UAV-Assisted NOMA Network Power Allocation Under Offshore Multi-energy Complementary Power Generation System

To solve the shortage of non-renewable energy sources, the development and utilization of abundant renewable energy sources at sea are gradually attracting attention. For the acquisition and analysis of offshore energy sources, we propose a new offloading framework for grid communication under offshore multi-energy power generation systems. This framework can enhance the performance of offshore communication and provide a good basis for command transmission of multi-energy complementary power generation systems. In this paper, we consider network offloading with the help of unmanned aerial vehicles (UAVs), while adopting non-orthogonal multiple access (NOMA) techniques on each UAV. The system hardware loss and incomplete successive interference cancellation (SIC) are jointly optimized for UAV trajectory and power allocation to minimize the system energy loss. To solve the non-convex problem, we use a two-step Deep Reinforcement Learning (DRL) based algorithm. Numerical results are based on the number of iterations and the variation of the signal-to-noise ratio magnitude to evaluate the effectiveness of the proposed algorithm in the system in terms of system energy consumption, transmission rate, interruption probability, and error rate. This research was funded by the National Natural Science Foundation of China, grant number U2006222 and Natural Science Foundation of Shandong Province, grant number ZR2020MF138.