Proceedings of the twenty-third annual ACM symposium on Principles of distributed computing
A game-theoretic study of CSMA/CA under a backoff attack
IEEE/ACM Transactions on Networking (TON)
Optimal channel probing and transmission scheduling for opportunistic spectrum access
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
On myopic sensing for multi-channel opportunistic access: structure, optimality, and performance
IEEE Transactions on Wireless Communications - Part 2
Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: A POMDP framework
IEEE Journal on Selected Areas in Communications
A noncooperative spectrum sensing game with maximum network throughput
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Opportunistic spectrum access with multiple users: learning under competition
INFOCOM'10 Proceedings of the 29th conference on Information communications
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We consider the problem of efficient opportunistic spectrum access in cognitive radio networks where there are multiple secondary users trying to share access to multiple channels. In our formulation, each user has a potentially different valuation of each channel and wishes to pick a channel in such a way as to maximize its benefit without interfering with other users. There is a fundamental tradeoff in this problem -- while information about other secondary users is useful in making a good channel sensing/access decision, the communication cost of gathering this information must be taken into account. We formulate the problem as a multi-round negotiation game in which the users try to gather "just-enough-information" to make their decisions. The channel valuations are modeled as independently uniformly distributed random variables between 0 and 1. We propose a threshold-based channel sensing policy based on observations from a previous work. For a two-user two-channel setting, we calculate optimal thresholds, and obtain the corresponding performance for cases with no information exchange, partial information exchange, and full information exchange. We then show how the optimal amount of information exchange varies with the cost of negotiation.