基于遗传算法的无人机自组网吞吐量优化方法

Optimization for Throughput of Unmanned Aerial Vehicle Ad Hoc Based on Genetic Algorithm

在一些紧急情况下,无人机自组网可以为特定地域提供通信服务。无人机自组网中的每架无人机相当于一个路由器,通过无线链路形成一个网络,以实现中继通信的目的。吞吐量是一个重要的网络性能指标,无人机节点的位置对其有一定影响。首先从无人机位置和终端选择两个方面分析了无人机自组网吞吐量的影响因素;其次,建立了无人机自组网吞吐量优化的数学模型;再次,提出了一种基于遗传算法的无人机位置优化算法,使无人机网络的吞吐量最大化;最后,使用Matlab从性能、位置约束半径(PCR)的影响和粒度半径(PSR)的影响三个方面对该算法进行了仿真。结果表明,通过控制无人机位置,吞吐量可以达到预期目标,优化速度与PCR和PSR有关。

In some emergency situations,drone ad hoc networks can provide communication services for specific regions.In a drone ad hoc network,each drone acts as a router,and the wireless links between them form a network to achieve relay communication.Throughput is an important network performance,and the location of drone nodes has a certain impact on it.This article first analyzes the factors that affect the throughput of drone ad hoc networks from two aspects:drone location and terminal selection;Secondly,a mathematical model for optimizing the throughput of unmanned aerial vehicle(UAV) ad hoc networks was established;Once again,a genetic algorithm based drone position optimization algorithm was proposed to maximize the throughput of the drone;Finally,Matlab was used to simulate the algorithm from three aspects:performance,the impact of Position Constraint Radius(PCR),and the impact of Particle Size Radius(PSR).The results indicate that by controlling the position of the drone,the throughput can achieve the expected goal,and the optimization speed is related to PCR and PSR.