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主动干扰辅助下的无人机隐蔽通信功率与位置联合优化
作者姓名:章礼玮  李国鑫  陈瑾  王海超  贺文辉  黄育侦
作者单位:陆军工程大学通信工程学院,江苏南京 210007;军事科学院国防科技创新研究院,北京 100091
摘    要:利用友好干扰节点发送人工噪声是无线隐蔽通信中一种常见实现方法,可以增加监听者做出判断的不确定性,从而实现隐蔽传输。为此,考虑在无人机隐蔽通信网络中,部署一个空中的友好干扰节点,发射人工噪声干扰地面监听者的检测。对无人机与地面用户之间实现无线隐蔽传输进行了研究,分析了其有效隐蔽性能,联合优化了2架无人机的发送功率和位置部署以最大化隐蔽传输速率,使用粒子群优化算法与功率位置交替迭代算法2种优化方法得到最优的无人机部署位置及功率分配方案。仿真结果表明,联合优化方案相比于固定位置只优化功率的基准方案可以显著地提高系统隐蔽传输性能,且交替迭代算法所得结果要优于粒子群优化算法。

关 键 词:无人机通信  隐蔽通信  隐蔽传输速率  粒子群优化算法
收稿时间:2022/11/21 0:00:00
修稿时间:2023/1/24 0:00:00

Joint optimization of power and position of UAV covertcommunication assisted by active jamming
Authors:ZHANG Liwei  LI Guoxin  CHEN Jin  WANG Haichao  HE Wenhui  HUANG Yuzhen
Affiliation:College of Communications Engineering, Army Engineering University of PLA, Nanjing 210007 ,China; National Innovation Institute of Defense Technology, Academy of Military Sciences, Beijing 100091 , China
Abstract:Using friendly jamming nodes and transmitting artificial noise has been a commonmethod in covert communication, which can increase the uncertainty of the warden''s judgment.This paper considered adding a friendly jamming node in the unmanned aerial vehicle(UAV) communication network and transmit artificial noise to interfere with the detection ofground monitors.The realization of wireless covert transmission between UAVs and groundusers was studied, and its effective covert performance was analyzed. The transmission powerand location deployment of two UAV were jointly optimized to maximize the covert transmissionrate. Two optimization methods, particle swarm optimization (PSO) algorithm andpower position alternate iteration algorithm, were used to obtain the optimal deploymentlocation and power allocation scheme for UAV. Simulation results have shown that the jointoptimization scheme could significantly improve the covert transmission performance of the system compared to the fixed position power with only optimization benchmark scheme, andthe results obtained by the alternating iteration algorithm are superior to those by the particleswarm optimization algorithm.
Keywords:
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