Learning-Based spectrum selection in cognitive radio ad hoc networks

Authors:
Marco Di Felice;Kaushik Roy Chowdhury;Cheng Wu;Luciano Bononi;Waleed Meleis
Affiliations:
Department of Computer Science, University of Bologna, Italy;Department of Electrical and Computer Engineering, Northeastern University, Boston;Department of Electrical and Computer Engineering, Northeastern University, Boston;Department of Computer Science, University of Bologna, Italy;Department of Electrical and Computer Engineering, Northeastern University, Boston
Venue:
WWIC'10 Proceedings of the 8th international conference on Wired/Wireless Internet Communications
Year:
2010

Citing 10
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Abstract

Cognitive Radio Ad Hoc Networks (CRAHNs) must identify the best operational characteristics based on the local spectrum availability, reachability with other nodes, choice of spectrum, while maintaining an acceptable end-to-end performance. The distributed nature of the operation forces each node to act autonomously, and yet has a goal of optimizing the overall network performance. These unique characteristics of CRAHNs make reinforcement learning (RL) techniques an attractive choice as a tool for protocol design. In this paper, we survey the state-of-the-art in the existing RL schemes that can be applied to CRAHNs, and propose modifications from the viewpoint of routing, and link layer spectrum-aware operations. We provide a framework of applying RL techniques for joint power and spectrum allocation as an example of Q-learning. Finally, through simulation study, we demonstrate the benefits of using RL schemes in dynamic spectrum conditions.