Unmanned aerial vehicle communication system based on intelligent reflecting surface assistance and joint optimization method

Abstract

The invention belongs to the technical field of wireless communication, and particularly relates to an unmanned aerial vehicle communication system based on intelligent reflecting surface assistance and a joint optimization method, which are characterized in that a mode of deploying an AIRS is proposed, and a rotation angle, namely a direction angle, of an intelligent reflecting surface is introduced; secondly, combining the three-dimensional position of the unmanned aerial vehicle, the active beam forming of the base station and the passive beam forming of the intelligent reflecting surface to describe the signal-to-noise ratio of a user side; an AIRS single reflection system model with the optimization target of maximizing the signal-to-noise ratio of the receiving end is established, and then solving and analysis are carried out according to the characteristics of the AIRS single reflection model. According to the method, through joint optimization design of the three-dimensional track of the unmanned aerial vehicle, active beam forming of the base station antenna and passive beam forming of the intelligent reflecting surface, the signal-to-noise ratio of a single user side can be maximized, and the communication quality is improved.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, specifically a UAV communication system and joint optimization method based on intelligent reflector-assisted system. It enhances signal coverage and improves the signal-to-noise ratio (SNR) and other related wireless communication performance indicators at the user end by combining airborne intelligent reflectors (AIRS), base station beamforming and UAV trajectory planning. Background Technology

[0002] Traditional communication networks are prone to signal coverage blind spots due to obstacles such as terrain and buildings, affecting communication quality and reliability. Intelligent Reflectors (IRS), as a novel communication relay technology, passively reflect incident signals, thereby enabling active control and optimization of the signal propagation path.

[0003] AIRS, as an application of IRS on an aerial platform, combines the high mobility and ease of deployment of UAVs to achieve dynamic adjustment of coverage to optimize the performance of wireless communication networks. The three-dimensional trajectory planning of UAVs and the beamforming capabilities of AIRS allow communication networks to adapt to changing environments and demands. However, effectively integrating UAV trajectory design, AIRS beamforming, and active beamforming at base stations remains a significant research challenge.

[0004] Traditional methods rely heavily on convex optimization techniques and heuristic algorithms. However, these methods face challenges of non-convexity and high problem complexity when dealing with propagation path optimization, especially when multiple adjustable variables jointly affect the performance of communication systems. Existing technologies cannot fully utilize the potential capabilities of UAVs and AIRS, which limits further improvements in system performance. Summary of the Invention

[0005] The purpose of this invention is to solve the problem of signal propagation path optimization in existing wireless communication networks, especially to address the problem that traditional methods cannot effectively integrate UAV three-dimensional trajectory planning, AIRS passive beamforming, and base station active beamforming, and to maximize the signal-to-noise ratio at the user end based on these methods.

[0006] Specifically, the present invention aims to achieve the following objectives: (1) Design a signal-to-noise ratio optimization model that considers the three-dimensional position of the UAV, base station beamforming and intelligent reflector beamforming; (2) Propose an effective algorithm that can handle non-convex optimization problems in the model and find the optimal UAV trajectory under the conditions of satisfying the actual flight area, speed limit and azimuth angle limit; (3) Maximize the sum of the signal-to-noise ratios of the receivers during different flight periods, thereby optimizing the overall performance of the wireless communication system; (4) Considering the complexity of the problem, design an efficient genetic algorithm to search for the optimal solution, while ensuring that the proposed solution is easy to implement and deploy in real-world scenarios.

[0007] Through the above methods, the present invention aims to solve the problem of the inability to efficiently integrate the joint optimization of UAV trajectory and intelligent reflective surface in the prior art, and to improve the reliability of wireless communication networks.

[0008] This invention proposes a UAV communication system and joint optimization method based on intelligent reflector-assisted communication. It is applied to a communication system that includes a fixed base station, a UAV equipped with an intelligent reflector, and ground users. The system maximizes the total received signal-to-noise ratio of ground users during the communication cycle by jointly optimizing the UAV's three-dimensional trajectory, the intelligent reflector's reflection parameters, the base station's beamforming, and the intelligent reflector's azimuth angle.

[0009] The core of this invention lies in first proposing a deployment method for AIRS, introducing the rotation angle (or azimuth angle) of the intelligent reflector, and secondly combining the three-dimensional position of the UAV, active beamforming of the base station, and passive beamforming of the intelligent reflector to describe the signal-to-noise ratio (SNR) at the user end. Specifically, this invention establishes an AIRS single-reflection system model with the optimization objective of maximizing the SNR at the receiver, and then solves and analyzes the model based on its characteristics.

[0010] The method specifically includes the following steps: Step 1: Obtain the parameters of the communication scenario, including the location of the fixed base station, the location s of the ground user, the initial and final positions of the UAV, the flight altitude and speed constraints of the UAV, and the number of reflective units of the intelligent reflector; construct a system model, which includes a UAV discretized trajectory model, an intelligent reflector orientation angle model, a channel model from the base station to the intelligent reflector and from the intelligent reflector to the user, and a user received signal and signal-to-noise ratio model. Step 2: Establish a joint optimization problem with the goal of maximizing the total received signal-to-noise ratio of the user. The optimization variables of the optimization problem include the three-dimensional trajectory of the UAV, the phase shift matrix of the smart reflector, the beamforming vector of the base station, and the azimuth angle of the smart reflector. The constraints include the phase shift constraint of the smart reflector, the flight area constraint of the UAV, the flight speed constraint of the UAV, and the azimuth angle constraint of the smart reflector. Step 3: The joint optimization problem is decomposed into three sub-problems using an alternating optimization framework, namely, the base station active beamforming optimization sub-problem, the intelligent reflector passive beamforming and azimuth angle joint optimization sub-problem, and the UAV trajectory optimization sub-problem; Solve the active beamforming optimization subproblem of the base station to obtain the optimal base station beamforming vector; Solve the joint optimization subproblem of passive beamforming and orientation angle of the intelligent reflector to obtain the optimal phase shift matrix and orientation angle of the intelligent reflector; To solve the aforementioned UAV trajectory optimization subproblem, a genetic algorithm is used to optimize the UAV's three-dimensional trajectory. Step 4: Iteratively optimize the three sub-problems until the convergence condition is met, and output the optimal UAV trajectory and intelligent reflector orientation angle.

[0011] Furthermore, the specific methods for each step are as follows: The specific method for system initialization and model establishment in step 1 is as follows: Obtain fixed parameters for the communication scenario, including base station coordinates, ground user locations, UAV start and end points, flight altitude and speed constraints, and the number of intelligent reflector units. Calculate the total communication time. T Discretize into N There are 1 time slot, and the length of each time slot is 1. The drone trajectory is represented as a sequence of time slot locations. .

[0012] Establish a system model and define the three-dimensional position of the UAV. and the orientation angle of the intelligent reflective surface Constructing a base station to intelligent reflector channel and intelligent reflector to user channel And derive the user reception signal-to-noise ratio model. .in The horizontal position For height.

[0013] The specific method for establishing the joint optimization problem in step 2 is as follows: The optimization objective is to maximize the total received signal-to-noise ratio for the user. The optimization variables include the drone trajectory sequence. Intelligent reflector phase shift matrix Base station beamforming vector and intelligent reflective surface orientation angle sequence .

[0014] The constraints include: phase shift constraints on the intelligent reflector (phase shift constraints for each reflector element); UAV flight area constraints (horizontal and vertical flight boundaries); and UAV flight speed constraints (maximum horizontal speed). and vertical velocity ); Intelligent reflector orientation angle constraint (orientation angle) (Value range constraints).

[0015] The specific method for implementing the alternating optimization framework in step 3 is as follows: Step 3.1 Base Station Active Beamforming Optimization Fixed drone trajectory Intelligent reflector phase shift and direction angle Optimize base station beamforming vector Calculate the equivalent channel matrix. The largest eigenvalue corresponds to the eigenvector, from which the optimal beamforming vector is obtained. ,in It is the base station transmit array response vector. This refers to the number of base station antennas. Substituting the solution into the original problem eliminates variables. .

[0016] Step 3.2 Joint Optimization of Passive Beamforming and Direction Angle of Intelligent Reflector Fixed drone trajectory and optimal base station beamforming Jointly optimize the phase shift matrix of the intelligent reflector. and direction angle Calculate the optimal phase shift value. Compensation for signal phase difference, where It is the reflection unit index. Within the constraint interval. Internal optimization orientation angle make The largest, of which It is the azimuth angle related to the relative positions of the user and the base station.

[0017] Step 3.3 Drone trajectory optimization Optimizing the 3D position of the UAV using a genetic algorithm and direction angle This includes: initializing the population that satisfies the constraints; and using the total signal-to-noise ratio... Evaluate the trajectory for the fitness function; perform selection, crossover, and mutation operations; handle nonlinear constraints; generate a new generation of population and iterate until convergence.

[0018] The specific method for iterative optimization and result output in step 4 is as follows: Implement alternating optimization iterations: initialize all optimization variables; sequentially solve for the optimal base station beamforming. Intelligent reflective surface parameters Drone trajectory superscript Indicates the first The solution of the next iteration; compare the improvement in the overall objective function value with the threshold. Determine if convergence is achieved; output the optimal solution. subscript This represents the optimal solution.

[0019] This invention specifically includes the following aspects: This invention considers an IRS-assisted UAV wireless communication network. Due to the complex communication scenarios, in order to cope with the obstruction effect of obstacles, a UAV carrying a smart reflector is dispatched to work.

[0020] In a three-dimensional coordinate system, the horizontal coordinates of the fixed base station and the user are respectively... .

[0021] Let the UAV's flight time be T, and then discretize the UAV's operating time T into N intervals. The time slot, i.e. .

[0022] Allowing UAVs to fly horizontally And the UAV altitude in its vertical flight region time slot n Due to the limitations of UAV's maneuverability and freedom of movement, its flight altitude meets the requirements. , These represent the minimum and maximum altitudes for UAV flight under traffic control.

[0023] Let the initial and final positions of the UAV be respectively and Its flight trajectory is approximated as a path sequence. .in The position of the UAV in time slot n. These represent the horizontal and vertical positions of the UAV, respectively. The velocity of the UAV within a continuous time slot satisfies: , For the average and average vertical velocities of the UAV, Its maximum horizontal and maximum vertical flight speeds.

[0024] AIRS by It consists of a passive reflection unit. Representing along x shaft and z The number of reflective elements on the axis. The number of antennas on the base station side is... K Root. Now consider the intelligent reflector rotating about the Z-axis during flight; this rotation angle is called the orientation angle of the intelligent reflector. This angle determines the direction of the reflected wave, ensuring that the reflected signal reaches a specific receiver. It is stipulated that the direction angle is 0 when the intelligent reflective surface is located in the XOZ plane.

[0025] Establish a coordinate system with the base station as the origin. The altitude of the AIRS in the nth time slot is... The AIRS array plane is perpendicular to the XOY plane, and its position is determined by the reference reflection element in the lower left corner. Since the distance between the IRS and the UAV is negligible, the three-dimensional position of the IRS can be equated with the three-dimensional position of the UAV, that is, the position of the AIRS in each time slot is... Therefore, the distance from the base station to AIRS AIRS to the receiving end The distance is In reality, the size of the AIRS is much smaller than the distance between the base station and the AIRS; therefore, the electromagnetic waves arriving at the AIRS can be considered uniform plane waves. The elevation angle of arrival formed by the electromagnetic waves emitted by the base station and the AIRS is... , which is the angle between the electromagnetic wave and the positive z-axis. The azimuth angle of arrival formed by the electromagnetic wave emitted by the base station and the AIRS is . , which is the angle between the electromagnetic wave and the positive x-axis, therefore the receiver array response of AIRS is expressed as According to the principle of arrays, we can obtain:

[0026]

[0027] in , , .

[0028] Similarly, the transmit array response from the base station to the AIRS can be obtained. ; For convenience, let Therefore, the channel matrix from the base station to the AIRS can be expressed as:

[0029]

[0030] in, This refers to the base station's transmission power. Let be the gain of the base station antenna. In the entire wireless communication system, let the size of the AIRS be... Then the size of each small reflective unit is The path loss from the AIRS to the receiver, passing through the m-th reflecting unit, is:

[0031]

[0032] in For receiving antenna gain, This represents the angle between the electromagnetic wave and the positive z-axis, i.e., the angle of incidence when the electromagnetic wave strikes the AIRS. If each reflecting element has a suitable reflection phase such that the electromagnetic waves passing through the AIRS constructively superimpose at the receiver, then the path loss from the AIRS to the receiver is:

[0033]

[0034] use and This indicates that the electromagnetic waves reflected by AIRS reach the receiving end. Given the pitch and azimuth angles, the array reflection response of AIRS can be expressed as:

[0035] in: , , .

[0036] Therefore, the channel matrix from the AIRS to the receiver can be expressed as:

[0037] Therefore, the signal received at the receiving end can be obtained as follows:

[0038] in It is a diagonal phase shift matrix, representing the phase shift of each reflecting unit; It is the signal transmitted by the transmitting end; It is a beamforming vector related to the number of antennas at the transmitting end. ; n This represents additive white Gaussian noise with a mean of 0. The signal-to-noise ratio at the receiver can be expressed by the following formula. ;

[0039] The signal-to-noise ratio at the receiver in each time slot is:

[0040] The overall signal-to-noise ratio of the system is:

[0041] in Phase shift of the reflecting unit Represented as Then, the signal-to-noise ratio at the receiver is a function of the AIRS position vector. The position vector of the receiving end Phase shift of the reflecting unit The orientation angle of the intelligent reflective surface and base station beamforming vector One relevant parameter. To maximize the signal-to-noise ratio at the receiver, this problem can be expressed as:

[0042]

[0043] In this optimization problem, C1 represents the phase shift adjustment range of the IRS; C2 and C3 constrain the flight area of ​​the UAV; C4 limits the flight speed of the UAV; and C5 limits the rotation angle of the AIRS. To ensure that the signal transmitted from the base station can be received by the receiver after passing through the intelligent reflector, the azimuth angle of the intelligent reflector must meet certain constraints, meaning the signal can reach the receiver after reflection by the AIRS. .

[0044] Design an algorithm to solve the optimization problem: Analysis revealed that the optimization problem was a non-convex problem that could not be solved directly. The problem was decomposed into three sub-problems using a block-based iterative approach, and closed-form expressions for the optimal solutions for base station and intelligent reflector beamforming were obtained. For UAV trajectory optimization, the non-convex problem was first approximated into a convex optimization problem by applying the continuous convex approximation method.

[0045] from As can be seen from the expression, the parameters to be optimized are multiplied, and the optimal objective function is a non-convex function. Furthermore, due to the involvement of four parameters, the overall solution complexity is high, and existing convex optimization methods cannot be used to solve it. Next, we will use an alternating optimization method to analyze and solve problem P1 step by step.

[0046] In response to the problem The overall solution approach follows the alternating optimization principle. Regardless of the reflection link from the AIRS to the receiver, the beamforming vector at the base station should point towards the AIRS. For any given AIRS location... Receiver position and AIRS phase shift The optimal base station beamforming vector is given in the literature. When intelligent reflector beamforming... and drone trajectory Once determined, we only need to analyze the objective function that maximizes the signal-to-noise ratio. First, we calculate the channel matrix. The largest eigenvalue is calculated by finding the eigenvector corresponding to that eigenvalue. This vector is the optimal active beamforming vector. . It can be simplified to:

[0047]

[0048] It can be obtained It is a rank-1 matrix, and its eigenvectors are ,and Only with AIRS location This is relevant. At this point, the optimal beamforming vector at the base station has been obtained. Substituting this into problem P1, we can obtain the latest problem P1.1.

[0049]

[0050] at this time It can be written as:

[0051] First, assume the location of the receiver is known. Therefore, problem P1.1 can be further simplified to problem P1.2.

[0052]

[0053] For problem P1.2, when the drone trajectory is fixed, first solve for the optimal phase shift at AIRS:

[0054] At this time, the receiving end The SNR can be written as:

[0055] Therefore, for a given receiver location Its received signal-to-noise ratio depends entirely on the position of the AIRS and the orientation angle of the smart reflector. To maximize Therefore, it is necessary Written This leads to question P1.3.

[0056]

[0057] By observing question P1.3, we can see that it is about... Higher-order polynomial equations This is the position vector of AIRS, and the AIRS altitude variable is... The horizontal position vector is Direction angle .

[0058]

[0059] Therefore, the signal-to-noise ratio at the receiver for each time slot is:

[0060] Therefore, the above problem becomes solving for maximizing the three-dimensional position function of the UAV, i.e., only with respect to the AIRS altitude variable. The horizontal position vector is Direction angle One question:

[0061]

[0062] Iterative algorithm design: Considering that this problem only relates to the three-dimensional position of the AIRS, it is necessary to find a solution that maximizes the signal-to-noise ratio while satisfying all flight area and speed constraints. For such high-order polynomials and non-convex functions, numerical optimization methods are typically required to search for the maximum value while satisfying all given constraints. This invention proposes a genetic algorithm to solve this problem. After initializing the parameters, the population generated in this iteration, i.e., the three-dimensional trajectory of the UAV, is obtained. Then, the corresponding optimal passive beamforming of the intelligent reflector and active beamforming of the base station antenna are calculated. After population selection, crossover, and mutation operations, the values ​​of the parameters are updated for the next iteration until the objective function converges. The specific algorithm steps are as follows:

[0063] 1) Initialization: Set the initial population (set of possible flight path solutions) for the drone's flight trajectory. And the iteration count k=0. Simultaneously, the iteration termination threshold of the algorithm is set. Population size and maximum number of generations.

[0064] 2) Fitness calculation: In solving problem P2, in the k-th iteration, the fitness value of each individual (the UAV flight path solution) is calculated using the fitness function (objective_fitness function). This function reflects the gains under the flight trajectory and direction angle.

[0065] 3) Selection operation: Based on the fitness value of individuals, a selection mechanism is used to select a subset of individuals as parents for the next generation.

[0066] 4) Crossover and Mutation Operations: Perform crossover and mutation operations on the selected parents to produce offspring. Crossover allows two individuals to exchange some of their genes, while mutation randomly changes the values ​​of certain genes to increase population diversity.

[0067] 5) Creating a new population: Replacing some individuals in the original population with offspring obtained through crossover and mutation results in a new generation of population. .

[0068] 6) Nonlinear constraint processing: Obtaining a new generation of population Then, the nonlinear constraint (nonlinear_constraints) function is used to check whether each individual satisfies the nonlinear constraints (including velocity constraints, position constraints, and orientation angle constraints).

[0069] 7) Evaluate and update: Calculate the new population The fitness of the population is compared with the optimal fitness values ​​of the two generations to assess whether the quality of the solution has improved.

[0070] 8) Termination check: Determine if the algorithm has converged, i.e., check if the improvement in fitness value is less than a given termination threshold. If the improvement in the optimal fitness value at the time of pairing does not reach the threshold. Then the number of iterations will be... If the algorithm fails, return to step 2) to proceed to the next iteration. Otherwise, terminate the algorithm.

[0071] 9) Output: Once the algorithm terminates, the final optimized variable value is output, i.e., the UAV's flight trajectory Q. And the maximum target value obtained.

[0072] The technical effects of this invention are as follows: (1) By combining the three-dimensional trajectory of the UAV, the active beamforming of the base station antenna and the passive beamforming of the intelligent reflector, a scheme is proposed that can maximize the signal-to-noise ratio of a single user terminal and improve the communication quality.

[0073] (2) This invention provides a valuable method for realizing emergency communication assisted by unmanned aerial vehicles. Attached Figure Description

[0074] Figure 1 This is a schematic diagram of the process of this invention.

[0075] Figure 2 This is a model diagram of the UAV communication system assisted by the intelligent reflective surface of this invention.

[0076] Figure 3 This is a schematic diagram of the geometric model of the UAV communication system assisted by the intelligent reflective surface of the present invention.

[0077] Figure 4 This refers to the signal-to-noise ratio of each time slot at the receiver when the flight time is 30 minutes, without considering the UAV altitude vector optimization scheme.

[0078] Figure 5 This refers to the signal-to-noise ratio of each time slot at the receiver when the flight time is 30 minutes, considering the UAV altitude vector optimization scheme.

[0079] Figure 6 This is the signal-to-noise ratio of each time slot at the receiver when the flight time is 40 hours, considering the UAV altitude vector optimization scheme. Detailed Implementation

[0080] To better understand the above technical solution, a detailed analysis is provided below in conjunction with the accompanying drawings and specific implementation methods.

[0081] A UAV communication system based on intelligent reflector-assisted communication and a joint optimization method, such as Figure 1 As shown, it includes the following steps: The first step is to make the following specific settings for the parameters: (1) such as Figure 2 The diagram shows a model of a UAV communication system assisted by a smart reflector. Figure 3 The geometric model diagram shown depicts the three-dimensional coordinates of the base station and the ground user (receiver). and The drone's flight altitude range is Horizontal flight area The coordinates of the initial and final points of the drone's position are respectively and The base station's transmit power is set to... The gain of the transmitting antenna is The gain of the receiving antenna is Number of array elements The number of antennas at the base station. (2) Other parameters related to UAV flight are set as flight time slots. Maximum horizontal speed of drone flight Maximum vertical speed of drone flight ,

[0082] The second step is to determine whether to control the altitude vector of the drone's trajectory. Analysis under two optimization schemes: Optimizing the drone trajectory requires adjusting the drone's altitude variable. The horizontal position vector is Optimization is performed by comparing whether or not altitude vectorization is applied to the drone's flight path. The signal-to-noise ratios obtained from the two schemes are compared under control. Figure 4 and Figure 5 The signal-to-noise ratio (SNR) of the receiver for each time slot is given under two optimization schemes (T=30). In comparison, in the two-dimensional trajectory scheme (i.e., for the altitude vector...), In the control scheme, the aircraft maintains the same altitude throughout its journey from the starting point to the destination. After finding the optimal point, the aircraft hovers nearby before reaching the destination. In the three-dimensional trajectory scheme, the aircraft prioritizes descending to find the optimal signal-to-noise ratio (SNR) during its journey. The drone then hovers within this relatively optimal area before finally reaching the destination. In comparison, the two-dimensional trajectory scheme results in a more stable SNR and a higher average SNR at the receiver. However, in the three-dimensional trajectory scheme, the drone finds a point within a time slot that maximizes the receiver's SNR, but this consumes time, resulting in a lower average SNR at the receiver compared to the two-dimensional scheme.

[0083] The third step is to analyze how the system's average signal-to-noise ratio changes with the drone's flight time. Figure 5 and Figure 6 The changes in the signal-to-noise ratio at the receiver are presented by altering the drone's flight time. Figure 5 Figure 6 The figures show the signal-to-noise ratio (SNR) of the receiver in each time slot when the drone's flight time T is 30 and T is 40, respectively. By changing the drone's flight time T, the receiver's average SNR increases with increasing flight time T. This is because as the flight time increases, the drone allocates more time to hovering near better points, thus improving the receiver's average SNR. Furthermore, the general shape of the drone's flight trajectory initially changes significantly at certain points as time increases. This is because when the flight time T is short, the drone needs to meet distance and speed limitations from the starting point to the destination. As the flight time T gradually increases, the general shape of the drone's flight trajectory no longer changes significantly at certain points initially; the drone chooses to hover in areas with better SNR.

[0084] The above-described embodiments are merely illustrative of the implementation methods of the present invention, but should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the protection scope of the present invention.

Claims

1. A UAV communication system and joint optimization method based on intelligent reflector-assisted communication, characterized in that, First, a deployment method for AIRS is proposed, introducing the rotation angle, i.e., the orientation angle, of the intelligent reflector. Second, the three-dimensional position of the UAV, the active beamforming of the base station, and the passive beamforming of the intelligent reflector are combined to describe the signal-to-noise ratio at the user end. An AIRS single-reflection system model is established with the optimization objective of maximizing the signal-to-noise ratio at the receiver. Then, the model is solved and analyzed based on its characteristics.

2. The UAV communication system and joint optimization method based on intelligent reflector-assisted communication as described in claim 1, characterized in that, Specifically, the process includes the following: Step 1: Obtain the parameters of the communication scenario, including the location of the fixed base station, the location s of the ground user, the initial and final positions of the UAV, the flight altitude and speed constraints of the UAV, and the number of reflective units of the smart reflector. A system model is constructed, which includes a discretized trajectory model of the UAV, an orientation angle model of the intelligent reflector, a channel model from the base station to the intelligent reflector and from the intelligent reflector to the user, and a user received signal and signal-to-noise ratio model. Step 2: Establish a joint optimization problem with the goal of maximizing the total received signal-to-noise ratio of the user. The optimization variables of the optimization problem include the three-dimensional trajectory of the UAV, the phase shift matrix of the smart reflector, the beamforming vector of the base station, and the azimuth angle of the smart reflector. The constraints include the phase shift constraint of the smart reflector, the flight area constraint of the UAV, the flight speed constraint of the UAV, and the azimuth angle constraint of the smart reflector. Step 3: The joint optimization problem is decomposed into three sub-problems using an alternating optimization framework, namely, the base station active beamforming optimization sub-problem, the intelligent reflector passive beamforming and azimuth angle joint optimization sub-problem, and the UAV trajectory optimization sub-problem; Solve the active beamforming optimization subproblem of the base station to obtain the optimal base station beamforming vector; Solve the joint optimization subproblem of passive beamforming and orientation angle of the intelligent reflector to obtain the optimal phase shift matrix and orientation angle of the intelligent reflector; To solve the aforementioned UAV trajectory optimization subproblem, a genetic algorithm is used to optimize the UAV's three-dimensional trajectory. Step 4: Iteratively optimize the three sub-problems until the convergence condition is met, and output the optimal UAV trajectory and intelligent reflector orientation angle.

3. The UAV communication system and joint optimization method based on intelligent reflector-assisted communication according to claim 2, characterized in that, The specific method for system initialization and model establishment in step 1 is as follows: Obtain fixed parameters of the communication scenario, including base station coordinates, ground user location, UAV start and end points, flight altitude and speed constraints, and the number of intelligent reflector units; calculate the total communication time. Discretize into There are 1 time slot, and the length of each time slot is 1. ; The drone trajectory is represented as a sequence of time slot locations. ; Establish a system model and define the three-dimensional position of the UAV. and the orientation angle of the intelligent reflective surface ;in The horizontal position For height; construct the base station to smart reflector channel and intelligent reflector to user channel And derive the user reception signal-to-noise ratio model. .

4. The UAV communication system and joint optimization method based on intelligent reflector-assisted communication according to claim 2, characterized in that, The specific method for establishing the joint optimization problem in step 2 is as follows: The optimization objective is to maximize the total received signal-to-noise ratio for the user. ; Optimization variables include drone trajectory sequences Intelligent reflector phase shift matrix Base station beamforming vector and intelligent reflective surface orientation angle sequence ; The constraints include: intelligent reflector phase shift constraint; UAV flight area constraint; UAV flight speed constraint; intelligent reflector orientation angle constraint.

5. The UAV communication system and joint optimization method based on intelligent reflector-assisted communication according to claim 2, characterized in that, The specific method for implementing the alternating optimization framework in step 3 is as follows: Step 3.1 Base station active beamforming optimization; Fixed drone trajectory Intelligent reflector phase shift and direction angle Optimize base station beamforming vector Calculate the equivalent channel matrix The largest eigenvalue corresponds to the eigenvector, from which the optimal beamforming vector is obtained. ,in It is the base station transmit array response vector. It refers to the number of base station antennas; Substitute the solution into the original problem to eliminate variables. ; Step 3.2 Joint optimization of passive beamforming and azimuth angle of intelligent reflector; Fixed drone trajectory and optimal base station beamforming Jointly optimize the phase shift matrix of the intelligent reflector. and direction angle ; Calculate the optimal phase shift value Compensation for signal phase difference; where , It is the reflection unit index; within the constraint interval Internal optimization orientation angle make Maximum; of which and It is the azimuth angle related to the relative positions of the user and the base station; Step 3.3 Drone trajectory optimization; Optimizing the 3D position of the UAV using a genetic algorithm and direction angle This includes: initializing the population that satisfies the constraints; and using the total signal-to-noise ratio. Evaluate the trajectory for the fitness function; perform selection, crossover, and mutation operations; handle nonlinear constraints; generate a new generation of population and iterate until convergence.

6. The UAV communication system and joint optimization method based on intelligent reflector-assisted communication according to claim 2, characterized in that, The specific method for iterative optimization and result output in step 4 is as follows: Implement alternating optimization iterations: initialize all optimization variables; sequentially solve for the optimal base station beamforming. Intelligent reflective surface parameters and Drone trajectory superscript Indicates the first The solution of the next iteration; compare the improvement in the overall objective function value with the threshold. Determine if convergence is achieved; output the optimal solution. , , and subscript This represents the optimal solution.

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