Learning-stage based decentralized adaptive access policy for dynamic spectrum access

We consider the problem of decentralized online learning and channel access in a cognitive radio network. Based on an existing distributed access policy proposed in [1], named the ρ^RAND policy, we propose an adaptive decentralized access policy in which the distributed coordination among secondary users is adjusted at different stages of learning accuracy of the primary network. Specifically, we exploit a “perceived population” by each secondary user to reduce collision events at different learning stages. We design a metric that measures the level of learning accuracy and use that as an indicator to adjust the “perceived population” by each secondary user. Simulations show that our proposed adaptive policy improves the leading constant of the normalized regret and can provide substantial improvement over the ρ^RAND policy.