Performance Analysis and Path-Planning for Self-Energized UAV-Assisted Relay Networks

This article studies performance analysis for a novel unmanned aerial vehicles (UAVs) selection algorithm for UAV-assisted relay networks. Several UAVs are located randomly between the transmitter and receiver base stations in the proposed model. Assuming no direct link between the transmitter and receiver, only one UAV is selected to act as a relay. Furthermore, we assume that the UAVs are energized from a dedicated power base station. The UAV selection mechanism is performed over two phases. In phase one, the UAVs that have succeeded in harvesting energy greater than a predefined threshold are eligible to be selected. Whereas in phase two, the UAV with the highest signal-to-noise ratio is selected to act as a relay. We derive closed-form expressions for the total outage probability, average throughput, and average symbol error probability under several practical assumptions, such as the nonlinear energy harvesting model, random UAV locations, and Nakagami-$m$ fading channel models. Moreover, we propose a UAV localization approach using deep reinforcement learning (DRL) for the selected UAV. The localization problem is found to be nonconvex. Thus, a DRL approach is used to find the optimum UAV trajectory. The simulation results reveal that the proposed localization and selection approaches outperform conventional techniques in the literature. Furthermore, findings show the practicality and validity of the derived closed-form expressions.