Wireless MAC Slot Allocation Using Distributed Multi-Armed Bandit Learning and Slot Defragmentation

This paper presents a distributed framework for Medium Access Control (MAC) slot allocation in time-asynchronous wireless networks using Multi-Armed Bandits (MAB) based learning. MAC slot allocation is formulated as an MAB problem where the nodes act as independent learning agents and learn transmission policies that ensure collision free transmissions. A novel concept of Hysteretic MAB has been introduced to speed up learning convergence. In order to reduce the bandwidth overhead while maintaining a desired MAB learning speed, a novel slot defragmentation mechanism is introduced. Not relying on network time synchronization makes the proposed mechanism feasible for low-complexity and low-cost transceivers for wireless sensor and loT networks. The proposed mechanism is tested and evaluated on both fully connected and arbitrary mesh network topologies and is shown to be scalable with network size and topological degree. It is also shown that in partially connected topologies, the mechanism learns spatial channel reuse, thus leading to better spectral usage efficiency.