Multi-rate medium access protocol based on reinforcement learning

Many wireless devices employ multi-rate techniques to improve network performance. However, despite the significant amount of research aimed at dynamically adjusting the transmission rate, the majority of this effort considers neither the competing nodes in wireless mesh networks nor the congestion in the nodes. This work employs distributed intelligent agents to observe the surrounding environment in order to dynamically adjust the individual node transmission rates. Reinforcement learning is employed to control the way each node updates its transmission rate based on the transmission rate of the adjacent node as well as the traffic load. This work is validated through extensive simulations that compare the proposed model with three of the most widely cited schemes. The results indicate significant improvement in system throughput.