Repeated Auctions with Learning for Spectrum Access in Cognitive Radio Networks

Details Repeated Auctions with Learning for Spectrum Access in Cognitive Radio Networks Repeated Auctions with Learning for Spectrum Access in Cognitive Radio Networks

2009-10-12

arxiv.org/abs/0910.2240v1

Computer Science

Information Theory

This paper is presented in Allerton Conference on Communication, Control, and Computing 2009

Scientific paper

In this paper, spectrum access in cognitive radio networks is modeled as a repeated auction game subject to monitoring and entry costs. For secondary users, sensing costs are incurred as the result of primary users' activity. Furthermore, each secondary user pays the cost of transmissions upon successful bidding for a channel. Knowledge regarding other secondary users' activity is limited due to the distributed nature of the network. The resulting formulation is thus a dynamic game with incomplete information. In this paper, an efficient bidding learning algorithm is proposed based on the outcome of past transactions. As demonstrated through extensive simulations, the proposed distributed scheme outperforms a myopic one-stage algorithm, and can achieve a good balance between efficiency and fairness.

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