Traffic matrix estimation: existing techniques and new directions
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
An information-theoretic approach to traffic matrix estimation
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Traffic matrices: balancing measurements, inference and modeling
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Theory, Volume 1, Queueing Systems
Theory, Volume 1, Queueing Systems
COPE: traffic engineering in dynamic networks
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
Stable and robust multipath oblivious routing for traffic engineering
ITC20'07 Proceedings of the 20th international teletraffic conference on Managing traffic performance in converged networks
Opportunistic power scheduling for dynamic multi-server wireless systems
IEEE Transactions on Wireless Communications
Varieties of learning automata: an overview
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The Impact of Stochastic Noisy Feedback on Distributed Network Utility Maximization
IEEE Transactions on Information Theory
A survey on wireless mesh networks
IEEE Communications Magazine
Adaptive downlink scheduling and rate selection: a cross-layer design
IEEE Journal on Selected Areas in Communications
Price dynamics in competitive agile spectrum access markets
IEEE Journal on Selected Areas in Communications
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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.