Abstract-The problem of cooperative allocation among multiple secondary users to maximize cognitive system throughput is considered. The channel availability statistics are initially unknown to the secondary users and are learnt via sensing samples. Two distributed learning and allocation schemes which maximize the cognitive system throughput or equivalently minimize the total regret in distributed learning and allocation are proposed. The first scheme assumes minimal prior information in terms of pre-allocated ranks for secondary users while the second scheme is fully distributed and assumes no such prior information. The two schemes have sum regret which is provably logarithmic in the number of sensing time slots. A lower bound is derived for any learning scheme which is asymptotically logarithmic in the number of slots. Hence, our schemes achieve asymptotic order optimality in terms of regret in distributed learning and allocation. Index Terms-Cognitive medium access, learning, multi-armed bandits, logarithmic regret, distributed algorithms.
In our recent work REF , we first formulated the problem of decentralized learning and access for multiple secondary users.
Opportunistic Spectrum Access with Multiple Users: Learning under Competition
2010 Proceedings IEEE INFOCOM
2010 Proceedings IEEE INFOCOM
Computer Science