Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/36003
Title: Optimization and learning algorithms for orthogonal frequency division multiplexing-based dynamic spectrum access.
Authors: HAMED AHMADI
Keywords: Dynamic spectrum access, Orthogonal frequency division multiplexing, Optimization, Learning, Evolutionary algorithms, Cognitive radio
Issue Date: 25-Jul-2012
Source: HAMED AHMADI (2012-07-25). Optimization and learning algorithms for orthogonal frequency division multiplexing-based dynamic spectrum access.. ScholarBank@NUS Repository.
Abstract: In the first part of this thesis, we consider the centralized approach where the spectrum is assigned to CRs through a central spectrum moderator. For systems without frequency reuse, we solve the subcarrier, bit and power assignment optimally, and also propose two evolutionary algorithms to solve the problem with lower complexity. For systems with frequency reuse, we propose a framework that converts the NP-hard problem into a mixed binary linear programming problem without making any approximations. In the second part of the thesis, learning algorithms are presented. First, an auction-based approach is proposed, where the CRs learn the bidding behavior of their competitors, and bid on the channels which are predicted to have the highest capacity per unit of cost. Finally, for distributed DSA, we propose a low complexity HMM-based learning algorithm which is able to order the subcarriers to be sensed according to the predicted probability of being unoccupied.
URI: http://scholarbank.nus.edu.sg/handle/10635/36003
Appears in Collections:Ph.D Theses (Open)

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