Deep Reinforcement Learning Explores EH‐RIS for Spectrum‐Efficient Drone Communication in 6G

Abstract

Reconfigurable intelligent surfaces (RISs) have emerged as a groundbreaking technology, revolutionizing wireless networks with enhanced spectrum and energy efficiency (EE). When integrated with drones, the combination offers ubiquitous deployment services in communication‐constrained areas. However, the limited battery life of drones hampers their performance. To address this, we introduce an innovative energy harvesting (EH), that is, EH‐RIS. EH‐RIS strategically divides passive reflection arrays across geometric space, improving EH and information transformation (IT). Employing a meticulous, exhaustive search algorithm, the resources of the drone‐RIS system are dynamically allocated across time and space to maximize harvested energy while ensuring optimal communication quality. Deep reinforcement learning (DRL) is employed to investigate drone‐RIS performance by intelligently allocating resources for EH and signal reflection. The results demonstrate the effectiveness of the DRL‐based EH‐RIS simultaneous wireless information and power transfer (SWIPT) system, demonstrating enhanced drone‐RIS spectrum‐efficient communication capabilities. Our investigation is summarized in unleashing potential, which shows how DRL and EH‐RIS work together to optimize drone‐RIS for next‐generation wireless networks.