SINR概率感知下的无人机覆盖优化

UAV Coverage Optimization on SINR Probability Perception

针对多无人机网络辅助灾区用户通信的场景,构建了一种基于信干噪比(Signal-to-Interference plus Noise Ratio,SINR)检测的概率感知模型,旨在最大化无人机服务区域的覆盖率,同时降低无人机额外能耗,并提升网络吞吐量。在该模型下,提出了两种改进的麻雀搜索算法,分别为Logistic高斯麻雀搜索算法(Logistic Gaussian Sparrow Search Algorithm,LGSSA)和Logistic柯西麻雀搜索算法(Logistic Cauchy Sparrow Search Algorithm,LCSSA)。首先使用Logistic混沌序列产生初始种群,以丰富种群的多样性,提高算法的全局搜索能力;然后,在LGSSA和LCSSA中分别引入高斯变异和柯西变异因子,以改善局部最优解。仿真结果表明改进后的算法可以有效地优化无人机的空中部署,大幅度提升无人机网络的覆盖率。

For multi-UAV network-assisted user communication in disaster areas,a probability perception model based on signal-to-interference plus noise ratio(SINR)detection is designed to maximize the coverage of the UAV service area,reduce additional energy consumption,and increase network throughput.Two improved Sparrow Search Algorithms(SSAs),Logistic Gaussian Sparrow Search Algorithm(LGSSA)and Logistic Cauchy Sparrow Search Algorithm(LCSSA)are proposed.First,the Logistic chaotic sequence is used to generate the initial population to enrich the diversity of the population and improve the global search ability of the algorithm.Then,Gaussian mutation and Cauchy mutation are introduced into LGSSA and LCSSA respectively to improve the local optimal solution.The simulation results show that the improved SSA can effectively optimize the three-dimensional deployment of UAV and increase the coverage rate of UAV network.