Energy-Efficient RIS-Assisted UAV Relaying for IoT via Q-Learning-Based Phase Optimization

The increasing deployment of low-power Internet of Things (IoT) devices in dense urban and rural environments is significantly constrained by Non-Line-of-Sight (NLoS) propagation, where buildings and terrain obstruct direct communication with terrestrial Base Stations (BSs). This paper investigates a Reconfigurable Intelligent Surface (RIS)-assisted Unmanned Aerial Vehicle (UAV) acting as an aerial relay to establish a virtual Line-of-Sight (LoS) communication link between blocked IoT devices and the BS through passive beam reflection. Since IoT devices operate under stringent energy constraints, the system is evaluated in terms of bandwidth-normalized Energy Efficiency (EE). For fixed numbers of devices and RIS elements and fixed power parameters, maximizing EE over the discrete RIS phase shifts is equivalent to maximizing the sum rate under a fixed power budget, a non-convex combinatorial problem where its search space grows exponentially with the RIS size. To address it without explicit channel modeling, a Q-learning-inspired phase-optimization heuristic is proposed, in which discrete RIS phase configurations are learned for each channel realization from scalar reward feedback only. Simulation results show that, for varying numbers of RIS elements, IoT transmit powers, and active devices, the proposed method achieves higher EE than random phase-shift selection and fixed passive reflection.