Deep Reinforcement Learning-Based Relay Selection for Energy-Harvesting Cooperative FSO Systems

Cooperative communication is a promising technology for enhancing the reliability and capacity of free-space optical (FSO) systems. It achieves this by employing multiple FSO links involving relays to overcome environmental challenges. In this work, we consider a cooperative decode-and-forward (DF) system with energy-harvesting capabilities. The relays are equipped with rechargeable batteries with limited capacity. The relays harvest energy from the optical source and store it in their batteries for later use in forwarding information. The relay selection process in this cooperative system with energy-harvesting capabilities is modeled as a Markov decision process (MDP). To improve long-term system capacity under energy constraints, a deep reinforcement learning (DRL)-based relay selection algorithm is proposed. Specifically, a deep Q-network (DQN) is trained using a battery-aware reward function to guide relay-selection decisions while accounting for the relays’ current battery levels. We evaluate the algorithm’s performance through simulations, focusing on its convergence, performance under different numbers of relays, adaptability to varying energy availability, and robustness across diverse weather conditions. The simulation results indicate that the proposed DQN-based algorithm outperforms tabular Q-learning, Battery-Aware Greedy (BA-Greedy), and random selection as baseline methods.