Unmanned aerial system optimization method, electronic device, medium, and computer program product

Abstract

This embodiment discloses an unmanned aerial vehicle (UAV) system optimization method, electronic device, medium, and computer program product. The UAV system optimization method includes: constructing an optimization model for the UAV system based on its secure communication rate and energy consumption; wherein the value of the optimization model is positively correlated with the secure communication rate and negatively correlated with the energy consumption; constructing constraint conditions for the optimization model based on constraint parameters; wherein the constraint parameters include one or more of the following: communication parameters of the UAV system's intelligent reflector (IRS), beam parameters of the UAV base station, and movement parameters of the UAV; combining the constraint conditions, determining the optimal solution of the optimization model with the objective of maximizing its value; and optimizing the UAV system based on the optimal solution.

Description

Technical Field

[0001] This application belongs to the field of wireless communication technology, and in particular relates to an unmanned aerial vehicle (UAV) system optimization method, electronic device, medium, and computer program product. Background Technology

[0002] With the rapid development of wireless communication technology, unmanned aerial vehicle (UAV) systems are playing an increasingly important role in communication. However, how to optimize UAV systems more effectively and enable them to operate at their optimal state remains a key challenge in current research. Summary of the Invention

[0003] This application provides a method, electronic device, medium, and computer program product for optimizing an unmanned aerial vehicle (UAV) system. By combining the UAV system's secure communication rate and energy consumption, the optimization of the UAV system helps to balance the requirements for UAV flight energy consumption and secure communication rate through reasonable optimization strategies and measures, thereby improving the optimization effect of the UAV system.

[0004] This application provides a method for optimizing an unmanned aerial vehicle (UAV) system, the method comprising: Based on the secure communication rate and energy consumption of the UAV system, an optimization model for the UAV system is constructed; wherein the value of the optimization model is positively correlated with the secure communication rate and negatively correlated with the energy consumption. The constraints of the optimization model are constructed based on the constraint parameters; wherein the constraint parameters include one or more of the following: the communication parameters of the intelligent reflector IRS of the UAV system, the beam parameters of the UAV base station, and the movement parameters of the UAV. Based on the constraints, and with the objective of maximizing the value of the optimization model, the optimal solution of the optimization model is determined; The unmanned aerial vehicle system is optimized based on the optimal solution.

[0005] This application provides an electronic device, which includes a processor and a memory for storing computer programs capable of running on the processor; wherein, The processor is used to run the computer program to perform any of the above-described unmanned aerial vehicle (UAV) system optimization methods.

[0006] This application provides a computer storage medium storing a computer program that, when executed by a processor, implements any of the above-described unmanned aerial vehicle (UAV) system optimization methods.

[0007] This application provides a computer program product, including a computer program that, when executed by a processor, implements any of the above-described unmanned aerial vehicle (UAV) system optimization methods.

[0008] This application provides an unmanned aerial vehicle (UAV) system optimization method, electronic device, medium, and computer program product. Based on the UAV system optimization method provided in this application, firstly, the optimization model construction process incorporates a comprehensive consideration of the UAV system's communication performance and flight energy efficiency. Secondly, constraints are constructed based on the communication parameters of the UAV system's IRS, the beam parameters of the UAV base station, and the UAV's movement parameters, ensuring that the optimization model can take into account various practical operational factors. Finally, by combining the constraints and aiming to maximize the value of the optimization model, the optimal solution is obtained. Based on this optimal solution, the UAV system is optimized, achieving a balance between safe communication rate and energy consumption, improving the overall energy efficiency and communication stability of the UAV system, and realizing comprehensive optimization of the UAV system. Attached Figure Description

[0009] Figure 1 This is a flowchart of an unmanned aerial vehicle (UAV) system optimization method provided in an embodiment of this application; Figure 2 This is a schematic diagram of an unmanned aerial vehicle (UAV) system provided in an embodiment of this application; Figure 3 This is a flowchart of another unmanned aerial vehicle (UAV) system optimization method provided in an embodiment of this application; Figure 4 This is a schematic diagram of the first simulation result provided in the embodiment of this application; Figure 5 This is a schematic diagram of the second simulation result provided in the embodiments of this application; Figure 6 This is a schematic diagram of the structure of an unmanned aerial vehicle (UAV) system optimization device provided in an embodiment of this application; Figure 7 This is a schematic diagram of the composition structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0010] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0011] Currently, there is a lack of comprehensive optimization methods for unmanned aerial vehicle (UAV) systems. Common optimization approaches, aimed at improving UAV system performance, often prioritize higher secure communication rates without considering the impact of limited flight energy consumption. However, in practical applications, UAV flight energy consumption and secure communication rates must be considered holistically. On one hand, reducing UAV flight energy consumption can extend its endurance and improve mission efficiency; on the other hand, improving secure communication rates ensures the security and integrity of communication data, guaranteeing the successful completion of missions.

[0012] The embodiments of this application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the embodiments provided herein are merely illustrative of the embodiments of this application and are not intended to limit the embodiments of this application. Furthermore, the embodiments provided below are some embodiments for implementing this application, and not all embodiments for implementing this application. Unless otherwise specified, the technical solutions described in the embodiments of this application can be implemented in any combination.

[0013] It should be noted that, in the embodiments of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a method or apparatus that includes a list of elements includes not only the elements expressly described, but also other elements not expressly listed, or elements inherent to implementing the method or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of other related elements in the method or apparatus that includes that element (e.g., steps in the method or units / modules in the apparatus; for example, units / modules in the apparatus may be portions of circuitry, processors, programs, or software, etc.).

[0014] The unmanned aerial vehicle (UAV) system optimization method provided in this application includes a series of steps. However, the UAV system optimization method provided in this application is not limited to the steps described. Similarly, the UAV system optimization device provided in this application includes a series of modules. However, the device provided in this application is not limited to the modules explicitly described. It may also include modules that need to be set up for obtaining relevant information or processing based on information.

[0015] Figure 1 A flowchart of an unmanned aerial vehicle (UAV) system optimization method is shown. Figure 1 The optimization methods for the unmanned aerial vehicle (UAV) system shown include: Step 101: Based on the secure communication rate and energy consumption of the UAV system, construct an optimization model for the UAV system.

[0016] Among them, the value of the optimization model is positively correlated with the secure communication rate and negatively correlated with energy consumption.

[0017] In the era of 6G mobile networks, the combination of drones and intelligent reflective surfaces (IRS) has shown great potential and application prospects. However, IRS-assisted drone systems still face some technical challenges. First, due to the broadcast characteristics of wireless channels, the communication security of drone systems is difficult to guarantee. Second, in real-world scenarios, due to the non-ideal characteristics of the transceiver hardware (such as transmitter modules, receiver modules, and antenna systems) in drone systems, damage to the transceiver hardware is unavoidable. Common transceiver hardware damage includes phase noise and carrier frequency offset.

[0018] In this embodiment, the unmanned aerial vehicle (UAV) system can consist of a UAV, a communication module, and a control platform. To adapt to 6G communication scenarios, the UAV system may also include an IRS (Infrared Reflector Controller). The IRS can be a passive device composed of a large number of programmable reflective elements, which can enhance the signal propagation path by adjusting the phase offset of each reflective element. The IRS can be deployed on the ground to assist the communication link between the UAV and ground users, improving the communication quality and security performance of the UAV system.

[0019] The secure communication rate of an unmanned aerial vehicle (UAV) system represents the maximum rate at which the system can stably transmit data while ensuring that the communication content is not leaked. The energy consumption of an UAV system can include flight energy consumption, auxiliary equipment energy consumption, and communication energy consumption, among which flight energy consumption is the largest source of energy consumption for the entire UAV system.

[0020] An optimization model based on the determination of secure communication rate and energy consumption can be used to measure the communication quality difference between the first and second users in an unmanned aerial vehicle (UAV) system, while also reflecting the energy consumption of the UAV system. The optimization model not only considers the data transmission bit rate but can also incorporate channel security indicators, such as signal strength comparison and interference suppression capability. Here, the first user represents the user legitimately receiving the signal, and the second user represents the user attempting to intercept the first user's signal. For example, in a typical application scenario, the optimization model might use the difference between the first user's signal-to-noise ratio (SNR) and the second user's SNR as part of the objective function for the secure communication rate, thereby ensuring communication security.

[0021] In this embodiment, the maximum data transmission rate can be determined based on the difference between the channel capacity of the first user and the channel capacity of the second user, while ensuring communication security, thus determining the secure communication rate. The secure communication rate can be determined based on the signal-to-noise ratio difference between the first user and the second user. Figure 2 A schematic diagram of a drone system is shown. The drone system in this embodiment includes an IRS and is equipped with... A drone with one antenna; where the IRS is used to assist drone communication. In practical applications, the drone system may also include a first user and a second user, which can be fixed base stations configured with a single antenna.

[0022] Combination Figure 2 The schematic diagram of the unmanned aerial vehicle (UAV) system shown illustrates that, in the nth time slot, the channel gain from the UAV to the IRS can be expressed as: ,in, In the nth time slot, the channel gain from the UAV to the first user can be expressed as: , In the nth time slot, the channel gain from the IRS to the first user can be expressed as: , In the nth time slot, the channel gain from the UAV to the second user can be expressed as: , In the nth time slot, the channel gain from the IRS to the second user can be expressed as: , Where M represents the total number of reflective elements in the IRS. , , This represents datasets with different dimensions.

[0023] Here, we assume the horizontal position coordinates of the first user are... The horizontal coordinates of the second user are The height at which the IRS is placed is The horizontal position coordinates are The drone's flight altitude is fixed at Assuming the channel between the UAV and the IRS consists only of the line-of-sight (LoS) component, then: (1) (2) in, Indicates reference distance Channel power gain at that time This represents the path loss parameter from the drone to the IRS. Indicates the distance between the drone and the IRS. Indicates the horizontal position of the drone. and The array responses at the receiving end (UAV to IRS) and the transmitting end (UAV) can be represented as follows: (3) (4) (5) in, Indicates the carrier wavelength. and These represent the horizontal and vertical spacing between the various reflective elements in the IRS, respectively. This indicates the angles of arrival (AoA) of the drone to the IRS. This indicates the azimuth angle of the drone to the IRS. This indicates the number of reflective elements in the IRS in the x-direction. This indicates the number of reflective elements in the IRS in the z-direction; , ; Represents the x-axis component of the UAV in its horizontal position; symbol It represents the Kronecker product.

[0024] (6) in, This refers to the drone's angles of departure (AoD). Indicates the distance between the drone's antenna elements. Indicates the number of antenna units carried by the drone; , This represents the position component of the UAV in the y-direction at a horizontal position.

[0025] The channel between the UAV, IRS, and ground users (first user, second user) is modeled as a Ricean channel model. The channel gain between the UAV and ground users is also considered. Channel gain between IRS and ground users This can be expressed based on the following formula, where It can be or , It can be or That is, i can be replaced with e or l.

[0026] (7) (8) in, This represents the path loss index from the drone to the ground user. When i is l, This represents the path loss exponent from the drone to the first user; when i is e, This represents the path loss index from the drone to the second user; This represents the path loss index from the IRS to the ground user. Indicates the distance between the IRS and the ground user. include or That is, when i is l, This represents the distance between the IRS and the first user, where i is e. Indicates the distance between the IRS and the second user; This represents the distance between the UAV and the ground user. When i is l, This represents the distance between the UAV and the first user, where i is e. Indicates the distance between the UAV and the second user; The Ricean fading factor represents the channel distance from the drone to the ground user. This represents the Ricean fading factor of the channel from the IRS to the ground user. This represents the non-line-of-sight (NLoS) path component of the channel from the UAV to the ground user. The NLoS path components of the channel from the IRS to the ground user are represented by complex Gaussian distributions. and . This represents the LOS component of the channel from the drone to the ground user. The LOS component of the channel from the IRS to the ground user can be represented as: (9) in, Indicates the departure angle of the drone. , This represents the position component of the ground user in the y-direction.

[0027] (10) (11) (12) in, Indicates the elevation angle away from the IRS. Indicates the azimuth angle away from the IRS. , , This represents the position component of the ground user in the x-direction.

[0028] Combination Figure 2The illustrated diagram of the UAV system shows that the received signal of the first user can be determined by considering the transceiver hardware damage of the first user's receiving device. Similarly, the received signal of the second user can be determined based on the transceiver hardware damage of the second user's receiving device. The communication rate of the first user is determined based on the received signal of the first user, and the communication rate of the second user is determined based on the received information of the second user. Finally, the safe communication rate of the UAV system is determined based on the communication rates of the first and second users.

[0029] Specifically, considering transceiver hardware damage, the first user's received signal It can be represented as: (13) in, ; , It is a one-dimensional data matrix; The beam parameters of the UAV base station can be specifically the beamforming vector of the UAV base station. The beamforming vector is a weighted vector used in antenna array systems. By adjusting the signal phase and amplitude of each antenna element, the direction and shape of the transmitted beam can be controlled. This indicates the signal sent from the drone's base station to the first user. Expectations to be met . The vector representing the transceiver hardware impairment of the drone base station. , follows a distribution ,in, The covariance of noise representing hardware damage to the transceiver of a drone base station. This represents the ratio of distortion noise power to the transmit power of the drone base station; This represents a diagonal matrix whose diagonal terms are the diagonal elements of matrix X. The noise represents the hardware impairment noise of the transceiver at the first user's location, and follows a distribution. ,in, It represents the ratio of distorted noise power to undistorted legitimate signal power. The additive white Gaussian noise corresponding to the channel of the first user has a mean of 0 and a variance of . The complex Gaussian distribution. This represents the phase shift matrix of the IRS. ,in, , which represents the controllable phase offset of each reflective element on the IRS surface.

[0030] Based on formula (13), formula (14) shows the received signal of the second user. Calculation method: (14) in, , The noise representing hardware impairment at the transceiver of the second user is distributed as follows: ,in, It represents the ratio of distorted noise power to undistorted legitimate signal power. The additive white Gaussian noise corresponding to the channel of the second user has a mean of 0 and a variance of . The complex Gaussian distribution.

[0031] Based on the received signal of the first user Second user's received signal Establish the secure communication rate expression for the UAV system, where, in addition... , The first user's communication rate It can be represented as: (15) The communication rate of the second user It can be represented as: (16) in, This represents the noise on the first user side. This represents the noise on the second user side. Based on formulas (15) and (16), the secure communication rate in this embodiment is... It can be determined based on formula (17): (17) in, When the communication rate of the second user is greater than that of the first user, the drone's transmit power is set to 0. .

[0032] Based on the above discussion, in an unmanned aerial vehicle (UAV) system, energy consumption mainly comes from the UAV's propulsion energy and the energy consumed during communication. Typically, the energy consumption for UAV propulsion is much greater than the power consumption during communication. Therefore, this embodiment uses UAV propulsion energy consumption to represent the overall energy consumption of the UAV system. For example, the energy consumption of the UAV system can be established by discretizing the UAV's flight time. The calculation formula is as follows: (18) in, This indicates the drone's flight speed in each time slot. , These are two parameters related to the drone's weight, wing area, and air density.

[0033] After establishing the formulas for calculating the secure communication rate and energy consumption of the UAV system, an optimization model for the UAV system can be constructed based on these formulas. The optimization model can be expressed as follows: (19) in, , representing the discrete set of time slots. It can be seen that the optimization model reflects the secure communication rate and energy consumption of the UAV system, and can represent the system's secure communication energy efficiency.

[0034] Step 102: Construct the constraints of the optimization model based on the constraint parameters.

[0035] The constraint parameters include one or more of the following: the communication parameters of the IRS of the UAV system, the beam parameters of the UAV base station in the UAV system, and the movement parameters of the UAV in the UAV system.

[0036] In practical applications, constraints are used to ensure that the optimization model is solved within a physically feasible range. For example, the communication parameters of the IRS in an unmanned aerial vehicle (UAV) system may include the phase shift matrix of the IRS. The constraints of the optimization model can be determined based on the phase shift matrix of the IRS. For instance, the phase of each reflector in the IRS can only take values ​​within a specific range, and the phase shift matrix can be determined based on the range of values ​​for the phase of each reflector in the IRS.

[0037] Constraints on the optimization model can also be determined based on the beam parameters of the UAV base station. These parameters may include transmit power, beamforming vector, etc., and are limited by hardware capabilities and relevant regulations. Constraints can also be determined based on the UAV's movement parameters, which may include its flight trajectory. The flight trajectory can be determined based on the UAV's speed, altitude, and horizontal position, and is typically restricted by flight time, energy consumption, and airspace management rules. By constructing constraints, it can be ensured that the results of the optimization model do not exceed the actual boundaries of the UAV system's operation.

[0038] Based on the optimization model of formula (19), constraints can be established for the optimization model according to the actual situation and usage requirements of the UAV system. For example, the constraint parameters may include one or more of the following: the position of the UAV, the flight speed of the UAV, the phase shift matrix of the IRS, and the beamforming vector of the UAV base station; among them, the beamforming vector of the UAV base station represents a set of antenna parameters used to implement beamforming technology on the UAV base station, including phase, amplitude and direction information, which are key factors that determine the signal propagation direction and coverage.

[0039] For example, for the optimization model of formula (19), the constraints constructed based on the constraint parameters may include one or more of the following: first sub-constraint C1, second sub-constraint C2, and third sub-constraint C3.

[0040] The first sub-constraint C1 can be a constraint constructed based on the drone's movement parameters. For example, the first sub-constraint C1 could be: (20) (twenty one) (twenty two) (twenty three) In this case, assuming the drone flies at a fixed altitude... This indicates the horizontal position of the drone in the nth time slot. When n is 1, it represents the initial horizontal position of the drone during flight; when n is N, it represents the horizontal position of the drone when it ends flight. This represents the flight speed in the nth time slot. The unit time slot is represented. It can be seen that formulas (20) to (23) constrain the position, initial flight position, final flight position and flight speed of the UAV. Indicates the initial position constraints. Indicates the initial velocity. Indicates the initial position; Indicates the final position constraint. Indicates the final position; This represents the maximum or limited flight speed of the UAV. It can be seen that formulas (20) to (23) provide constraints on the flight trajectory of the UAV.

[0041] The second sub-constraint C2 can be a constraint constructed based on the beam parameters of the UAV base station, specifically reflected in the transmit power of the UAV base station. For example, the second sub-constraint C2 could be: (twenty four) in, This represents the beamforming vector of the drone base station. This indicates the maximum transmit power of the drone base station or a limitation on the maximum transmit power of the drone base station. It can be seen that the transmit power of the drone base station can be limited through the second sub-constraint C2.

[0042] The third sub-constraint C3 can be a constraint constructed based on the communication parameters of the IRS. Based on the above method, the third sub-constraint can be a constraint constructed based on the phase shift matrix of the IRS. The phase shift matrix of the IRS can be expressed as: (25) in, , This indicates the controllable phase offset for each reflective element on the IRS surface.

[0043] Based on formula (25), the third sub-constraint C3 can be: (26) Formulas (20) to (26) above give the constraints constructed based on different constraint parameters. In practical applications, the constraints can be selected and adjusted based on specific needs.

[0044] Step 103: Combining the constraints, with the goal of maximizing the value of the optimization model, determine the optimal solution of the optimization model.

[0045] Based on the method given in step 102, after determining the constraints, the goal is to maximize the value of the optimization model. Within the range of constraints, the optimal solution of the optimization model can be determined. For example, among the constraints given in formulas (20) to (26) above, any one or more can be selected to constrain the solution of the optimization model according to actual needs. Under the constraints, the optimal solution that can improve the secure communication rate and reduce energy consumption can be determined.

[0046] In practical applications, finding the optimal solution depends on the selection and implementation of the optimization algorithm. Since the optimization problem involved in this embodiment is typically non-convex, meaning the objective function or constraints of the optimization model are usually non-convex and do not satisfy the convexity requirement, numerical optimization methods, such as semidefinite relaxation (SDR), successful convex approximation (SCA), and Dinkelbach transformation, can be used to process the objective function or constraints of the optimization model, making the processed objective function or constraints satisfy the convexity requirement and thus enabling the determination of the optimal solution.

[0047] For example, in the problem of optimizing the flight trajectory of a drone, the objective function of the optimization model can be constrained based on the first sub-constraint C1. By introducing auxiliary variables and relaxing constraints, the objective function of the optimization model can be transformed into a series of sub-problems of convex functions for solution. The optimal velocity and optimal position that satisfy the first sub-constraint C1 can be determined by finding the optimal solution.

[0048] Alternatively, when the objective function of the optimization model is constrained by the first sub-constraint C1, the second sub-constraint C2, and the third sub-constraint C3, the optimal mobility parameters, optimal IRS communication parameters, and optimal UAV base station beam parameters obtained under the constraints can be determined by solving the optimal solution. This allows the optimization model to achieve its maximum value based on the optimal mobility parameters, optimal IRS communication parameters, and optimal UAV base station beam parameters.

[0049] Taking the optimization model as shown in formula (19), and the constraints including the first sub-constraint C1, the second sub-constraint C2, and the third sub-constraint C3 mentioned above as an example, the objective function corresponding to maximizing the optimization model can be: (27) in, This represents the phase shift matrix of the IRS. This represents the beamforming vector of the drone base station. and These represent the drone's flight position and speed, respectively.

[0050] Step 104: Optimize the UAV system based on the optimal solution.

[0051] Once the optimal solution is determined, adjustments can be made to the UAV system based on it. In practical applications, applying the optimal solution means adjusting the configuration of the UAV system to achieve optimal energy efficiency and communication security. For example, adjusting the UAV's horizontal position and speed according to the optimal solution can minimize energy consumption while ensuring communication quality; adjusting the phase shift matrix of the IRS can enhance the signal strength received by the first user while weakening the signal quality received by the second user. Furthermore, the beamforming vector of the UAV base station can be adjusted according to the optimal solution to further improve communication efficiency and anti-interference capabilities.

[0052] In summary, the UAV system optimization method provided in this application firstly demonstrates the impact of transceiver hardware damage on the UAV system by constructing an optimization model, thus clarifying the direction of optimization. Secondly, constraints are established by combining various key parameters (such as the IRS phase shift matrix, UAV movement parameters, beamforming vector, etc.) to ensure that the optimization process is realistically feasible. Next, the optimal solution is obtained using an optimization algorithm. Finally, the optimal solution is applied to the actual configuration of the UAV system, achieving a dual improvement in communication quality and energy efficiency, forming a closed-loop optimization mechanism that can continuously adapt to complex and ever-changing communication environments.

[0053] In practical applications, steps 101 to 104 can be implemented based on a processor, which can be at least one of the following: Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), Central Processing Unit (CPU), Controller, Microcontroller, and Microprocessor.

[0054] Based on the UAV system optimization method given in the above embodiments, in order to optimize the UAV's movement parameters and reduce the UAV system's energy consumption, in some embodiments, the above constraints include a first sub-constraint condition; the above constraints for constructing the optimization model based on the constraint parameters include: constructing the first sub-constraint condition based on the UAV's movement parameters; the above determination of the optimal solution of the optimization model by combining the constraints and aiming to maximize the value of the optimization model includes: constructing a first objective function with the aim of maximizing the value of the optimization model; transforming the first objective function into a second objective function based on the Tinkelbach transform; and determining the optimal solution of the optimization model based on the second objective function and the first sub-constraint condition.

[0055] The movement parameters of a UAV refer to the dynamic set of information describing its flight state, specifically including two dimensions: position and velocity. Position represents the UAV's spatial coordinates at a given moment, typically composed of horizontal coordinates (x, y) and altitude (z), used to represent the UAV's flight trajectory. Velocity represents the UAV's rate of motion within a given time slot, including horizontal and vertical velocity. By incorporating the UAV's movement parameters into the constraint construction, the actual flight behavior of the UAV can be more accurately reflected, thereby improving the accuracy of UAV system modeling. To reduce the complexity of the first sub-constraint, the UAV's altitude can be assumed to be fixed, i.e., altitude and vertical velocity are not considered. The UAV's movement parameters can then include the UAV's position and corresponding velocity in each time slot.

[0056] In this embodiment, by modeling the position and velocity of the UAV in each time slot, the constructed first sub-constraint can effectively reflect the feasibility and energy consumption characteristics of the flight path corresponding to the movement parameter. For example, in a specific communication task, the UAV's movement parameters may need to fly along a preset trajectory while ensuring that the communication quality is not interfered with. In this case, if the actual flight speed and position changes corresponding to the UAV's movement parameters are not taken into account, the constructed constraint may become too idealistic and thus fail to match the complex situations in real-world scenarios.

[0057] Furthermore, incorporating the drone's movement parameters into the constraints can enhance the system's robustness. For example, in the presence of sudden wind speeds or terrain obstacles, relying solely on static trajectory constraints may prevent the drone from completing its mission. However, by introducing real-time position and velocity parameters, the constraints can be dynamically adjusted based on the current environment, enabling more flexible optimization decisions.

[0058] In this embodiment, the objective function is set to maximize the optimization model, that is, maximize the secure communication rate and reduce the energy consumption of the UAV system. This objective function allows the optimization process to not only focus on the security of the UAV system's communication, but also take into account the rationality of the UAV system's energy consumption.

[0059] To obtain the optimal solution of the optimization model, the first sub-constraint can be used as input to optimize the solution process. During the solution process, by introducing slack variables and using the convex approximation algorithm (SCA), the optimization model and non-convex optimization problem can be transformed into multiple solvable convex subproblems, thereby gradually approaching the global optimum.

[0060] In practical applications, the optimal solution can be determined by combining the first sub-constraint and other constraint parameters, such as the communication parameters of the IRS and the beam parameters of the UAV base station. Alternatively, to simplify the solution process, the communication parameters of the IRS and the beam parameters of the UAV base station can be fixed, and the optimal solution of the optimization model can be determined solely by the first sub-constraint.

[0061] The Tinkelbach transform is a mathematical method for handling fractional programming problems. It transforms the original nonlinear fractional objective function into an equivalent parametric form, facilitating subsequent solution. In this embodiment, the Tinkelbach transform is applied to the joint optimization problem of UAV flight trajectory and velocity. The fractional expression in the first objective function of the optimization model is transformed into a linear objective function related to auxiliary variables, i.e., the second objective function. This allows the originally complex non-convex problem to be transformed into a series of convex subproblems for iterative solution. The Tinkelbach transform not only preserves the optimal solution characteristics of the original problem but also significantly reduces the solution difficulty and improves the algorithm's convergence speed.

[0062] After completing the Tinkelbach transform and obtaining the second objective function, the optimization process of the optimization model can be transformed into an optimization process of the second objective function by introducing slack variables and combining them with the Continuous Convex Approximation (SCA) method. This gradually transforms the non-convex constraints in the optimization problem into a series of convex constraints, thereby achieving iterative solution of the optimization model. Specifically, slack variables can be introduced and combined with the Continuous Convex Approximation (SCA) method. First, a first-order Taylor expansion is performed on the non-convex terms in the optimization problem to approximate them as convex functions. Then, an alternating optimization approach is used to successively approximate the optimal solution. Finally, convex optimization solvers such as the Convex Programming Toolbox (CVX) can be used to solve the optimization model and obtain the optimal solution.

[0063] This embodiment effectively addresses the non-convexity of the original optimization problem by introducing slack variables and the Continuous Convex Approximation (SCA) method, enabling efficient solution of the complex first objective function and constraints under limited computational resources. Introducing slack variables and the SCA method not only improves solution efficiency but also ensures the accuracy of the results, thereby achieving coordinated optimization of the UAV flight trajectory, beamforming, and IRS phase shift matrix.

[0064] In this embodiment, a secure communication energy efficiency optimization model suitable for IRS-assisted unmanned aerial vehicle (UAV) systems is constructed by combining the Tinkelbach transform and continuous convex approximation. This method effectively handles non-convex optimization problems, thereby improving the communication performance and anti-interference capabilities of UAV systems and enabling them to adapt to more complex and dynamic wireless communication environments.

[0065] In summary, this embodiment provides an effective optimization method for solving the joint optimization problem involving flight trajectory, beamforming, and IRS configuration in UAV systems. The method combining Tinkelbach transform and continuous convex approximation linearizes the original fractional objective function and handles non-convex constraints using the continuous convex approximation method. This combination improves the solvability of the optimization model and enhances the practical deployment capability of the joint optimization problem involving flight trajectory, beamforming, and IRS configuration in UAV systems.

[0066] Combining the first sub-constraint C1 given in the above formula with the optimization model, we can construct the first objective function with the goal of maximizing the value of the optimization model: (28) The movement parameters of the UAV can be optimized using the first objective function and the first sub-constraint given by formula (28).

[0067] It can be seen that formula (28) is a fractional optimization problem. Based on the method given in this embodiment, and based on the Tinkelbach transform, the first objective function in formula (28) is transformed into the second objective function: (29) in, A value greater than 0 indicates an auxiliary variable. Assuming in... If the optimal solution can be obtained under the following circumstances, then The value at the optimal solution is: (30) Regarding energy consumption Because of the fractions in it It is non-convex, therefore slack variables are introduced. Processing the fractions in the second objective function Then the expression for the drone's flight energy consumption can be transformed into: (31) At the same time, constraints are introduced into formula (31). .

[0068] When optimizing the movement parameters of the UAV, the position and velocity variables of the UAV in each time slot are first considered in the second objective function. , Separate it out and extract the second objective function. Represented as: (32) Among them, in formula (32) , , , , , , , j represents the number of iterations. , . , Determined based on formula (7), , It can be determined based on formula (8). It can be determined based on formula (2).

[0069] , , , They are represented as follows: (33) It can be seen that the above function is still about , Since it is a non-concave function, the more difficult-to-handle variables in the expression can be relaxed into the constraints by introducing slack variables, and then the SCA method can be used to convert the non-convex constraints into convex constraints.

[0070] Subsequently, based on the above processing method, the non-convex optimization problem of UAV movement parameters can be transformed into a convex optimization problem, which can then be optimally solved by CVX.

[0071] Based on the methods given in the above embodiments, in order to reduce the complexity of determining the optimal solution, in some embodiments, the determination of the optimal solution of the optimization model based on the second objective function and the first sub-constraint includes: determining the first communication parameter and the first beam parameter; wherein, the first communication parameter is any communication parameter of the IRS, and the first beam parameter is any beam parameter of the UAV base station; and determining the optimal solution of the optimization model based on the second objective function, the first sub-constraint, the first communication parameter, and the first beam parameter.

[0072] In this embodiment, the communication parameters of the IRS in the current UAV system can be determined as the first communication parameters, and the beam parameters of the current UAV base station can be determined as the first beam parameters. That is, based on the fixed communication parameters of the IRS and the beam parameters of the UAV base station, under the first sub-constraint, the optimal movement parameters are searched to maximize the value of the optimization model.

[0073] To further optimize the beam parameters of the UAV base station, in some embodiments, the above constraints include a second sub-constraint; the constraints for constructing the optimization model based on the constraint parameters include: constructing a second sub-constraint based on the beam parameters of the UAV base station; the above-mentioned combination of constraints to determine the optimal solution of the optimization model with the objective of maximizing the value of the optimization model includes: determining a second communication parameter and a first mobility parameter; wherein the second communication parameter is any communication parameter of the IRS, and the first mobility parameter is any mobility parameter of the UAV base station; and the optimal solution of the optimization model is determined based on the second sub-constraint, the second communication parameter, and the first mobility parameter with the objective of maximizing the value of the optimization model.

[0074] Corresponding to the above embodiments, when optimizing the beam parameters of the UAV base station, in order to reduce computational complexity, the communication parameters of the IRS can be fixed to obtain the second communication parameters, and the movement parameters of the UAV can be fixed to obtain the first movement parameters. In this embodiment, the second communication parameters can be the communication parameters of the IRS in the current UAV system, and the first movement parameters can be the movement parameters of the currently flying UAV. The second communication parameters can be the same as or different from the first communication parameters described above.

[0075] Based on the optimization models given in the above embodiments, as shown in formulas (18) and (19), it can be seen that the beam parameters of the UAV base station only affect the secure communication rate in the optimization model, and in practical applications, the beam parameters of the UAV base station have a relatively small impact on the energy consumption of the UAV system. Therefore, maximizing the value of the optimization model can be transformed into maximizing the secure communication rate, and the third objective function can be: (34) By transforming the third objective function mentioned above and introducing slack variables... , , , Through slack variables , , , By constraining each parameter in the third objective function, we obtain a new constraint C4: (35) in, , ;

[0076] It can be seen that, due to the presence of non-convex constraints in the new constraint C4, such as , The corresponding constraints can therefore be transformed into convex constraints using the SCA method. At the initial point... At this point, the following formula holds true: (36) (37) in, , , express exist The derivative, express exist The derivative of .

[0077] Based on the above formula transformation, the optimal solution problem of the third objective function can be transformed into a positive semi-definite problem. Then, the optimal solution of the third objective function can be solved based on CVX to optimize the beam parameters of the UAV base station.

[0078] To further optimize the communication parameters of the IRS, in some embodiments, the above constraints include a third sub-constraint; the constraints for constructing the optimization model based on the constraint parameters mentioned above include: constructing a third sub-constraint based on the communication parameters of the IRS; the above combination of constraints, with the objective of maximizing the value of the optimization model, to determine the optimal solution of the optimization model includes: determining a second movement parameter and a second beam parameter; wherein, the second beam parameter is any beam parameter of the UAV base station; the second movement parameter is any movement parameter of the UAV base station; based on the third sub-constraint, the second movement parameter, and the second beam parameter, with the objective of maximizing the value of the optimization model, to determine the optimal solution of the optimization model.

[0079] Corresponding to the above embodiments, in order to reduce computational complexity when optimizing the communication parameters of the IRS, the beam parameters of the UAV base station can be fixed to obtain the second beam parameters, and the movement parameters of the UAV can be fixed to obtain the second movement parameters. In this embodiment, the second beam parameters can be the beam parameters of the current UAV base station, and the second movement parameters can be the movement parameters of the currently flying UAV. The second beam parameters can be the same as or different from the first beam parameters described above. The second movement parameters can be the same as or different from the first movement parameters described above.

[0080] Communication parameters of an IRS typically include reflection coefficients, phase shift, and gain, which directly affect the signal propagation path and strength. When constructing a third sub-constraint, the communication parameters of the IRS can be taken into consideration, such as limiting the maximum gain, minimum gain, and phase shift range of the IRS reflection unit, to ensure the feasibility of the IRS in practical applications and meet the basic requirements of the IRS physical layer communication.

[0081] In practical implementation, when constructing the third sub-constraint based on the IRS communication parameters, mathematical modeling can be used to transform the IRS communication parameters into a set of linear or nonlinear constraints as part of the optimization problem. For example, in the nth time slot, the IRS phase shift matrix can be represented as an M×1 vector, where M is the total number of reflection units in the IRS, and each element in the phase shift matrix represents the phase shift of the corresponding reflection unit. By imposing reasonable constraints on these parameters, such as ensuring the phase shift angle is within the range of [0, 2π] and that the phase shift change between adjacent time slots does not exceed a certain threshold, signal distortion and communication instability can be effectively prevented.

[0082] To find the optimal solution to the optimization model, iterative optimization methods are typically employed, such as the semidefinite relaxation (SDR) method and the continuous convex approximation (SCA) method. In each iteration, the current value of the optimization model can be calculated based on the current IRS phase shift matrix, the UAV's movement parameters, and the UAV base station's beam parameters. These variables can then be adjusted to achieve higher energy efficiency. In the optimization process described above, the third sub-constraint limits the range of variation of the IRS communication parameters, ensuring that the optimization process can proceed within the feasible region.

[0083] In practical applications, the steps to determine the optimal solution of the optimization model, in conjunction with the third sub-constraint and with the objective of maximizing the model's value, can be implemented using numerical optimization toolkits (such as CVX), leveraging efficient algorithms to quickly find the optimal solution. Furthermore, by introducing slack variables and approximation methods, the originally complex non-convex optimization problem can be transformed into a series of convex optimization problems, thereby reducing computational complexity and accelerating convergence.

[0084] Based on the optimization model given in the above embodiments, as shown in formulas (18) and (19), it can be seen that the communication parameters of the IRS only affect the secure communication rate in the optimization model. Therefore, maximizing the value of the optimization model can be transformed into maximizing the secure communication rate. Taking the phase shift matrix of the IRS as the communication parameter of the IRS as an example, the fourth objective function can be: (38) The channel gain of the first user in the fourth objective function The variables related to the IRS phase shift parameters were separated, and the following results were obtained: (39) in, , .

[0085] Similarly, for the second user, we get: (40) in, .

[0086] Next, the above formula is expanded, and slack variables are introduced. , , and By applying constraints to the parameters in the expanded formula and processing the fourth objective function in formula (38), the following constraint condition C5 is obtained: (41) in, , , , , , , .

[0087] Since constraint C5 is a non-convex constraint, it can be transformed into a convex constraint using the SCA method. , Then at the feasible point At that location, we obtained: (42) (43) in, express exist The derivative at point; express exist The derivative at point .

[0088] Therefore, the optimization problem of the communication parameters of the IRS can be transformed into a semi-positive definite programming problem, and the optimal solution of the fourth objective function can be obtained by CVX.

[0089] Based on the method for determining the optimal solution given in the above embodiments, in order to comprehensively determine the optimal solution of the optimization model by combining multiple parameters, in some embodiments, the above constraints include a first sub-constraint, a second sub-constraint, and a third sub-constraint; the above constraints for constructing the optimization model based on constraint parameters include: a first sub-constraint based on the UAV's mobility parameters; a second sub-constraint based on the UAV base station's beam parameters; and a third sub-constraint based on the IRS's communication parameters.

[0090] Based on the method described in the above embodiments, the constraints of the optimization model can also be determined based on the first sub-constraint, the second sub-constraint, and the third sub-constraint. The above-mentioned combination of constraints to determine the optimal solution of the optimization model with the objective of maximizing the value of the optimization model includes: combining the first, second, and third sub-constraints to determine the optimal solution of the optimization model with the objective of maximizing the value of the optimization model.

[0091] For example, based on the method given in the above embodiments, the target movement parameters of the UAV, the target beam parameters of the UAV base station, and the target communication parameters of the IRS can be determined simultaneously based on the first sub-constraint, the second sub-constraint, and the third sub-constraint. The flight trajectory of the UAV is adjusted based on the target movement parameters, the beam parameters of the UAV base station are adjusted based on the target beam parameters, and the phase shift parameters of each reflection unit in the IRS are adjusted based on the target communication parameters. Multiple configurations of the UAV system are adjusted, which improves the secure communication rate of the UAV system while reducing the energy consumption of the UAV system.

[0092] Based on the constraints including a first sub-constraint, a second sub-constraint, and a third sub-constraint, in some embodiments, the above-mentioned combination of constraints to determine the optimal solution of the optimization model with the objective of maximizing the value of the optimization model includes: determining a first optimal solution of the optimization model with the objective of maximizing the value of the optimization model by combining the first sub-constraint; determining a second optimal solution of the optimization model with the objective of maximizing the value of the optimization model by combining the second sub-constraint; determining a third optimal solution of the optimization model with the objective of maximizing the value of the optimization model by combining the third sub-constraint; and determining the optimal solution of the optimization model based on the first optimal solution, the second optimal solution, and the third optimal solution.

[0093] When determining the constraints of the optimization model based on the first, second, and third sub-constraints, the method described in the above embodiments allows for the following steps: First, based on the first sub-constraint and the optimization model, the first optimal solution under the first sub-constraint is obtained, i.e., the optimal movement parameter under the first sub-constraint. Simultaneously, based on the second sub-constraint and the optimization model, the optimal beam parameter under the second sub-constraint is obtained, and based on the third sub-constraint and the optimization model, the optimal IRS communication parameter under the third sub-constraint is obtained. Finally, by combining the optimal movement parameter under the first sub-constraint, the optimal beam parameter under the second sub-constraint, and the optimal IRS communication parameter under the third sub-constraint, the optimal solution that maximizes the value of the optimization model is determined iteratively.

[0094] For example, based on the specific calculation method given in the above embodiments, the first optimal solution can be determined based on the above formula (28), the second optimal solution can be determined based on the above formula (34), and the third optimal solution can be determined based on the above formula (38). Based on the method given in this embodiment, Figure 3 A flowchart of another unmanned aerial vehicle (UAV) system optimization method is shown, including: Step 301: Begin.

[0095] Step 302: Establish a secure communication system for unmanned aerial vehicles (UAVs).

[0096] It is possible to establish such as Figure 2 The drone system shown is a drone safety communication system, which may include an IRS.

[0097] Step 303: Construct the optimization model and constraints.

[0098] Based on the method described in the above embodiments, an optimization model for the UAV system can be constructed based on the system's secure communication rate and energy consumption. Furthermore, the constraints of the optimization model can be determined based on one or more of the first, second, and third sub-constraints.

[0099] For example, the constraints of the optimization model can be the power constraints of the UAV base station, the phase shift constraints of the IRS, and the flight trajectory constraints of the UAV. The following explanation uses an example where the constraints include a first sub-constraint, a second sub-constraint, and a third sub-constraint.

[0100] Step 304: Initialize the UAV system configuration information and initialize the iteration count i.

[0101] Where i can be an integer greater than or equal to 1. The UAV system configuration information can be initialized based on the UAV's flight requirements, such as initializing ground user information, UAV base station beam parameters, IRS communication parameters, and UAV movement parameters.

[0102] Step 305: Solve for the optimal solution of the beam parameters of the UAV base station.

[0103] Based on the method described in the above embodiments, the optimal solution for the beam velocity parameters of the UAV base station is obtained based on the second sub-constraint and the optimization model. In practical applications, the optimal solution for the beam parameters of the UAV base station can be obtained by fixing the UAV movement parameters and the IRS communication parameters.

[0104] Step 306: Solve for the optimal solution of the IRS communication parameters.

[0105] Based on the method described in the above embodiments, the optimal solution for the IRS communication parameters is obtained based on the third sub-constraint and the optimization model. In practical applications, the optimal solution for the IRS communication parameters can be obtained by fixing the UAV movement parameters and the beam parameters of the UAV base station.

[0106] Step 307: Solve for the optimal solution of the UAV's movement parameters.

[0107] Based on the method described in the above embodiments, the optimal solution for the UAV movement parameters is obtained based on the first sub-constraint and the optimization model. In practical applications, the beam parameters of the UAV base station and the phase shift parameters of the IRS can be fixed to obtain the optimal solution for the UAV base station beam velocity parameters.

[0108] Steps 305 to 307 can be performed simultaneously, or the execution order of each step in steps 305 to 307 can be determined according to actual needs.

[0109] Step 308: Calculate the objective function value .

[0110] Based on the optimal solutions for the beam parameters of the UAV base station, the IRS communication parameters, and the UAV movement parameters obtained in steps 305 to 307, the value of the objective function is determined by substituting them into the objective function. .

[0111] Step 309: Determine whether the value of the objective function meets the preset conditions.

[0112] Based on the objective function value of the current iteration and the objective function value of the previous iteration The relative rate of change is used to determine whether a preset condition is met. When the relative rate of change is less than or equal to a first threshold... If the condition is met, proceed to step 311; otherwise, proceed to step 310.

[0113] The following formula can be used to determine whether the objective function value of the current iteration number i satisfies the preset condition: (44) Step 310: Update the iteration count.

[0114] Update the iteration count by setting i = i + 1; then execute step 305.

[0115] Step 311: Obtain the optimal solution.

[0116] Step 312: End.

[0117] Based on the method described in this application, the following is obtained: Figure 4 The diagram shows the first simulation result. Wherein, Figure 4 Scheme 1 is an optimization scheme for the UAV system that ignores hardware damage to the UAV system transceiver and aims to maximize the secure communication rate. Scheme 2 is an optimization scheme for the UAV system that ignores hardware damage to the UAV system transceiver based on the optimization method of the above embodiments. Scheme 3 is an optimization scheme that considers hardware damage to the UAV system transceiver, but the UAV system does not include an IRS. Scheme 4 is an optimization scheme that considers hardware damage to the UAV system transceiver and aims to maximize the secure communication rate.

[0118] Figure 4 The impact of the maximum transmit power of a UAV on the energy efficiency of secure communication (values ​​from the optimization model) is presented. Figure 4 It can be seen that for all schemes, the secure communication energy efficiency of the UAV system increases with the increase of the UAV's maximum transmit power, and the rate of increase gradually slows down. This is because increasing the maximum transmit power provides greater freedom for optimizing the beamforming vector of the UAV base station, thereby maximizing secure communication energy efficiency. However, since the noise power caused by transceiver hardware damage is proportional to the transmission power, an increase in transmission power leads to an increase in noise power; therefore, the rate of increase in secure communication energy efficiency gradually slows down. Figure 4 The comparison curves in the figures illustrate that the optimization method of this application is superior to other optimization schemes. The beamforming vector of the IRS and the base station, as well as the optimization design of the speed and position of the UAV, provided in this application embodiment can effectively suppress the impact of transceiver hardware damage on the UAV system, and at the same time effectively improve the secure communication efficiency of the UAV system.

[0119] Figure 5 A schematic diagram of the second simulation results is shown, illustrating the impact of transceiver hardware impairments on the energy efficiency of secure communication. Based on Figure 5 As can be seen, the secure communication efficiency of each scheme gradually decreases with the increase of the transceiver hardware impairment coefficient, indicating that transceiver hardware impairment severely reduces the performance of the IRS-assisted UAV system. Compared with Scheme 3, deploying an IRS and optimizing its phase shift parameters can significantly improve the safety performance of the UAV system. Compared with Schemes 1 and 2, which ignore the impact of transceiver hardware impairment, considering and suppressing the impact of transceiver hardware impairment during beamforming vector design can effectively improve the secure communication efficiency of the UAV system. Since transceiver hardware impairment is unavoidable in actual systems, and Schemes 1 and 2 did not consider the impact of transceiver hardware impairment on UAV system performance during the design process, their secure communication efficiency is lower than that of the schemes that suppress the impact of transceiver hardware impairment. Furthermore, compared with Scheme 4, which ignores UAV flight energy consumption, the optimization method given in this application embodiment reasonably balances the relationship between the system's secure communication rate and energy consumption, thereby improving the system's secure communication efficiency.

[0120] To ensure the safe transmission of information by unmanned aerial vehicles (UAVs), improve the energy efficiency of UAV systems, and suppress transceiver hardware damage, this application proposes an optimization method for suppressing the impact of non-ideal hardware characteristics in IRS-assisted UAV systems. First, an IRS-assisted UAV secure communication system model is established. Utilizing the statistical characteristics of transceiver hardware damage, the expression for the optimization model under the influence of non-ideal hardware characteristics is derived. Then, factors such as the UAV base station beamforming vector, the IRS phase shift matrix, and the UAV's flight position and velocity are jointly optimized to maximize the value of the optimization model. Finally, for the proposed non-convex optimization problem, iterative optimization is used to decouple the problem into three sub-constraints for optimal solution. The active beamforming vector and the IRS phase shift matrix are solved using semidefinite programming (SDP), and the UAV's flight position and velocity are obtained using the relaxation variable method and the SCA algorithm. Simulation results show that the optimization method presented in this application effectively suppresses the impact of transceiver hardware damage while improving the energy efficiency of secure communication in UAV systems.

[0121] Those skilled in the art will understand that the order in which the steps are written in the above-described method of the specific implementation does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic. The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, and improvements made within the spirit and scope of this application are included within the scope of protection of this application.

[0122] Based on the unmanned aerial vehicle (UAV) system optimization method proposed in the foregoing embodiments, this application also provides an UAV system optimization device. Figure 6 A schematic diagram of a drone system optimization device is shown, such as... Figure 6 As shown, the optimization device for the unmanned aerial vehicle system includes: The first processing module 601 is used to construct an optimization model of the UAV system based on the secure communication rate and energy consumption of the UAV system; wherein the value of the optimization model is positively correlated with the secure communication rate and negatively correlated with the energy consumption; and to construct the constraint conditions of the optimization model based on constraint parameters; wherein the constraint parameters include one or more of the following: the communication parameters of the UAV system's IRS, the beam parameters of the UAV base station, and the movement parameters of the UAV.

[0123] The second processing module 602 is used to determine the optimal solution of the optimization model by combining the constraints and aiming to maximize the value of the optimization model.

[0124] The optimization module 603 is used to optimize the UAV system based on the optimal solution.

[0125] In practical applications, the first processing module 601, the second processing module 602, and the optimization module 603 can be implemented based on a processor and a communication device.

[0126] In some embodiments, the constraints include a first sub-constraint. The first processing module 601 is specifically used to construct the first sub-constraint based on the movement parameters of the UAV. The second processing module 602 is specifically used to construct a first objective function with the goal of maximizing the value of the optimization model; to transform the first objective function into a second objective function based on the Tinkelbach transform; and to determine the optimal solution of the optimization model based on the second objective function and the first sub-constraint.

[0127] In some embodiments, the second processing module 602 is specifically used to determine a first communication parameter and a first beam parameter; wherein the first communication parameter is any communication parameter of the IRS and the first beam parameter is any beam parameter of the UAV base station; and to determine the optimal solution of the optimization model based on the second objective function, the first sub-constraint, the first communication parameter and the first beam parameter.

[0128] In some embodiments, the constraints include second sub-constraints; the first processing module 601 is specifically used to construct the second sub-constraints based on the beam parameters of the UAV base station; the second processing module 602 is specifically used to determine the second communication parameters and the first movement parameters; wherein the second communication parameters are any communication parameters of the IRS, and the first movement parameters are any movement parameters of the UAV; based on the second sub-constraints, the second communication parameters, and the first movement parameters, the optimal solution of the optimization model is determined with the objective of maximizing the value of the optimization model.

[0129] In some embodiments, the constraints include a third sub-constraint; the first processing module 601 is specifically used to construct the third sub-constraint based on the communication parameters of the IRS; the second processing module 602 is specifically used to determine the second movement parameter and the second beam parameter; wherein, the second beam parameter is any beam parameter of the UAV base station; the second movement parameter is any movement parameter of the UAV base station; based on the second sub-constraint, the second movement parameter and the second beam parameter, the optimal solution of the optimization model is determined with the objective of maximizing the value of the optimization model.

[0130] In some embodiments, the constraints include a first sub-constraint, a second sub-constraint, and a third sub-constraint; the first processing module 601 is specifically used to construct the first sub-constraint of the optimization model based on the movement parameters of the UAV; construct the second sub-constraint of the optimization model based on the beam parameters of the UAV base station; and construct the third sub-constraint of the optimization model based on the communication parameters of the IRS.

[0131] In some embodiments, the second processing module 602 is specifically configured to: determine a first optimal solution of the optimization model by combining a first sub-constraint with the objective of maximizing the value of the optimization model; determine a second optimal solution of the optimization model by combining a second sub-constraint with the objective of maximizing the value of the optimization model; determine a third optimal solution of the optimization model by combining a third sub-constraint with the objective of maximizing the value of the optimization model; and determine the optimal solution of the optimization model based on the first optimal solution, the second optimal solution, and the third optimal solution.

[0132] It should be noted that the descriptions of the above device embodiments are similar to those of the above method embodiments, and have similar beneficial effects. For technical details not disclosed in the device embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0133] It should be noted that, in the embodiments of this application, if the above-described methods are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a terminal, server, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), magnetic disks, or optical disks. Thus, the embodiments of this application are not limited to any specific hardware and software combination.

[0134] This application also provides an electronic device. Figure 7 This is a schematic diagram of the composition structure of an electronic device provided in an embodiment of this application, as shown below. Figure 7 As shown, the electronic device 70 may include: Memory 701 is used to store executable instructions.

[0135] The processor 702 is used to implement any of the above-mentioned unmanned aerial vehicle system optimization methods when executing the executable instructions stored in the memory 701.

[0136] The processor 702 mentioned above can be at least one of ASIC, DSP, DSPD, PLD, FPGA, CPU, controller, microcontroller, and microprocessor.

[0137] The aforementioned computer-readable storage medium or memory 701 may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM), etc.; it may also be various terminals that include one or any combination of the above-mentioned memories, such as mobile phones, computers, tablet devices, personal digital assistants, etc.

[0138] This application embodiment further provides a computer storage medium storing computer-executable instructions, which are used to implement any of the unmanned aerial vehicle system optimization methods provided in the above embodiments.

[0139] Correspondingly, this application embodiment further provides a computer program product, which includes computer-executable instructions for implementing any of the unmanned aerial vehicle system optimization methods provided in the above embodiments.

[0140] In some embodiments, the functions or modules of the apparatus provided in this application can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0141] The description of the various embodiments above tends to emphasize the differences between the various embodiments. The similarities or similarities between them can be referred to, and for the sake of brevity, they will not be repeated here.

[0142] The methods disclosed in the various method embodiments provided in this application can be arbitrarily combined to obtain new method embodiments without conflict.

[0143] The features disclosed in the various product embodiments provided in this application can be arbitrarily combined without conflict to obtain new product embodiments.

[0144] The features disclosed in the various method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method or device embodiments.

[0145] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0146] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of protection of this application, and these forms are all within the protection scope of this application.

Claims

1. A method for optimizing an unmanned aerial vehicle (UAV) system, characterized in that, The method includes: Based on the secure communication rate and energy consumption of the UAV system, an optimization model for the UAV system is constructed; wherein the value of the optimization model is positively correlated with the secure communication rate and negatively correlated with the energy consumption. The constraints of the optimization model are constructed based on the constraint parameters; wherein the constraint parameters include one or more of the following: the communication parameters of the intelligent reflector IRS of the UAV system, the beam parameters of the UAV base station, and the movement parameters of the UAV. Based on the constraints, and with the objective of maximizing the value of the optimization model, the optimal solution of the optimization model is determined; The unmanned aerial vehicle system is optimized based on the optimal solution.

2. The method according to claim 1, characterized in that, The constraints include a first sub-constraint; the constraints for constructing the optimization model based on the constraint parameters include: The first sub-constraint is constructed based on the movement parameters of the UAV; The process of determining the optimal solution of the optimization model by combining the constraints and aiming to maximize the value of the optimization model includes: To maximize the value of the optimization model, a first objective function is constructed; Based on the Tinkelbach transform, the first objective function is transformed into the second objective function; Based on the second objective function and the first sub-constraint, the optimal solution of the optimization model is determined.

3. The method according to claim 2, characterized in that, Determining the optimal solution of the optimization model based on the second objective function and the first sub-constraints includes: Determine a first communication parameter and a first beam parameter; wherein the first communication parameter is any communication parameter of the IRS, and the first beam parameter is any beam parameter of the UAV base station; The optimal solution of the optimization model is determined based on the second objective function, the first sub-constraint, the first communication parameters, and the first beam parameters.

4. The method according to claim 1, characterized in that, The constraints include second sub-constraints; the constraints for constructing the optimization model based on the constraint parameters include: The second sub-constraint condition is constructed based on the beam parameters of the UAV base station; The process of determining the optimal solution of the optimization model by combining the constraints and aiming to maximize the value of the optimization model includes: Determine a second communication parameter and a first movement parameter; wherein the second communication parameter is any communication parameter of the IRS, and the first movement parameter is any movement parameter of the UAV; Based on the second sub-constraint, the second communication parameter, and the first movement parameter, the optimal solution of the optimization model is determined with the objective of maximizing the value of the optimization model.

5. The method according to claim 1, characterized in that, The constraints include a third sub-constraint; the constraints for constructing the optimization model based on the constraint parameters include: The third sub-constraint is constructed based on the communication parameters of the IRS; The process of determining the optimal solution of the optimization model by combining the constraints and aiming to maximize the value of the optimization model includes: Determine a second movement parameter and a second beam parameter; wherein the second beam parameter is any beam parameter of the UAV base station; the second movement parameter is any movement parameter of the UAV base station; Based on the third sub-constraint, the second movement parameter, and the second beam parameter, the optimal solution of the optimization model is determined with the objective of maximizing the value of the optimization model.

6. The method according to claim 1, characterized in that, The constraints include a first sub-constraint, a second sub-constraint, and a third sub-constraint; the constraints for constructing the optimization model based on the constraint parameters include: The first sub-constraint condition of the optimization model is constructed based on the movement parameters of the UAV; The second sub-constraint condition of the optimization model is constructed based on the beam parameters of the UAV base station; The third sub-constraint of the optimization model is constructed based on the communication parameters of the IRS.

7. The method according to claim 6, characterized in that, The process of determining the optimal solution of the optimization model by combining the constraints and aiming to maximize the value of the optimization model includes: Based on the first sub-constraint, and with the objective of maximizing the value of the optimization model, the first optimal solution of the optimization model is determined; Combining the second sub-constraint, with the objective of maximizing the value of the optimization model, the second optimal solution of the optimization model is determined; By combining the third sub-constraint, and with the objective of maximizing the value of the optimization model, the third optimal solution of the optimization model is determined; Based on the first optimal solution, the second optimal solution, and the third optimal solution, the optimal solution of the optimization model is determined.

8. An electronic device, characterized in that, The electronic device includes a processor and a memory for storing computer programs capable of running on the processor; wherein, The processor is used to run the computer program to perform the method according to any one of claims 1 to 7.

9. A computer storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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