基于甲虫搜索的改进粒子群无人机辅助网络部署优化算法

在体育赛场等用户大规模聚集或者突发灾难的情况下，地面基站经常面临过载甚至瘫痪的问题。此时，多无人机(UAV)辅助网络系统可以很好地为地面基站提供信号补偿，有效地增强局部地区的通信质量。然而，无人机的机动性和网络流动引起的拓扑结构变化，会导致频繁的间歇性连接甚至出现传输故障。因此，UAV基站的有效部署以及网络性能的优化成为亟待解决的问题。该文提出一种基于甲虫搜索的改进粒子群UAV辅助网络部署优化算法—智能高效算法(IEA)，利用甲虫搜索算法(BAS)的个体寻优优势，对粒子群算法(PSO)进行改进，并首次采用双门限约束保证用户通信质量，使得多UAV系统下的网络性能得到了改善。仿真结果表明，相对于传统算法，该文提出的IEA算法在系统吞吐量、用户平均吞吐量以及频谱效率等方面都获得了较大提升。

Improved Particle Swarm Optimization Unmanned Aerial Vehicle-assisted Network Deployment Optimization Algorithm Based on Beetle Antennae Search

In the case of large gathering of users such as sports venues or sudden disasters, the ground base stations often face the problem of overloading or even paralysing. In this case, the multi-Unmanned Aerial Vehicle (UAV) auxiliary network system can provide the signal compensation for ground base stations and enhance effectively the communication quality in local areas. However, the topology changes induced by the mobility of UAV and the network flows, will lead to frequent intermittent connections or even transmission failures. Therefore, the efficient deployment of UAV base stations, as well as the optimization of network performance, become urgent issues. In this paper, an improved Particle Swarm Optimization (PSO) UAV assisted network deployment optimization algorithm based on the Beetle Antennae Search (BAS), the Intelligent and Efficient Algorithm (IEA), is proposed to improve PSO algorithm by using the individual seeking advantages of BAS algorithm. And for the first time, the double threshold constraint is applied to ensure the communication quality of users, which makes the network performance under the multi-UAV system improved. The simulation results show that, compared with the traditional algorithms, the IEA algorithm proposed in this paper achieves an obvious improvement in terms of the system throughput, the user’s average throughput as well as the spectral efficiency.