RL-Based Queue Management for QoS Support in Multi-channel Multi-radio Mesh Networks

In this paper, we consider the important aspect of quality of service (QoS) in wireless mesh networks, focusing on packet delays and packet drops. We observe that the options of solely focusing on throughput, and of only depending on the QoS type characterization by the application level protocol is not sufficient. We propose the use of multiple queues to hold the packets based on their QoS requirement (not necessarily corresponding to the QoS application types). We suggest the technique of reinforcement learning (RL) for the important step of assigning packet to one of the queues, and present an implementation using the TD(0) algorithm. We present various simulation results comparing our algorithm to other known algorithms and frameworks. Our results indicate that our RL-based algorithm is significantly better than previously known results in packet drop ratio.