In cognitive radio networks (CRNs), dynamic spectrum access allows (unlicensed) users to identify and access unused channels opportunistically, thus improves spectrum utilization. In this paper, we address the user-channel allocation problem in multi-user multi-channel CRNs without a prior knowledge of channel statistics. The result of channel access is stochastic with unknown distribution, and statistically different for each user. In deciding the channel for access, a user needs to either explore a channel to learn its statistics, or exploit the channel with the highest expected reward based on the information collected so far. Further, a channel should be accessed exclusively by one user at a time to avoid collision. Using multi-armed bandit framework, we develop two rate-optimal algorithms with low computational complexities of $O(N)$O(N) and $O(NK)$O(NK), respectively, where $N$N denotes the number of users and $K$K denotes the number of channels. Further, we extend the results and develop an algorithm that is amenable to implement in a distributed fashion.
- Cognitive radio networks
- combinatorial multi-armed bandits
- dynamic spectrum access
- low complexity
ASJC Scopus subject areas
- Computer Networks and Communications
- Electrical and Electronic Engineering