A TDMA protocol with reinforcement learning slot selection for MANETs

With the rapid development of wireless communication and the advantage of infrastructure-less technologies, mobile ad hoc networks (MANETs) have attracted great attention on military and rescue applications. Medium access control is an important issue in MANETs. Contention-based MAC protocols (e.g., CSMA) do not ensure a reliable transmission due to the possibility of collisions. On the contrary, schedule-based MAC protocols (e.g., TDMA) can solve the collision problem with a scheduled transmission plan. However, under an infrastructure-less environment, it is non-trivial for each node to determine its own transmission plan. This work investigates how to use reinforcement learning (RL) to help nodes determine their transmission plans in a TDMA protocol. More precisely, we design a cross-layer TDMA protocol with a RL-based slot selection algorithm. We have validated the proposed protocol by the ns-3 network simulator.