Wireless Personal Communications: An International Journal
The Bayesian pursuit algorithm: a new family of estimator learning automata
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
Rank-based dynamic frequency hopping without strict coordination in IEEE 802.22 WRAN system
International Journal of Communication Systems
Primary weight measure and its support in stochastic routing for dynamic cognitive radio networks
Proceedings of the 1st ACM workshop on Cognitive radio architectures for broadband
Convergence rate control for distributed multi-hop wireless mesh networks
Computers and Electrical Engineering
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In this work, the stochastic traffic engineering problem in multihop cognitive wireless mesh networks is addressed. The challenges induced by the random behaviors of the primary users are investigated in a stochastic network utility maximization framework. For the convex stochastic traffic engineering problem, we propose a fully distributed algorithmic solution which provably converges to the global optimum with probability one. We next extend our framework to the cognitive wireless mesh networks with nonconvex utility functions, where a decentralized algorithmic solution, based on learning automata techniques, is proposed. We show that the decentralized solution converges to the global optimum solution asymptotically.