An Adaptive MAC Protocol Based on Time-Domain Interference Alignment for UWANs

The spatial and temporal uncertainty caused by large propagation delays is a fundamental feature of Underwater Acoustic Networks (UWANs), which seriously affects the performance of the UWANs and also brings challenges to the design of MAC protocols. In this paper, we develop an adaptive MAC protocol based on deep reinforcement learning for UWANs, called ARL-MAC protocol, to intelligently allocate time slots for nodes. Firstly, we design a reward mechanism based on the idea of Time-Domain Interference Alignment (TDIA). We determine the reward according to the combination of the node action and the feedback corresponding to the action. Then, we propose a flexible training mechanism to deal with the ever-changing underwater environment, which improves the fairness of time slot allocation. In addition, we introduce the Deep Recurrent Q-Network (DRQN) algorithm to solve the partially observable information issue. Finally, we evaluate the ARL-MAC protocol with the different number of nodes and changing network environment. Simulation results reveal that the ARL-MAC protocol outperforms other MAC protocols for UWANs in terms of throughput, collision rate and service fairness.