Optimising Energy Harvesting and Throughput for UAV-Assisted IRS Systems With Adaptive Energy Harvesting

Integrating intelligent reflecting surfaces (IRS) with unmanned aerial vehicles (UAVs) presents a promising approach for future energy-efficient wireless communications. This paper proposes an adaptive framework that dynamically balances energy harvesting (EH) efficiency and system throughput by adjusting the required EH efficiency based on the UAV's power levels and communication needs. Utilising real-coded genetic algorithm (RCGA), the framework effectively tackles challenges posed by multi-user interference (MUI) and imperfect channel estimation (CE). Our results demonstrate that the RCGA-based approach outperforms deep reinforcement learning (DRL) methods, delivering superior energy harvesting and throughput in realistic conditions. The adaptive EH strategy not only optimises throughput performance but also ensures efficient UAV energy management, particularly in dynamic and energy-constrained environments, making it a robust solution for sustained UAV operations in dynamic and energy-constrained environments.