Decentralized Online Learning Algorithms for Opportunistic Spectrum Access

—The fundamental problem of multiple secondary users contending for opportunistic spectrum access over multiple channels in cognitive radio networks has been formulated recently as a decentralized multi-armed bandit (D-MAB) problem. In a D-MAB problem there are M users and N arms (channels) that each offer i.i.d. stochastic rewards with unknown means so long as they are accessed without collision. The goal is to design a decentralized online learning policy that incurs minimal regret, defined as the difference between the total expected rewards accumulated by a model-aware genie, and that obtained by all users applying the policy. We make two contributions in this paper. First, we consider the setting where the users have a prioritized ranking, such that it is desired for the K-th-ranked user to learn to access the arm offering the K-th highest mean reward. For this problem, we present the first distributed policy that yields regret that is uniformly logarithmic over time without r...

• Type: Journal
• Year: 2011
• Where: CORR
• Authors: Yi Gai, Bhaskar Krishnamachari
• Keywords: Cognitive Radio, CORR 2011, Education, Radio Networks, Secondary Users