DRL-Optimized Optical Communication for a Reliable UAV-Based Maritime Data Transmission
Hanjiang Luo, Saisai Ma, Hang Tao, Rukhsana Ruby, Jiehan Zhou, Kaishun Wu
Abstract
Maritime data transmission with unmanned aerial vehicles (UAVs) in maritime Internet of Things (MIoT) systems has received increasing attention due to its flexibility and low cost. To further improve the efficiency of maritime data transmission between the UAVs and the maritime buoys, optical communication is considered as a promising technique because of its low latency and high bandwidth. However, optical communication encounters the challenge of beam pointing alignment, particularly in maritime data transmission involving wave disturbance and drift of buoy, which deteriorates and even interrupts the line-of-sight (LOS) optical transmission. To tackle the challenge, this paper proposes DERLOC, a reliable data transmission solution based on deep reinforcement learning (DRL). We first provide the optimization analysis of reliable data transmission and formulate the data transmission procedure as a Markov decision process (MDP) aiming at maximizing the received signal intensity. Afterwards, we propose a beam pointing adjustment algorithm based on the soft actor-critic (SAC) approach to alleviate the performance deterioration caused by waves. Then, we analyze the drift characteristic of a buoy and develop a method which enables UAV to predict the position and determine an optimal movement control strategy for ensuring the effectiveness of beam pointing and maintaining stable LOS communication. Through extensive simulations and real-time data validation, the results demonstrate that DERLOC is effective and enables a reliable data transmission via optical links.