Open Access

Opportunistic Spectrum Access in Self-Similar Primary Traffic

EURASIP Journal on Advances in Signal Processing20092009:762547

Received: 16 February 2009

Accepted: 14 July 2009

Published: 7 September 2009


We take a stochastic optimization approach to opportunity tracking and access in self-similar primary traffic. Based on a multiple time-scale hierarchical Markovian model, we formulate opportunity tracking and access in self-similar primary traffic as a Partially Observable Markov Decision Process. We show that for independent and stochastically identical channels under certain conditions, the myopic sensing policy has a simple round-robin structure that obviates the need to know the channel parameters; thus it is robust to channel model mismatch and variations. Furthermore, the myopic policy achieves comparable performance as the optimal policy that requires exponential complexity and assumes full knowledge of the channel model.


Markovian ModelQuantum InformationOptimization ApproachOptimal PolicyChannel Model

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Authors’ Affiliations

Department of Electrical and Computer Engineering, University of California, Davis, USA


© Xiangyang Xiao et al. 2009

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.