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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.

Routing optimization in wireless mesh networks under uncertain traffic demands