Energy-Efficient Optimization Algorithm in NOMA-Based UAV-Assisted Data Collection Systems

In order to relieve the traffic burden of the ground base station (BS) and improve system operation efficiency, we study the energy-efficient optimization problem in a non-orthogonal multiple access (NOMA)-based unmanned aerial vehicle (UAV)-assisted data collection system, where a UAV assists a ground BS for traffic offloading. Particularly, the joint optimization problem of the placement of the UAV and transmit power of the sensor nodes is formulated to maximize the energy efficiency of all sensor nodes under the quality-of-service constraints and the probabilistic ground-to-air channel model. To balance the optimized performance and online operation time, we propose an alternating-based offline optimization algorithm for obtaining the optimal online UAV trajectory policy. Under the given UAV placement, the formulated problem is first transformed into a convex power allocation subproblem. Based on the power-allocation solutions, the deep deterministic policy gradient algorithm is then leveraged to approach the optimal UAV placement. Simulations show that the proposed algorithm can obtain an efficient performance while consuming an average online operation time of fewer than 0.2 seconds.