A Two-Dimensional Multiarmed Bandit Approach to Secondary Users with Network Coding in Cognitive Radio Networks

Abstract EN

We study how to utilize network coding to improve the throughput of secondary users (SUs) in cognitive radio networks (CRNs) when the channel quality is unavailable at SUs.

We use a two-dimensional multiarmed bandit (MAB) approach to solve the problem of SUs with network coding under unknown channel quality in CRNs.

We analytically prove the asymptotical-throughput optimality of the proposed two-dimensional MAB algorithm.

Simulation results show that our proposed algorithm achieves comparable throughput performance, compared to both the theoretical upper bound and the scheme assuming known channel quality information.