Analysis and Validation of The Effect of Various Queueing Configurations to the End-to-end Throughput of Multi-Hop Wireless Network

A multi-hop wireless network is created by connecting multiple wireless access points (APs) as the backhaul of the network to increase the network coverage. The issue of spatial bias, unbalanced network performance of end-to-end throughput and delay occurs when the total offered load of the associated stations to the backhaul exceeds the wireless link capacity. Station associated to the access point with more hops away from the gateway will experience a significant amount of delay and lower end-to-end throughput compared to the station with fewer hops to the gateway. The equality of local successful transmit probability and mesh successful transmit probability in congested APs, which is the main root cause of the spatial bias problem, is modelled and validated. If the packet arrival ratio of local over mesh ingress interface is larger than the respective queue length ratio, the mesh ingress interface successful transmit probability will be higher than the local ingress interface successful transmit probability and vice-versa. By controlling the ratio of queue lengths, stations associated to the access point with more hops away from the gateway are given higher transmit opportunity, and therefore the spatial bias problem in multi-hop wireless network can be alleviate

Online Self-Organizing Network Control with Time Averaged Weighted Throughput Objective

We study an online multisource multisink queueing network control problem characterized with self-organizing network structure and self-organizing job routing. We decompose the self-organizing queueing network control problem into a series of interrelated Markov Decision Processes and construct a control decision model for them based on the coupled reinforcement learning (RL) architecture. To maximize the mean time averaged weighted throughput of the jobs through the network, we propose a reinforcement learning algorithm with time averaged reward to deal with the control decision model and obtain a control policy integrating the jobs routing selection strategy and the jobs sequencing strategy. Computational experiments verify the learning ability and the effectiveness of the proposed reinforcement learning algorithm applied in the investigated self-organizing network control problem