基于改进遗传算法的认知无线传感网动态频谱分配方案

蔡畅; 王亚芳; 苗兵梅; 姜慧

doi:10.11959/j.issn.1000-0801.2017217

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基于改进遗传算法的认知无线传感网动态频谱分配方案

研究与开发 | 更新时间：2024-06-05

- 基于改进遗传算法的认知无线传感网动态频谱分配方案
- Dynamic spectrum allocation for cognitive radio sensor networks based on improved genetic algorithm
- 电信科学 2017年33卷第8期页码：85-93
- 作者机构：
- 作者简介：
  
  [ "蔡畅（1993-），女，河北科技大学信息科学与工程学院硕士生，主要研究方向为数字通信技术和无线传感网。" ]
  [ "王亚芳（1962-），女，河北科技大学信息科学与工程学院副教授，主要研究方向为信息基础设施、下一代网络和数字交换与传输技术。" ]
  [ "苗兵梅（1992-），女，河北科技大学信息科学与工程学院硕士生，主要研究方向为数字通信技术。" ]
  [ "姜慧（1991-），女，河北科技大学信息科学与工程学院硕士生，主要研究方向为数字通信技术。" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2017217
  中图分类号： TP393
- 网络出版日期：2017-08，
  
  纸质出版日期：2017-08-15
- 稿件说明：
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蔡畅, 王亚芳, 苗兵梅, 等. 基于改进遗传算法的认知无线传感网动态频谱分配方案[J]. 电信科学, 2017,33(8):85-93.

Chang CAI, Yafang WANG, Bingmei MIAO, et al. Dynamic spectrum allocation for cognitive radio sensor networks based on improved genetic algorithm[J]. Telecommunications science, 2017, 33(8): 85-93.
蔡畅, 王亚芳, 苗兵梅, 等. 基于改进遗传算法的认知无线传感网动态频谱分配方案[J]. 电信科学, 2017,33(8):85-93. DOI： 10.11959/j.issn.1000-0801.2017217.

Chang CAI, Yafang WANG, Bingmei MIAO, et al. Dynamic spectrum allocation for cognitive radio sensor networks based on improved genetic algorithm[J]. Telecommunications science, 2017, 33(8): 85-93. DOI： 10.11959/j.issn.1000-0801.2017217.

摘要

将认知无线电中的动态频谱分配技术应用在无线传感网中，针对工作在ISM（industrial，scientific and medical）频段的无线传感网面临的频谱资源紧缺问题，提出一种基于改进自适应遗传算法的动态频谱分配方案。该算法以图论着色模型为基础，以最大带宽收益和最小切换频率为目标函数，在交叉和变异过程中采用自适应交叉概率和变异概率代替固定的交叉概率和变异概率。仿真结果表明，与传统遗传算法和颜色敏感图论着色算法相比，该算法可以实现提高频谱利用率、降低能量消耗的预期目标。

Abstract

ISM (industrial scientific and medical) bands where wireless sensor network works faced with the shortage of spectrum resources problems.Aimed at this case

dynamic spectrum allocation in cognitive radio technology was applied in wireless sensor network.A dynamic frequency spectrum allocation scheme was proposed.The algorithm was a modified adaptive genetic algorithm which was based on graph coloring model.In addition

the objective functions of the algorithm were maximum bandwidth gains and minimum spectrum handoff

besides

in the crossover and mutation process

adaptive crossover probability and mutation probability was used instead of the fixed.Experimental results confirm that compared with the traditional genetic algorithm and color sensitive graph coloring algorithm

the proposed algorithm can achieve the expected goal of improving the spectral efficiency and reducing energy consumption.

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references

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