision variables: position of UAV at time slot n , first-hop beamforming vector , second-hop beamforming vector ; The objective function is total energy consumption ; S32, when the number of iterations S32, when the number of iterations S32, when the number of iterations S32, when the number of iterations S32, when the number of iterations When the iteration number combines the historical optimal position x best and Brownian motion disturbance to achieve local refinement search; When the number of iterations is: introduce the weighted Levi flight to improve the convergence precision and avoid local optimum; S33, with probability selecting either an environmental camouflage or a fast escape strategy, the environmental camouflage approximating a historical optimal solution by perturbing the current solution, the fast escape strategy increasing solution space diversity by augmenting the solution with a random vector; S34, updating the optimal solution of the non-convex coupling problem of the UAV flight trajectory and output: calculating the new position fitness value, updating the individual if it is better than the original value; outputting the optimal solution after iteration, including the trajectory and beamforming vectors .

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