Channel Selection Based on Trust and Multiarmed Bandit in Multiuser, Multichannel Cognitive Radio Networks

This paper proposes a channel selection scheme for the multiuser, multichannel cognitive radio networks. This scheme formulates the channel selection as the multiarmed bandit problem, where cognitive radio users are compared to the players and channels to the arms. By simulation negotiation we can achieve the potential reward on each channel after it is selected for transmission; then the channel with the maximum accumulated rewards is formally chosen. To further improve the performance, the trust model is proposed and combined with multi-armed bandit to address the channel selection problem. Simulation results validate the proposed scheme.