Learning-Based Trajectory Design and Time Allocation in UAV-Supported Wireless Powered NOMA-IoT Networks

Abstract

With the help of unmanned aerial vehicle (UAV), remote terminals that out of wireless coverage can be connected to the Internet of Things (IoT) networks. Currently, the IoT relies on a large number of low-cost wireless sensors with limited energy supply to realize ubiquitous monitoring and intelligent control. The wireless powered communication (WPC) and the non-orthogonal multiple access (NOMA) technologies can solve the problems of energy supply and massive access of IoT terminals respectively. Exploiting these benefits, we investigate joint UAV 3D trajectory design and time allocation for aerial data collection in wireless powered NOMA-IoT networks. To maximize the total fair network throughput, we jointly consider energy limitation, QoS requirements and flight conditions. The problem is non-convex and time-dimension coupled which is intractable to solve by traditional optimization methods. Therefore, we develop a deep reinforcement learning (DRL) algorithm called FC-TDTA (fair communication is accomplished by trajectory design and time allocation), which uses the deep deterministic policy gradient (DDPG) as its basis. Simulation results show that the proposed approach performs better than benchmarks in fair throughput maximization.