Secrecy Capacity Maximization for UAV Aided NOMA Communication Networks

With the rapid development of wireless communications, it is challenging to guarantee secure wireless transmission and massive connectivity in the process of data collection. In this paper, we consider an unmanned aerial vehicle (UAV)aided Non-orthogonal Multiple Access (NOMA) communication network. Specifically, the UAV is deployed to collect the data of transmission devices (TDs) in the NOMA manner subject to the eavesdropping attack, while a group of auxiliary devices (ADs) are deployed to provide the cooperative jamming to the eavesdropper. Driven by this networking model, we aim to maximize the total secrecy capacity by jointly optimizing the TDs' and ADs' power allocations and the ADs' scheduling decisions. Considering the problem's non-convexity, we propose a deep reinforcement learning based online optimization algorithm to maximize the total secrecy capacity. Numerical results demonstrate that the proposed algorithm can achieve considerable performance gain over some existing algorithms.