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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2015.10
- 수록면
- 388 - 399 (12page)
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In this paper, we study parameter adjustments that maximize the energy efficiency of green cognitive radio. Because the problem of parameter adjustments for green cognitive radio can be looked at as a complex discrete optimization problem, evolutionary algorithms can be applied to solve it. Parameter optimization methods based on quantum genetic algorithm (QGA), particle swarm optimization (PSO), and chaotic quantum particle swarm optimization (CQPSO) are designed. In particular, CQPSO integrates the characteristics of chaos with quantum particle swarm optimization (QPSO), which gives it strong global search abilities. Chaotic mutation is introduced into the proposed CQPSO to avoid premature convergence and keep diversity in populations. Quantum computing has excellent features used to increase optimization speed and enhance the search abilities of the algorithm. The proposed CQPSO method provides good performance in terms of convergence rate and convergence accuracy, and can search for an optimal solution to parameter adjustments in green cognitive radio networks. Through simulations comparing it to QGA and PSO, we conclude that the proposed CQPSO can improve energy efficiency and meet users’ quality of service needs.
목차
Abstract
Introduction
Parameter Adjustment Model in Green Cognitive Radio
Parameter Optimization Based on Evolutionary Algorithms
Evaluation and Experimental Results
Conclusions
References
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