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Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm 被引量:4

Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm
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摘要 Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm is proposed, and a fitness function is provided. Simulations are conducted using the adaptive niche immune genetic algo- rithm, the simulated annealing algorithm, the quantum genetic algorithm and the simple genetic algorithm, respectively. The results show that the adaptive niche immune genetic algorithm performs better than the other three algorithms in terms of the multi-user cognitive radio network resource allocation, and has quick convergence speed and strong global searching capability, which effectively reduces the system power consumption and bit error rate. Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm is proposed, and a fitness function is provided. Simulations are conducted using the adaptive niche immune genetic algo- rithm, the simulated annealing algorithm, the quantum genetic algorithm and the simple genetic algorithm, respectively. The results show that the adaptive niche immune genetic algorithm performs better than the other three algorithms in terms of the multi-user cognitive radio network resource allocation, and has quick convergence speed and strong global searching capability, which effectively reduces the system power consumption and bit error rate.
作者 Zu Yun-Xiao Zhou Jie 俎云霄;周杰(School of Electronic Engineering,Beijing University of Posts and Telecommunications,Beijing 100876,China)
出处 《Chinese Physics B》 SCIE EI CAS CSCD 2012年第1期558-565,共8页 中国物理B(英文版)
基金 Project supported by the Research Fund for Joint China-Canada Research and Development Projects of the Ministry of Scienceand Technology,China(Grant No.2010DFA11320)
关键词 cognitive radio networks niche genetic algorithm King map resource allocation cognitive radio networks, niche genetic algorithm, King map, resource allocation
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