Elements of information theory
Elements of information theory
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Multiplicative updates outperform generic no-regret learning in congestion games: extended abstract
Proceedings of the forty-first annual ACM symposium on Theory of computing
A negotiation game for multichannel access in cognitive radio networks
Proceedings of the 4th Annual International Conference on Wireless Internet
Multi-agent Q-learning of channel selection in multi-user cognitive radio systems: a two by two case
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: A POMDP framework
IEEE Journal on Selected Areas in Communications
Cognitive Medium Access: Constraining Interference Based on Experimental Models
IEEE Journal on Selected Areas in Communications
Distributed learning in multi-armed bandit with multiple players
IEEE Transactions on Signal Processing
Distributed learning approach for channel selection in cognitive radio networks
Proceedings of the Nineteenth International Workshop on Quality of Service
Dynamic channel, rate selection and scheduling for white spaces
Proceedings of the Seventh COnference on emerging Networking EXperiments and Technologies
A highly available spectrum allocation service model in dynamic spectrum market
Future Generation Computer Systems
Spatial spectrum access game: nash equilibria and distributed learning
Proceedings of the thirteenth ACM international symposium on Mobile Ad Hoc Networking and Computing
CSpy: finding the best quality channel without probing
Proceedings of the 19th annual international conference on Mobile computing & networking
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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.