Cognitive radio spectrum allocation using evolutionary algorithms

Cognitive radio has been regarded as a promising technology to improve spectrum utilization significantly. In this letter, spectrum allocation model is presented firstly, and then spectrum allocation methods based on genetic algorithm (GA), quantum genetic algorithm (QGA), and particle swarm optimiz...

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Veröffentlicht in:IEEE transactions on wireless communications Jg. 8; H. 9; S. 4421 - 4425
Hauptverfasser: Zhao, Zhijin, Peng, Zhen, Zheng, Shilian, Shang, Junna
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.09.2009
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Allocations
Applied sciences
Biological cells
Channels
Chromosome mapping
Cognitive radio
Evolutionary computation
Exact sciences and technology
Genetic algorithms
Interference constraints
Mathematical models
Optimization
Organization, operation and development plans
Particle swarm optimization
Quantum computing
quantum genetic algorithm
Radio
Radiocommunication specific techniques
Radiocommunications
Searching
Space technology
spectrum allocation
Telecommunications
Telecommunications and information theory
Availability
Performance evaluation
Evolutionary algorithm
Frequency allocation
Mapping
genetic algorithms
Cognitive radio
Particle swarm optimization
quantum genetic algorithm
Biomimetics
Genetic algorithm
spectrum allocation
Channel allocation
Graph colouring
Radio spectrum
Software radio
ISSN:1536-1276, 1558-2248
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Beschreibung
Zusammenfassung:Cognitive radio has been regarded as a promising technology to improve spectrum utilization significantly. In this letter, spectrum allocation model is presented firstly, and then spectrum allocation methods based on genetic algorithm (GA), quantum genetic algorithm (QGA), and particle swarm optimization (PSO), are proposed. To decrease the search space we propose a mapping process between the channel assignment matrix and the chromosome of GA, QGA, and the position of the particle of PSO, respectively, based on the characteristics of the channel availability matrix and the interference constraints. Results show that our proposed methods greatly outperform the commonly used color sensitive graph coloring algorithm.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2009.080939