中国邮电高校学报(英文) ›› 2012, Vol. 19 ›› Issue (3): 22-26.doi: 10.1016/S1005-8885(11)60260-4

• Wireless • 上一篇    下一篇

Parameter adjustment based on improved genetic algorithm for cognitive radio networks

赵军辉,李非,ZHANG Xue-xue   

  1. Chinese Academy of Sciences
  • 收稿日期:2011-12-30 修回日期:2012-03-16 出版日期:2012-06-30 发布日期:2012-06-08
  • 通讯作者: 赵军辉 E-mail:junhuizhao@gmail.com
  • 基金资助:

    国家自然科学基金项目;中央高校基本科研业务费;东南大学移动通信国家重点实验室开放课题

Parameter adjustment based on improved genetic algorithm for cognitive radio networks

  1. Chinese Academy of Sciences
  • Received:2011-12-30 Revised:2012-03-16 Online:2012-06-30 Published:2012-06-08
  • Contact: Junhui-Hui ZHAO E-mail:junhuizhao@gmail.com

摘要:

Multi-objective parameter adjustment plays an important role in improving the performance of the cognitive radio (CR) system. Current research focus on the genetic algorithm (GA) to achieve parameter optimization in CR, while general GA always fall into premature convergence. Thereafter, this paper proposed a linear scale transformation to the fitness of individual chromosome, which can reduce the impact of extraordinary individuals exiting in the early evolution iterations, and ensure competition between individuals in the latter evolution iterations. This paper also introduces an adaptive crossover and mutation probability algorithm into parameter adjustment, which can ensure the diversity and convergence of the population. Two applications are applied in the parameter adjustment of CR, one application prefers the bit error rate and another prefers the bandwidth. Simulation results show that the improved parameter adjustment algorithm can converge to the global optimal solution fast without falling into premature convergence.

关键词:

cognitive radio, genetic algorithm, global optimal solution, linear scale transformation, adaptive crossover and mutation probability

Abstract:

Multi-objective parameter adjustment plays an important role in improving the performance of the cognitive radio (CR) system. Current research focus on the genetic algorithm (GA) to achieve parameter optimization in CR, while general GA always fall into premature convergence. Thereafter, this paper proposed a linear scale transformation to the fitness of individual chromosome, which can reduce the impact of extraordinary individuals exiting in the early evolution iterations, and ensure competition between individuals in the latter evolution iterations. This paper also introduces an adaptive crossover and mutation probability algorithm into parameter adjustment, which can ensure the diversity and convergence of the population. Two applications are applied in the parameter adjustment of CR, one application prefers the bit error rate and another prefers the bandwidth. Simulation results show that the improved parameter adjustment algorithm can converge to the global optimal solution fast without falling into premature convergence.

Key words:

cognitive radio, genetic algorithm, global optimal solution, linear scale transformation, adaptive crossover and mutation probability

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