Research on Mitigating Atmosphere Turbulence Fading by Relay Selections in Free-Space Optical Communication Systems with Multi-Transceivers

In free-space optical communication (FSOC) systems, atmospheric turbulence can bring about power fluctuations in receiver ends, restricting channel capacity. Relay techniques can divide a long FSOC link into several short links to mitigate the fading events caused by atmospheric turbulence. This paper proposes a Reinforcement Learning-based Relay Selection (RLRS) method based on Deep Q-Network (DQN) in a FSOC system with multiple transceivers, whose aim is to enhance the average channel capacity of the system. Malaga turbulence is studied in this paper. The presence of handover loss is also considered. The relay nodes serve in decode-and-forward (DF). Simulation results demonstrate that the RLRS algorithm outperforms the conventional greedy algorithm, which implies that the RLRS algorithm may be utilized in practical FSOC systems.