Adaptive Q-Learning Trajectory Optimization for the Hybrid NOMA and OMA Assisted UAV Communications Network

Benefit to the advantages of easy deployment and high flexibility, unmanned aerial vehicle (UAV) has been utilized to act as the aerial base station, providing communications service for target areas, such as remote regions and disaster areas. However, with the ever-increasing demand of high-speed and high-quality connections, the efficient multiple access constitutes the main challenge of the UAV communications network. Therefore, in this paper, we propose a hybrid multiple access strategy for UAV communications network, where both the non-orthogonal multiple access (NOMA) and the orthogonal multiple access (OMA) technology are invoked for the sake of efficiently handling the multi-users data-hungry connections. To expound, an adaptive Q-learning based trajectory optimization algorithm is conceived, which is capable of successively solving the problem of user clustering, power allocation and UAV’s trajectory, yielding the maximized achievable throughput. The numerical simulation results demonstrate that the proposed scheme has superiority in terms of average coverage and achievable rate, compared to that of the conventional OMA and NOMA schemes.