MAC Protocol for Underwater Acoustic Networks Based on Deep Reinforcement Learning

Abstract

This paper introduces a Medium Access Control (MAC) protocol for Underwater Acoustic Network (UAN) where one of the transmitter nodes equipped with a Deep Reinforcement Learning (DRL) agent learns the communication environment and adapts its transmission policy to maximize the network throughput. In contrast to a radio frequency (RF) wireless network where the propagation delay is ignored, the UAN experiences significant propagation delays in both transmission from source to sink and feedback from sink to source. When co-existing with a Time Division Multiple Access (TDMA) node or a slotted Aloha node, the DRL node learns to take advantage of the difference in propagation delays and occupy the spare time slots of the network to achieve minimum collision in the UAN environment. Computer simulation results demonstrate the performance gain of using the DRL agent in both synchronous and asynchronous timing models.