Genetic algorithm-based optimization for cognitive radio networks

Authors:
Si Chen;Timothy R. Newman;Joseph B. Evans;Alexander M. Wyglinski
Affiliations:
Wireless Innovation Laboratory, Department of Electrical & Computer Engineering, Worcester Polytechnic Institute, Worcester, MA;Wireless@Virginia Tech, Bradley Department of Electrical & Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA;Information and Telecommunication Technology Center, The University of Kansas, Lawrence, KS;Wireless Innovation Laboratory, Department of Electrical & Computer Engineering, Worcester Polytechnic Institute, Worcester, MA
Venue:
Sarnoff'10 Proceedings of the 33rd IEEE conference on Sarnoff
Year:
2010

Citing 4
Cited 2

Adaptation in natural and artificial systems

Adaptation in natural and artificial systems
Cognitive engine implementation for wireless multicarrier transceivers

Wireless Communications & Mobile Computing - Cognitive Radio, Software Defined Radio And Adaptive Wireless Systems
Population adaptation for genetic algorithm-based cognitive radios

Mobile Networks and Applications
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation

A multi-objective genetic optimization for spectrum sensing in cognitive radio

Expert Systems with Applications: An International Journal
Survey Comparative performance analysis of evolutionary algorithm based parameter optimization in cognitive radio engine: A survey

Ad Hoc Networks

Quantified Score

Hi-index	0.01

Visualization

Abstract

Genetic algorithms are well suited for optimization problems involving large search spaces. In this paper, we present several approaches designed to enhance the convergence time and/or improve the performance results of genetic algorithmbased search engine for cognitive radio networks, including techniques such as population adaptation, variable quantization, variable adaptation, and multi-objective genetic algorithms (MOGA). Note that the time required for a genetic algorithm to reach a decent solution substantially increases with system complexity, and thus techniques are needed that will help facilitate achieving adequate results over a short period of time.