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.