Multi-unmanned aerial vehicle cooperative optimization method and system based on reconfigurable intelligent surface

Abstract

The application relates to the technical field of unmanned aerial vehicle communication, and provides a multi-unmanned aerial vehicle cooperative optimization method and system based on a reconfigurable intelligent surface, a multi-unmanned aerial vehicle communication and perception integrated system model assisted by the reconfigurable intelligent surface is constructed, a joint optimization problem containing base station and unmanned aerial vehicle association variables, beam forming vectors, reconfigurable intelligent surface reflection phases and unmanned aerial vehicle trajectories is established by taking the minimization of system total information age and system total energy consumption as an objective function and combining constraint conditions; the joint optimization problem is divided into four sub-problems of base station and unmanned aerial vehicle association optimization, beam forming vector optimization, reconfigurable intelligent surface reflection phase optimization and unmanned aerial vehicle trajectory optimization; each sub-problem is solved through alternating iteration, the base station and unmanned aerial vehicle association variables, the beam forming vectors, the reflection phases and the unmanned aerial vehicle trajectories are iteratively updated, the objective function is converged until the optimization result is output. The application reduces the information age and energy consumption of unmanned aerial vehicle communication and perception.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) communication technology, and in particular to a multi-UAV collaborative optimization method and system based on reconfigurable smart surfaces. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] With the rapid development of the low-altitude economy and intelligent drone technology, the integrated sensing and communication (ISAC) capabilities of drones are constantly being enhanced, enabling drones to perform tasks such as environmental perception and target detection while providing communication services.

[0004] With the rapid development of 5G / 6G communication technologies, some applications highly sensitive to quality of service have emerged, such as real-time data acquisition, emergency communication, target tracking, and vehicle-to-everything (V2X) communication. These applications need to meet the dual requirements of low information age and low energy consumption. Because traditional direct communication is easily affected by building obstruction and channel fading, it is difficult to guarantee communication and sensing performance simultaneously. Therefore, traditional UAV communication and sensing integrated systems face the challenges of high information age and high energy consumption. Summary of the Invention

[0005] To overcome the shortcomings of the prior art, this invention provides a multi-UAV collaborative optimization method and system based on reconfigurable smart surfaces, aiming to solve the problems of high information age and high energy consumption in the existing multi-UAV communication and sensing integrated system.

[0006] To achieve the above objectives, the technical solution provided by the present invention is as follows: In a first aspect, the present invention provides a multi-UAV cooperative optimization method based on a reconfigurable smart surface, comprising: A model of a multi-UAV communication and sensing integrated system with reconfigurable smart surface assistance is constructed. With the objective function of minimizing the total information age and total energy consumption of the system, and combined with the constraints, a joint optimization problem is established, which includes the variables associated with the base station and UAVs, the beamforming vector, the reflection phase of the reconfigurable smart surface, and the trajectory of the UAVs. The joint optimization problem is decomposed into four sub-problems: base station and UAV association optimization, beamforming vector optimization, reconfigurable smart surface reflection phase optimization, and UAV trajectory optimization. Each sub-problem is solved iteratively by alternating between them, and the base station and UAV association variables, beamforming vector, reflection phase, and UAV trajectory are updated iteratively until the objective function converges, and the optimization results are output.

[0007] As a further technical solution, the reconfigurable smart surface-assisted multi-UAV communication and sensing integrated system model includes multiple base stations, multiple UAVs equipped with reconfigurable smart surfaces, and a target to be tested.

[0008] As a further technical solution, the objective function, which minimizes the total information age and total energy consumption of the system, is expressed as: ; C1: ; C2: ; C3: ; C4: ; C5: ; C6: ; C7: ; C8: ; C9: , ; in, Indicates the mth base station A collection of drones that can be served in a time slot Let K represent the set of base stations, and K represent the number of drones. Indicates the first Each time slot Indicates the time slot interval. It is a weighting coefficient that reflects the importance of age in information. This indicates the total age of the system's information. It is a weighting coefficient that reflects the importance of energy consumption. This represents the total energy consumption of the system. Indicates drone No. Can each time slot be synchronized with the base station? communication, Indicate target l With base station The perceived signal-to-interference-plus-noise ratio. Indicates the perception threshold. Indicates drone to base station Uplink signal-to-interference-plus-noise ratio, This indicates the maximum threshold for uplink transmission. Indicates base station No. Each time slot is available for serviced drones The emitted beamforming vector, Indicates the maximum transmission power of the base station. Indicates drone No. Each time slot to the base station The emitted beamforming vector, Indicates drone Maximum launch power, Indicates drone No. The trajectory of each time slot, Indicates the maximum speed of the drone. Represents a set of time slots. Indicates drone No. The trajectory of each time slot, This represents the safe distance between two drones, where S is the number of passive reflective elements. This represents the phase diagonal matrix of the reconfigurable smart surface unit. This represents the constant mode that the phase shift matrix must satisfy. This represents the transmit power of base station m. Indicates drone The transmission power.

[0009] As a further technical solution, the total information age of the system is composed of information transmission time, expressed as: ; in, This indicates the total age of the system's information. This means that, under the premise of satisfying the effective number of transmission time slots, in time slots drones Total AoI, Indicates the first Each time slot Indicates the total number of time slots. Indicates the first One base station, Represents a set of base stations. Indicates the first One drone, Indicates the mth base station A collection of drones that can be served in a time slot.

[0010] The total energy consumption of the system includes the energy consumption of UAV flight and the energy consumption of UAV communication, expressed as: ; in, This represents the total energy consumption of the system. Indicates the first Each time slot Indicates the total number of time slots. Indicates the drone's first Flight energy consumption per time slot Indicates the drone's first Communication energy consumption per time slot; The energy consumption of the drone flight is expressed as follows: ; in, Indicates the drone's first Flight energy consumption per time slot Indicates the first One base station, Represents a set of base stations. Indicates the first One drone, This represents the set of drones that can be served by base station m in its nth time slot. This indicates the profile power of the propeller blades when the drone is hovering. Indicates drone No. Flight speed per time slot Indicates the linear velocity of the rotor tip. Indicates the hovering guidance power of the drone. Indicates the hovering guidance speed of the drone. Indicates the fuselage drag coefficient. Indicates air density, Indicates the first One passive reflection unit Indicates the rotor disk area. Indicates the time slot interval; The power consumption of the UAV communication is expressed as follows: ; in, Indicates the drone's first Communication energy consumption per time slot Indicates the first One base station, Represents the set of base stations Indicates the first One drone, This represents the set of drones that can be served by base station m in its nth time slot. Indicates drone No. Each time slot to the base station The emitted beamforming vector, Indicates drone Transmission power Indicates the duration of the uplink transmission phase.

[0011] As a further technical solution, the optimization of the association between the base station and the UAV specifically involves: fixing the trajectory, beam, and reconfigurable smart surface phase, and using a penalty function and successive convex approximation SCA to solve for the optimal association variables, ensuring that each UAV accesses the optimal base station in each time slot.

[0012] As a further technical solution, the beamforming vector optimization specifically involves fixing the associated variables, trajectory, and reconfigurable smart surface phase, performing a first-order Taylor expansion on the non-convex SINR constraint to convexize the problem, and solving for the optimal transmission beams of the base station and the UAV.

[0013] As a further technical solution, the reconfigurable smart surface reflection phase optimization specifically involves: fixing the associated variables, trajectory, and beam, using Projected Gradient Descent (PGD) to update the reconfigurable smart surface phase along the negative gradient, and projecting it onto [0, 2]. ]between.

[0014] As a further technical solution, the UAV trajectory optimization specifically involves: fixing the associated variables, beamforming, reconfigurable smart surface phase, using SCA and adaptive trust regions, linearizing the communication rate, flight energy consumption and collision avoidance constraints, and solving for the optimal flight trajectory.

[0015] Secondly, the present invention provides a multi-UAV cooperative optimization system based on a reconfigurable smart surface, comprising: Joint optimization problem construction module: used to construct a model of a multi-UAV communication and sensing integrated system assisted by reconfigurable smart surface, and to establish a joint optimization problem with the objective function of minimizing the total information age and total energy consumption of the system, combined with constraints, including base station and UAV related variables, beamforming vector, reconfigurable smart surface reflection phase and UAV trajectory; The optimization solution module decomposes the joint optimization problem into four sub-problems: base station and UAV association optimization, beamforming vector optimization, reconfigurable smart surface reflection phase optimization, and UAV trajectory optimization. It solves each sub-problem alternately and iteratively, updating the base station and UAV association variables, beamforming vector, reflection phase, and UAV trajectory until the objective function converges and the optimization results are output.

[0016] Thirdly, the present invention provides a computer-readable storage medium for storing a computer program; wherein the computer program, when executed by a processor, implements the multi-UAV cooperative optimization method based on reconfigurable smart surfaces as described in the first aspect above.

[0017] As can be seen from the above technical solutions, the present invention has the following beneficial effects: This invention uses the total information age and total energy consumption of the system as objective functions. While ensuring communication and sensing performance, it significantly reduces the information age and total energy consumption of the system, improving the timeliness and energy efficiency of UAV services in low-altitude scenarios. Through problem decomposition and alternating iterative optimization, the original non-convex optimization problem is transformed into multiple efficiently solvable convex subproblems, reducing computational complexity and enabling rapid approximate optimal solutions in dynamic scenarios. Simultaneously, it considers the LoS / NLoS channel characteristics and reconfigurable intelligent surface reflection gain in urban scenarios, improving the wireless propagation environment through reconfigurable intelligent surface phase optimization, thereby enhancing the robustness of communication and sensing performance. A unified energy consumption model is constructed, considering both UAV flight energy consumption and communication energy consumption, resulting in optimization results that better reflect the energy efficiency characteristics of actual UAV systems. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of a scenario for constructing a reconfigurable intelligent surface-assisted multi-UAV communication and sensing integrated system model provided in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the dual-stage time-slot structure provided in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the sub-problem solving process provided in Embodiment 1 of the present invention. Detailed Implementation

[0020] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0021] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0022] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0023] Terminology Explanation: UAV: Unmanned Aerial Vehicle; AoI: Age of Information; RIS: Reconfigurable Intelligent Surface; ISAC: Integrated Sensing and Communication.

[0024] Example 1 This embodiment provides a multi-UAV cooperative optimization method based on reconfigurable smart surfaces, including: Step 1: Construct a multi-UAV communication and sensing integrated system model with reconfigurable smart surface assistance, and take minimizing the total information age and total energy consumption of the system as the objective function. Combined with the constraints, establish a joint optimization problem including the base station and UAV related variables, beamforming vector, reconfigurable smart surface reflection phase and UAV trajectory. Step 2: Decompose the joint optimization problem into four sub-problems: base station and UAV association optimization, beamforming vector optimization, reconfigurable smart surface reflection phase optimization, and UAV trajectory optimization. Solve each sub-problem alternately and iteratively, updating the base station and UAV association variables, beamforming vector, reflection phase, and UAV trajectory until the objective function converges, and output the optimization results.

[0025] The following content provides a more detailed description of the multi-UAV cooperative optimization method based on reconfigurable smart surfaces provided in this embodiment.

[0026] In step 1, a multi-UAV communication and sensing integrated system model with reconfigurable smart surface assistance is constructed. With minimizing the total information age and total energy consumption of the system as the objective function, and combined with the constraints, a joint optimization problem is established, which includes the variables associated with the base station and UAVs, the beamforming vector, the reflection phase of the reconfigurable smart surface, and the trajectory of the UAVs.

[0027] In one specific implementation, the reconfigurable smart surface-assisted multi-UAV communication and sensing integrated system model includes multiple base stations, multiple UAVs equipped with reconfigurable smart surfaces, and a target to be tested.

[0028] Specifically, the reconfigurable intelligent surface-assisted multi-UAV communication and sensing integrated system model is composed of... A base station with Na-type antennas, ={1, 2, 3, ..., M}, K-frame carries a drone with a reconfigurable smart surface. ={1, 2, 3, ..., K} and a target to be measured. .

[0029] This invention uses a three-dimensional Cartesian coordinate system to describe the positions of base stations and UAVs. The position of the m-th base station is represented as... = The kth drone equipped with a reconfigurable smart surface was in time slot The position is represented as = For safety reasons, the drone flies at a constant altitude; for the target l Its position is represented as .

[0030] like Figure 1 As shown, base stations 1 and 2 serve as the core of ground communication and sensing, achieving dual functions through the integrated communication and sensing ISAC waveform. The air-to-ground link is communication between base station 2 and UAV 3. Communication between base station 1 and UAV 2 is via a reflection-assisted link equipped with a reconfigurable smart surface (e.g., base station 1 → (UAV 1) → UAV 2). On the other hand, the signal emitted by the base station is scattered by the target to generate an echo, and the echo signal travels through the direct link of target → base station 2 or through the "target → (UAV 1) → Base Station 1” This reflection link returns to the base station. For the sake of simplicity, this invention only considers one controllable reflection of the incident signal to enhance signal strength and improve link quality.

[0031] Meanwhile, this invention will define the total task time interval. The time slots are uniformly divided into N discrete time slots, with n∈ , ={1, 2, ..., N} represents the current time slot index, and the length of each time slot is set to... Furthermore, it is assumed that the system state remains quasi-static within a single time slot. To accurately characterize the evolution of information transmission and information age, each time slot employs a two-stage frame structure design, such as... Figure 2 As shown: The first stage is the downlink communication stage, which lasts for [duration missing]. The second stage is the uplink transmission stage, which lasts for [duration missing]. The drone transmits mission data back to the associated base station.

[0032] In one specific implementation, the objective function of minimizing the total information age and total energy consumption of the system is expressed as: ; C1: ; C2: ; C3: , ; C4: , ; C5: ; C6: ; C7: ; C8: , ; C9: , ; in, Indicates the mth base station A collection of drones that can be served in a time slot Let K represent the set of base stations, and K represent the number of drones. Indicates the first Each time slot Indicates the time slot interval. It is a weighting coefficient that reflects the importance of age in information. This indicates the total age of the system's information. It is a weighting coefficient that reflects the importance of energy consumption. This represents the total energy consumption of the system. Indicates drone No. Can each time slot be synchronized with the base station? communication, Indicate target l With base station The perceived signal-to-interference-plus-noise ratio. Indicates the perception threshold. Indicates drone to base station Uplink signal-to-interference-plus-noise ratio, This indicates the maximum threshold for uplink transmission. Indicates base station No. Each time slot is available for serviced drones The emitted beamforming vector, Indicates the maximum transmission power of the base station. Indicates drone No. Each time slot to the base station The emitted beamforming vector, Indicates drone Maximum launch power, Indicates drone No. The trajectory of each time slot, Indicates the maximum speed of the drone. Represents a set of time slots. Indicates drone No. The trajectory of each time slot, This represents the safe distance between two drones, where S is the number of passive reflective elements. This represents the phase diagonal matrix of the reconfigurable smart surface unit. This represents the constant mode that the phase shift matrix must satisfy. This represents the transmit power of base station m. Indicates drone The transmission power.

[0033] In one specific implementation, the total information age of the system is composed of information transmission time, and is expressed as: ; in, This indicates the total age of the system's information. This means that, under the premise of satisfying the effective number of transmission time slots, in time slots drones Total AoI, Indicates the first Each time slot Indicates the total number of time slots. Indicates the first One base station, Represents a set of base stations. Indicates the first One drone, Indicates the mth base station A collection of drones that can be served in a time slot.

[0034] In real-world scenarios, drones typically establish links and configure assets with ground base stations before transmitting mission data back.

[0035] Let the total amount of information transmitted by a single drone be... According to Shannon's formula, the uplink data rate achievable by the drone is... In the In a time slot, the drone The amount of information that has been transmitted is During the data transmission phase, the amount of data transmitted by the drone is... ,like The cumulative amount of information transmitted The transmission process is complete; if + The cumulative amount of information transmitted is Transmission will continue in the next time slot.

[0036] Let the number of effective transmission time slots be That is, satisfying The smallest integer.

[0037] like , Given the total number of time slots, then in time slots... drones Information age is .

[0038] The total energy consumption of the system includes the energy consumption of UAV flight and the energy consumption of UAV communication, expressed as: ; in, This represents the total energy consumption of the system. Indicates the first Each time slot Indicates the total number of time slots. Indicates the drone's first Flight energy consumption per time slot Indicates the drone's first Communication energy consumption per time slot; The energy consumption of the drone flight is expressed as follows: ; in, Indicates the drone's first Flight energy consumption per time slot Indicates the first One base station, Represents a set of base stations. Indicates the first One drone, This represents the set of drones that can be served by base station m in its nth time slot. This indicates the profile power of the propeller blades when the drone is hovering. Indicates drone No. Flight speed per time slot Indicates the linear velocity of the rotor tip. Indicates the hovering guidance power of the drone. Indicates the hovering guidance speed of the drone. Indicates the fuselage drag coefficient. Indicates air density, Indicates the first One passive reflection unit Indicates the rotor disk area. Indicates the time slot interval; The power consumption of the UAV communication is expressed as follows: ; in, Indicates the drone's first Communication energy consumption per time slot Indicates the first One base station, Represents the set of base stations Indicates the first One drone, This represents the set of drones that can be served by base station m in its nth time slot. Indicates drone No. Each time slot to the base station The emitted beamforming vector, Indicates drone The transmission power, Indicates the duration of the uplink transmission phase.

[0039] In step 2, such as Figure 3 As shown, the joint optimization problem is decomposed into four sub-problems: base station and UAV association optimization, beamforming vector optimization, reconfigurable smart surface reflection phase optimization, and UAV trajectory optimization. Each sub-problem is solved iteratively, and the base station and UAV association variables, beamforming vector, reflection phase, and UAV trajectory are updated iteratively until the objective function converges, and the optimization results are output.

[0040] In one specific implementation, the base station and UAV association optimization is specifically as follows: fixed trajectory, beam, reconfigurable smart surface phase, and the optimal association variables are solved using a penalty function and successive convex approximation SCA to ensure that each UAV accesses the optimal base station in each time slot.

[0041] To address the nonconvexity of the binary constraint C1, it is equivalently transformed into two continuous constraints. The successive convex approximation SCA technique is employed. Specifically, for a given feasible point... The first-order Taylor expansion of the penalty term is obtained as follows: ; in, Indicates drone No. Can each time slot be synchronized with the base station? communication, Indicates the number of iterations. This represents the reference feasible point for the SCA iteration.

[0042] In one specific implementation, the beamforming vector optimization is as follows: fixing the associated variables, trajectory, and reconfigurable smart surface phase, performing a first-order Taylor expansion on the non-convex SINR constraint to convexize the problem, and solving for the optimal transmission beams of the base station and the UAV.

[0043] Specifically, define ; ; in, Indicates the useful received signal power. This represents the total power of interference plus noise. Indicates base station In the time slot A swarm of relay drones in the time-aware phase. Indicates base station To the target The channel, Indicate target With base station Indirect channels between them Indicates base station The emitted beamforming vector, Indicates interference with base stations To the target The channel, Indicate target Interference base stations Indirect channels between them Indicates interference with base stations The emitted beamforming vector, Indicates base station Additive white Gaussian noise at the receiver, Let represent the squared 2-norm of a vector.

[0044] ; can be simplified to , and then simplified to - 0, where W represents , express .

[0045] Let the current iteration point be Calculate exist The first derivative at: f = = Tr ; Its gradient is: f = ; Taylor expands the lower bound: f f +2 Re ; For the denominator, ; Its gradient is: ; Taylor expansion linear upper bound: ; in, Indicate target l With base station The perceived signal-to-interference-plus-noise ratio. Indicates the perception threshold. Represents a complex matrix. Tr This indicates the trace operation. Re Indicates taking the real part, Indicates base station The corresponding channel matrix, Indicates base station Additive white Gaussian noise at the receiver, This indicates the conjugate transpose.

[0046] Therefore, by constructing linearization constraints, we can obtain: ; The linear constraints have been obtained. Similarly, The corresponding linear constraints can also be obtained, which will not be elaborated here, and then the objective function is optimized by convex optimization.

[0047] In one specific implementation, the reconfigurable smart surface reflection phase optimization specifically involves: fixing the associated variables, trajectory, and beam, using Projected Gradient Descent (PGD) to update the reconfigurable smart surface phase along the negative gradient, and projecting it onto [0, 2]. ]between.

[0048] Let the initial phase be ( =1,2,…S), where The initial reflector coefficient matrix can be calculated from the initial phase. ; Calculate the gradient of the objective function with respect to the phase: in the current iteration i (phase is Under these conditions, the reflectivity of the surface is... Influence the signal-to-interference-plus-noise ratio of communication through indirect channels This indirectly affects the communication rate. And the information age; therefore, according to the chain rule, calculate the information age. phase gradient Reflectance coefficient Influence of indirect channel on perceived signal-to-interference-plus-noise ratio Meanwhile, the perceived signal-to-interference-plus-noise ratio must meet certain constraints. To address the non-convexity of this constraint, the Lagrange multiplier method is employed to transform the perceptual constraint into a penalty term of the objective function; make = Then the perceived constraint It can be abbreviated as ; Its gradient is: ; Energy consumption The gradient: Since the total energy consumption is the sum of the energy consumption of each time slot; where, when other variables are fixed, It is a constant, therefore , It is also a constant.

[0049] Then, update the phase angle along the negative gradient direction: ; in, Indicates the step size. This represents the objective function after introducing the Lagrange penalty term. Indicates phase.

[0050] Finally, projection is performed, as the reflectivity must meet certain requirements. =1 (i.e.) Project the updated phase angle: ; in, The projection value representing the new phase. This represents the new phase value after being updated by gradient descent. This represents the modulo operation; In one specific implementation, the UAV trajectory optimization involves: fixing the associated variables, beamforming, reconfigurable smart surface phase, using SCA and adaptive trust regions, linearizing the communication rate, flight energy consumption, and collision avoidance constraints, and solving for the optimal flight trajectory.

[0051] At the iteration point to Perform a first-order Taylor expansion: ; in, = , which is the rate at the current iteration point; its gradient is: = , Indicates the bandwidth of the communication channel. Indicates in time slot Below, drone two-dimensional level Direction coordinates Indicates in time slot Below, drone two-dimensional level Direction coordinates.

[0052] Convex lower bound of information age: number of effective transmission time slots satisfy Combination rate linearization: ; in, It is to satisfy The smallest integer.

[0053] Convex approximation of energy consumption: flight power It is the rate The non-convex function, at the current iteration speed Expand here: ; We obtain a convex approximation of flight energy consumption: ; in, yes The derivative with respect to velocity.

[0054] Collision avoidance constraint convex optimization: This linear constraint is derived from the original collision avoidance constraint. At point The first-order Taylor expansion at the given location is derived as a conservative approximation of the original constraint. During the iteration process, when the two drones satisfy the safe distance at their current positions, this constraint ensures that the new trajectory will not enter the danger zone.

[0055] ; To ensure the local validity of Taylor expansion, a trust region constraint is introduced: ,in For the first The radius of the trust region in the next iteration.

[0056] Finally, convergence is determined by calculating the change in the objective function. If the change is less than a threshold, the iteration stops and the optimal variable is output; otherwise, the iteration continues.

[0057] Example 2 This embodiment provides a multi-UAV cooperative optimization system based on a reconfigurable smart surface, including: Joint optimization problem construction module: used to construct a model of a multi-UAV communication and sensing integrated system assisted by reconfigurable smart surface, and to establish a joint optimization problem with the objective function of minimizing the total information age and total energy consumption of the system, combined with constraints, including base station and UAV related variables, beamforming vector, reconfigurable smart surface reflection phase and UAV trajectory; The optimization solution module decomposes the joint optimization problem into four sub-problems: base station and UAV association optimization, beamforming vector optimization, reconfigurable smart surface reflection phase optimization, and UAV trajectory optimization. It solves each sub-problem alternately and iteratively, updating the base station and UAV association variables, beamforming vector, reflection phase, and UAV trajectory until the objective function converges and the optimization results are output.

[0058] Example 3 This embodiment provides a computer-readable storage medium for storing a computer program; wherein the computer program, when executed by a processor, implements the multi-UAV cooperative optimization method based on reconfigurable smart surfaces as described in Embodiment 1.

[0059] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A multi-UAV cooperative optimization method based on reconfigurable smart surfaces, characterized in that, include: A model of a multi-UAV communication and sensing integrated system with reconfigurable smart surface assistance is constructed. With the objective function of minimizing the total information age and total energy consumption of the system, and combined with the constraints, a joint optimization problem is established, which includes variables related to the base station and UAVs, beamforming vector, reconfigurable smart surface reflection phase and UAV trajectory. The joint optimization problem is decomposed into four sub-problems: base station and UAV association optimization, beamforming vector optimization, reconfigurable smart surface reflection phase optimization, and UAV trajectory optimization. Each sub-problem is solved iteratively by alternating between them, and the base station and UAV association variables, beamforming vector, reflection phase, and UAV trajectory are updated iteratively until the objective function converges, and the optimization results are output.

2. The multi-UAV cooperative optimization method based on reconfigurable smart surfaces as described in claim 1, characterized in that, The reconfigurable smart surface-assisted multi-UAV communication and sensing integrated system model includes multiple base stations, multiple UAVs equipped with reconfigurable smart surfaces, and a target to be tested.

3. The multi-UAV cooperative optimization method based on reconfigurable smart surfaces as described in claim 1, characterized in that, The objective function, which minimizes the total information age and total energy consumption of the system, is expressed as: ； C1： ； C2： ； C3： ， ； C4： ， ； C5： ； C6： ； C7： ； C8： ， C9： ， ； in, Indicates the mth base station A collection of drones that can be served in a time slot Let K represent the set of base stations and K represent the number of drones. Indicates the first Each time slot Indicates the time slot interval. It is a weighting coefficient that reflects the importance of information age. Indicates the total age of the system information. It is a weighting coefficient that reflects the importance of energy consumption. This represents the total energy consumption of the system. Indicates drone No. Can each time slot be synchronized with the base station? communication, Indicate target l With base station The perceived signal-to-interference-plus-noise ratio. Indicates the perception threshold. Indicates drone to base station Uplink signal-to-interference-plus-noise ratio, This indicates the maximum threshold for uplink transmission. Indicates base station No. Each time slot is available for serviced drones The emitted beamforming vector, Indicates the maximum transmission power of the base station. Indicates drone No. Each time slot to the base station The emitted beamforming vector, Indicates drone Maximum launch power, Indicates drone No. The trajectory of each time slot, Indicates the maximum speed of the drone. Represents a set of time slots. Indicates drone No. The trajectory of each time slot, This represents the safe distance between two drones, where S is the number of passive reflective elements. This represents the phase diagonal matrix of the reconfigurable smart surface unit. This represents the constant mode that the phase shift matrix must satisfy. This represents the transmit power of base station m. Indicates drone The transmission power.

4. The multi-UAV cooperative optimization method based on reconfigurable smart surfaces as described in claim 1, characterized in that, The total information age of the system is composed of information transmission time, expressed as: ； in, Indicates the total age of the system information. This means that, under the premise of satisfying the effective number of transmission time slots, in time slots drones Total AoI, Indicates the first Each time slot Indicates the total number of time slots. Indicates the first One base station, Represents a set of base stations. Indicates the first One drone, Indicates the mth base station A collection of drones that can be served in a time slot. The total energy consumption of the system includes the energy consumption of UAV flight and the energy consumption of UAV communication, expressed as: ； in, This represents the total energy consumption of the system. Indicates the first Each time slot Indicates the total number of time slots. Indicates the drone's first Flight energy consumption per time slot Indicates the drone's first Communication energy consumption per time slot; The energy consumption of the drone flight is expressed as follows: ； in, Indicates the drone's first Flight energy consumption per time slot Indicates the first One base station, Represents a set of base stations. Indicates the first One drone, This represents the set of drones that can be served by base station m in its nth time slot. This indicates the profile power of the propeller blades when the drone is hovering. Indicates drone No. Flight speed per time slot Indicates the linear velocity of the rotor tip. Indicates the hovering guidance power of the drone. Indicates the hovering guidance speed of the drone. Indicates the fuselage drag coefficient. Indicates air density, Indicates the first One passive reflection unit Indicates the rotor disk area. Indicates the time slot interval; The power consumption of the UAV communication is expressed as follows: ； in, Indicates the drone's first Communication energy consumption per time slot Indicates the first One base station, Represents the set of base stations Indicates the first One drone, This represents the set of drones that can be served by base station m in its nth time slot. Indicates drone No. Each time slot to the base station The emitted beamforming vector, Indicates drone Transmission power Indicates the duration of the uplink transmission phase.

5. The multi-UAV cooperative optimization method based on reconfigurable smart surfaces as described in claim 1, characterized in that, The optimization of the association between the base station and the UAV involves: using a fixed trajectory, beam, and reconfigurable smart surface phase, and employing a penalty function and successive convex approximation SCA to solve for the optimal association variables, ensuring that each UAV accesses the optimal base station in each time slot.

6. The multi-UAV cooperative optimization method based on reconfigurable smart surfaces as described in claim 1, characterized in that, The beamforming vector optimization specifically involves fixing the associated variables, trajectory, and reconfigurable smart surface phase, performing a first-order Taylor expansion on the non-convex SINR constraint to convexize the problem, and solving for the optimal transmission beams of the base station and the UAV.

7. The multi-UAV cooperative optimization method based on reconfigurable smart surfaces as described in claim 1, characterized in that, The reconfigurable smart surface reflection phase optimization specifically involves: fixing the associated variables, trajectory, and beam, using Projected Gradient Descent (PGD) to update the reconfigurable smart surface phase along the negative gradient, and projecting it onto [0, 2]. ]between.

8. The multi-UAV cooperative optimization method based on reconfigurable smart surfaces as described in claim 1, characterized in that, The UAV trajectory optimization specifically involves: fixing the associated variables, beamforming, reconfigurable smart surface phase, using SCA and adaptive trust regions, linearizing the communication rate, flight energy consumption and collision avoidance constraints, and solving for the optimal flight trajectory.

9. A multi-UAV cooperative optimization system based on reconfigurable smart surfaces, characterized in that, include: Joint optimization problem construction module: used to construct a model of a multi-UAV communication and sensing integrated system assisted by reconfigurable smart surface, and to establish a joint optimization problem with the objective function of minimizing the total information age and total energy consumption of the system, combined with constraints, including base station and UAV related variables, beamforming vector, reconfigurable smart surface reflection phase and UAV trajectory; The optimization solution module decomposes the joint optimization problem into four sub-problems: base station and UAV association optimization, beamforming vector optimization, reconfigurable smart surface reflection phase optimization, and UAV trajectory optimization. It solves each sub-problem alternately and iteratively, updating the base station and UAV association variables, beamforming vector, reflection phase, and UAV trajectory until the objective function converges and the optimization results are output.

10. A computer-readable storage medium, characterized in that, Used to store computer programs; wherein the computer programs, when executed by a processor, implement the multi-UAV cooperative optimization method based on reconfigurable smart surfaces as described in any one of claims 1-8.

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