Harnessing Traffic Uncertainties in Wireless Mesh Networks—A Stochastic Optimization Approach

In this paper, we investigate the routing optimization problem in wireless mesh networks. While existing works usually assume static and known traffic demand, we emphasize that the actual traffic is time-varying and difficult to measure. In light of this, we alternatively pursue a stochastic optimization framework where the expected network utility is maximized. For multi-path routing scenario, we propose a stochastic programming approach which requires no priori knowledge on the probabilistic distribution of the traffic. For the single-path routing counterpart, we develop a learning-based algorithm which provably converges to the global optimum solution asymptotically.