2nd International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications

Research Article

Population Adaptation for Genetic Algorithm-based Cognitive Radios

  • @INPROCEEDINGS{10.1109/CROWNCOM.2007.4549811,
        author={Timothy R. Newman and Rakesh Rajbanshi and Alexander M. Wyglinski and Joseph B. Evans and Gary J. Minden},
        title={Population Adaptation for Genetic Algorithm-based Cognitive Radios},
        proceedings={2nd International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications},
        keywords={Algorithm design and analysis  Analytical models  Cognition  Cognitive radio  Decision making  Engines  Genetic algorithms  Information technology  Space technology  Wireless sensor networks},
  • Timothy R. Newman
    Rakesh Rajbanshi
    Alexander M. Wyglinski
    Joseph B. Evans
    Gary J. Minden
    Year: 2008
    Population Adaptation for Genetic Algorithm-based Cognitive Radios
    DOI: 10.1109/CROWNCOM.2007.4549811
Timothy R. Newman1,*, Rakesh Rajbanshi1,*, Alexander M. Wyglinski1,*, Joseph B. Evans1,*, Gary J. Minden1,*
  • 1: Information Technology and Telecommunications Center The University of Kansas, Lawrence, KS 66045
*Contact email: newman@ittc.ku.edu, rajbansh@ittc.ku.edu, alexw@ittc.ku.edu, evans@ittc.ku.edu, gminden@ittc.ku.edu


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 (EVF), which represents the amount of change in the environment parameters since the previous cognition cycle.