Stable Acoustic Relay Assignment with High Throughput via Lase Chaos-based Reinforcement Learning

Underwater Acoustic Networks (UANs) have gained significant attention from both industry and academia due to their indisputable advantages in improving link reliability, increasing system capacity, expanding transmission range and so on. Acoustic communication is most widely used underwater communication as sound wave is not absorbed by water so easily like electromagnetic wave and optical wave [1]. UANs typically consist of acoustic-linked seabed sensors, autonomous underwater vehicles, and ground stations that provide links to onshore control centers. Due to the battery-powered network nodes, shallow water acoustic channel characteristics, such as low available bandwidth and highly varying multi-path, maximizing throughput while minimizing consumption has become a very challenging task [2]. Recent studies have discussed the challenges and opportunities of underwater cognitive communication [3], proposed cooperative automatic repeat request protocols for higher channel quality [4], and analyzed the impact of low transmission rates and long preambles on medium access control protocols [5]. Artificial intelligence (AI) has experienced significant growth in popularity in recent years, and many industries and research fields have explored its potential applications, including information theory, game theory, biological systems, and so on [6-9].