Directional Interference Avoidance with Minimum Inter-Aircraft Interactions for Flying Ad Hoc Networks (FANET) with Directional Antennas Through Distributed Multi-Agent Reinforcement Learning

Flying ad hoc networks (FANETs) communications have started to use next-generation wireless technologies, such as terahertz (THz) networks. Conventional protocols often require a central coordinator (the main aircraft) and frequent message exchanges among aircraft by using omnidirectional antennas to make optimal communication adjustments. Those schemes cannot utilize the advantages of directional antennas used in THz communications. Moreover, it could significantly diminish the efficiency of high-speed THz links. Hence, we introduce a distributed adaptive layer (DAL) in the protocol stack to adjust the node's attributes according to the dynamic network properties in FANETs. Here the attributes include the sending rate, antenna facing angle, antenna beamwidth, directional interference levels (defined in the concept of exclusive region (ER)), and other flying parameters. We have also implemented the Multi-Agent Deep Distributed Reinforcement Learning (MADDRL) algorithm within the DAL protocol. Our decentralized intelligence model requires minimal information exchange among one-hop neighbors and enables each node/agent to interact with its environment based on individual and local observations. Furthermore, we employ two primary communication evaluation metrics, i.e., the end-to-end throughput and queueing delay, to showcase the effectiveness of our approach. Based on our simulations, the DAL protocol exhibits several advantages over the conventional schemes that rely on a central controller. These include 150% higher throughput, nearly 4 times less delay, and the reduction of protocol message exchanges.