Energy-Minimization Trajectory Optimization with Dynamic NOMA Clustering and Wireless Powering for UAV-Assisted Maritime Communication

Unmanned aerial vehicles (UAVs) of highly maneuverability have been considered as a promising solution to collect information from remote marine terminals (RMTs). However, the increasing number of RMTs following the development of maritime communications systems poses a great challenge to effective information collection. On one hand, the transmission performance is difficult to be guaranteed due to the constrained battery energy of RMTs. On the other hand, the energy of UAV is also limited, so that it can only collect information from a small number of RMTs during each cruise. To solve these problems, we propose a wireless powering cluster nonorthogonal multiple access (CNOMA) collection scheme. Specifically, a fixed-wing UAV transmits wireless energy signals to charge RMTs, then RMTs send information signals to the UAV in a C-NOMA manner by using the harvested energy. Taking the energy limitation of the UAV into account, we investigate an energy-minimization problem under the constraint of information collection requirement by designing a dynamic clustering strategy and jointly optimizing the UAV's trajectory and the interaction time between the UAV with RMTs. Experimental results show that the proposed scheme has lower energy consumption and higher energy efficiency than existing schemes while satisfying the information collection demands.