Abstract:

As a key submarine-based communication platform, unmanned underwater vehicle (UUV) can facilitate underwater wireless optical communication (UWOC).However, fluctuating characteristics of water body, different water qualities, multi-user access present challenges to UUV-assisted UWOC systems, which could be alleviated by an appropriate path planning to maximize the system and each user performance.Deep reinforcement learning (DRL) technology was applied in the path planning of autonomous vehicles, a trajectory planning scheme for UUV-assisted UWOC systems was proposed.The UUV automatically decides the navigation direction through deep Q-network (DQN) method, thereby improving the communication capacity of the system and each user.The impact of distinct water qualities on the capacity enhancement was also investigated.Simulation results suggest that the outputted strategy of DQN can improve the link capacity of the system and each user.This capacity improvement in clear seawater is better than that in pure seawater but lower than that in coastal water.

Key words: UUV, optical wireless communication, DRL, trajectory planning