Abstract-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.
Yang Song et al REF address the stochastic traffic engineering problem in multi-hop cognitive wireless mesh networks.
Stochastic Traffic Engineering in Multihop Cognitive Wireless Mesh Networks
IEEE Transactions on Mobile Computing
IEEE Transactions on Mobile Computing
Computer Science