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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, the authors 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.
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