Cognitive engine implementation for wireless multicarrier transceivers

  • Authors:

  • Tim R. Newman;Brett A. Barker;Alexander M. Wyglinski;Arvin Agah;Joseph B. Evans;Gary J. Minden

  • Affiliations:

  • Information and Telecommunication Technology Center, The University of Kansas, Lawrence, KS 66045, U.S.A.;Information and Telecommunication Technology Center, The University of Kansas, Lawrence, KS 66045, U.S.A.;Information and Telecommunication Technology Center, The University of Kansas, Lawrence, KS 66045, U.S.A.;Information and Telecommunication Technology Center, The University of Kansas, Lawrence, KS 66045, U.S.A.;Information and Telecommunication Technology Center, The University of Kansas, Lawrence, KS 66045, U.S.A.;Information and Telecommunication Technology Center, The University of Kansas, Lawrence, KS 66045, U.S.A.

  • Venue:
  • Wireless Communications & Mobile Computing - Cognitive Radio, Software Defined Radio And Adaptive Wireless Systems
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper presents a genetic-algorithm driven, cognitive radio decision engine that determines the optimal radio transmission parameters for single and multicarrier systems. Determining the appropriate radio parameters, given a dynamic wireless channel environment is the primary feature of cognitive radios for wireless communication systems. Genetic algorithms (GAs) are designed to select the optimal transmission parameters by scoring a subset of parameters and evolving them until the optimal value is reached for a given goal. Although there have been implementations of GA-based single carrier cognitive radio engines, the performance of these algorithms has not been thoroughly analyzed nor have the fitness functions employed by the algorithms been explored in detail. Multicarrier systems are common in today's communication environment, thus cognitive techniques that account for only single-carrier systems neglect the practical issues of multiple carriers. A set of accurate single carrier and multicarrier fitness functions for our GA implementation that completely control the evolution of the algorithm have been derived. The performance analysis results illustrate the trade-offs between the convergence time of the GA and the size of the GA search space. Copyright © 2007 John Wiley & Sons, Ltd.