Low-Complexity Learning for Dynamic Spectrum Access in Multi-User Multi-Channel Networks

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
$O(N)$
and
$O(NK)$
, respectively, where
$N$
denotes the number of users and
$K$
denotes the number of channels. Further, we extend the results and develop an algorithm that is amenable to implement in a distributed fashion.