无人机中基于C-DQN的资源分配和轨迹优化研究

Research on Resource Allocation and Trajectory Optimization Based on C-DQN in UAV

随着无人机作为空中中继和终端技术的迅速发展,物理层安全问题在近几年间已经成为一个研究热点.本文将无人机发射的通信功率分为两部分(保密信息功率和人工噪声功率),在传输保密信息时能有效防止非法窃听,同时考虑了无人机在空中飞行时所需的动力功耗,通过联合优化无人机的飞行轨迹和功率分配比,以实现在固定能量下平均传输信息量最大化的目的.将这一情景建模成马尔可夫模型(MDP),并利用Curiosity-Driven Deep Q-learning Network(C-DQN)算法进行训练优化,结果表明,该算法具有良好的收敛效果.

With the rapid development of UAV as air relay and terminal,the issue of physical layer security has becoming a research hotspot in the recent.This article divides the communication power sent by the UAV into two parts that include power of confidential information and power of artificial noise,which can effectively prevent eavesdroppers eavesdropping illegally when UAV is transmitting confidential information.It also considers aerodynamic power consumption when UAV is flying.In order to maximize the average amount of information transferred at a fixed energy,this article optimizes the flight path and power distribution of the UAV.This scenario is modeled as a Markov model(MDP),and uses the Curiosity-Driven Deep Q-learning Network(C-DQN)algorithm to achieve optimization.The result shows that the algorithm has a good convergent effect.