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MONET
2008

Population Adaptation for Genetic Algorithm-based Cognitive Radios

13 years 3 months ago
Population Adaptation for Genetic Algorithm-based Cognitive Radios
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 substantionally 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...
Timothy R. Newman, Rakesh Rajbanshi, Alexander M.
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2008
Where MONET
Authors Timothy R. Newman, Rakesh Rajbanshi, Alexander M. Wyglinski, Joseph B. Evans, Gary J. Minden
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