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. © 2010 IEEE.
| Original language | English |
|---|---|
| Article number | 5072224 |
| Pages (from-to) | 305-316 |
| Journal | IEEE Transactions on Mobile Computing |
| Volume | 9 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - Mar 2010 |
| Externally published | Yes |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Research Keywords
- Cognitive networks
- Learning algorithms
- Network utility maximization