Nonconvex Optimization of Collaborative Multiband Spectrum Sensing for Cognitive Radios with Genetic Algorithms

Cognitive Radio (CR) is a novel technology that permits secondary users (SUs) to transmit alongside primary users (PUs). PUs retain transparent communications whereas SUs perform spectrum sensing and adaptive transmission to avoid collisions. Ultra-wideband sensing is of primary importance for SU to...

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Bibliographic Details
Published in:International journal of digital multimedia broadcasting 2010, Vol.2010 (2010), p.1-12
Main Authors: Sanna, Michele, Murroni, Maurizio
Format: Article
Language:English
Subjects:
Approximation
Constrictions
Detection
Genetic algorithms
Genetics
Mean square errors
Optimization
Primaries & caucuses
Radio
Searching
Spectrum allocation
Statistics
Studies
Wireless networks
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Summary:Cognitive Radio (CR) is a novel technology that permits secondary users (SUs) to transmit alongside primary users (PUs). PUs retain transparent communications whereas SUs perform spectrum sensing and adaptive transmission to avoid collisions. Ultra-wideband sensing is of primary importance for SU to sense and access opportunistically several bands at a time. Reliable detection in wide geographical regions needs collaborative sensing. Optimal collaborative multiband sensing is not analytically solvable unless some approximations and solution domain restrictions are applied for convexity exploitation. In this paper, we demonstrate that convex constraints are deleterious. We propose an alternative optimization technique based on genetic algorithms. Genetic programming performs a direct search of the optimal solution without approximations and solution domain restrictions. As a consequence, collaborative multiband sensing can be consistently optimized without limitations. Additionally the genetic optimization exploits the correlation of time-varying channels for fast adaptive convergence.
ISSN:1687-7578
1687-7586
DOI:10.1155/2010/531857