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Erschienen in: Mobile Networks and Applications 5/2008

01.10.2008

Population Adaptation for Genetic Algorithm-based Cognitive Radios

verfasst von: Timothy R. Newman, Rakesh Rajbanshi, Alexander M. Wyglinski, Joseph B. Evans, Gary J. Minden

Erschienen in: Mobile Networks and Applications | Ausgabe 5/2008

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Abstract

Genetic algorithms are best suited for optimization problems involving large search spaces. The problem space encountered when optimizing the transmission parameters of an agile or cognitive radio for a given wireless environment and set of performance objectives can become prohibitively large due to the high number of parameters and their many possible values. Recent research has demonstrated that genetic algorithms are a viable implementation technique for cognitive radio engines. However, the time required for the genetic algorithms to come to a solution substantially increases as the system complexity grows. In this paper, we present a population adaptation technique for genetic algorithms that takes advantage of the information from previous cognition cycles in order to reduce the time required to reach an optimal decision. Our simulation results demonstrate that the amount of information from the previous cognition cycle can be determined from the environmental variation factor, which represents the amount of change in the environment parameters since the previous cognition cycle.

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Fußnoten
1
Empirically, we find α = 4 is sufficient to provide approximately linear relationship between the BER values and the fitness score.
 
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Metadaten
Titel
Population Adaptation for Genetic Algorithm-based Cognitive Radios
verfasst von
Timothy R. Newman
Rakesh Rajbanshi
Alexander M. Wyglinski
Joseph B. Evans
Gary J. Minden
Publikationsdatum
01.10.2008
Verlag
Springer US
Erschienen in
Mobile Networks and Applications / Ausgabe 5/2008
Print ISSN: 1383-469X
Elektronische ISSN: 1572-8153
DOI
https://doi.org/10.1007/s11036-008-0079-8

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