[1]农汉琦,孙蕴琪,黄 洁,等.基于机器学习的认知无线网络优化策略[J].计算机技术与发展,2020,30(05):125-131.[doi:10. 3969 / j. issn. 1673-629X. 2020. 05. 024]
　NONG Han-qi,SUN Yun-qi,HUANG Jie,et al.Optimization Strategy of Cognitive Radio Network Based on Machine Learning[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2020,30(05):125-131.[doi:10. 3969 / j. issn. 1673-629X. 2020. 05. 024]
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基于机器学习的认知无线网络优化策略()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年05期
页码:
125-131
栏目:
应用开发研究
出版日期:
2020-05-10

文章信息/Info

Title:
Optimization Strategy of Cognitive Radio Network Based on Machine Learning
文章编号:
1673-629X(2020)05-0125-07
作者:
农汉琦; 孙蕴琪; 黄 洁; 杨泽宇; 吴雪雯; 杨 科; 欧阳键
南京邮电大学 通信与信息工程学院,江苏 南京 210003
Author(s):
NONG Han-qi; SUN Yun-qi; HUANG Jie; YANG Ze-yu; WU Xue-wen; YANG Ke; OUYANG Jian
School of Telecommunications and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
关键词:
认知无线网络; 强化学习; 遗传算法; 隐马尔可夫模型; 频谱利用
Keywords:
cognitive radio; Q-learning algorithm; genetic algorithm; hidden Markov model; utilization of spectrum
分类号:
TP39
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 05. 024
摘要:
5G 的发展带来了终端设备爆炸式增长的现象,使得频谱资源紧缺的问题越加严峻,认知无线网(cognitive radio,CR)的提出,被认为是提高频谱利用率的有效途径。 认知无线网,融合了当代无线电通信技术、计算机技术、微电子学技术、软件无线电技术和现代信号处理技术等多学科之长, 通过感知周围的电磁环境、学习及理解等方式,自主为用户寻找到当前空闲的频谱,完成信息交互过程。 针对频谱资源紧张的现状,为改善频谱分配,首先介绍了有关认知无线网络的概念及其特点, 重点介绍了机器学习中遗传算法,强化学习和隐马尔可夫模型在认知无线网络中的应用,并展望了其在认知无线网络中的发展前景。 机器学习算法的引入,实现了高效的频谱资源管理,有效地解决了无线频谱资源紧张的问题。
Abstract:
The development of 5G has brought explosive growth of terminal equipment, making the shortage of spectrum resources become increasingly serious. The proposal of cognitive radio (CR) is considered as an effective way to improve spectrum utilization. CR incorporates many technologies,including modern radio communication technology,computer technology,microelectronics technology,software radio technology and modern signal processing technology. It can automatically find and allocate idle spectrum for users to complete the information interaction by means of sensing and understanding the environment. In view of the current situation of the shortage of spectrum resources, we discuss various learning algorithms and their application in cognitive radio to improve the technology of spectrum allocation. Some of the prominent learning algorithms discussed are genetic algorithm (GA) ,Q-learning algorithm and hidden Markov model (HMM) . The machine learning algorithms have achieved efficient spectrum resource management and solve the problem of wireless spectrum scarcity.

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更新日期/Last Update: 2020-05-10