认知网络中无线电信号智能感知方法研究

doi:10.3969/j.issn.0255-8297.2020.03.007

应用科学学报 ›› 2020, Vol. 38 ›› Issue (3): 410-418.doi: 10.3969/j.issn.0255-8297.2020.03.007

认知网络中无线电信号智能感知方法研究

黄堂森¹, 李小武¹, 曹庆皎²

1. 湖南科技学院电子与信息工程学院, 湖南永州 425199;
2. 河北工程大学水利水电学院, 河北邯郸 056038

收稿日期:2019-03-04 出版日期:2020-05-31 发布日期:2020-06-11
通信作者: 李小武,教授,研究方向为智能通信与复杂信号处理.E-mail:lixiaowu555@163.com E-mail:lixiaowu555@163.com
基金资助:
湖南省自然科学基金（No.2019JJ40097,No.2019JJ40096），教育部高等教育司产学合作协同育人项目基金（No.201801082096,No.201802293011），湖南科技学院应用特色学科建设项目基金（湘科院校发No.[2018]83），湖南省永州市科技局项目基金（No.2019YZKJ08），湖南省教育厅青年基金（No.17B107）资助

Research on Intelligent Sensing of Radio Signals in Cognitive Networks

HUANG Tangsen¹, LI Xiaowu¹, CAO Qingjiao²

1. School of Electronics and Information Engineering, Hunan University of science and Engineering, Yongzhou 425199,Hunan Province, China;
2. School of Water Conservancy and Hydropower, Hebei University of Engineering, Handan 056038, Hebei Province, China

Received:2019-03-04 Online:2020-05-31 Published:2020-06-11

摘要/Abstract

摘要： 无线电信号在噪声波动情形下的检测性能有待提高.该文提出了认知用户根据无线电环境变化自动调整检测阈值的感知方法.融合中心应用坐标搜索算法为认知用户提供最优控制参数，认知用户依据最优参数设定检测阈值，并自主学习特定无线电环境下的最佳阈值.此外，该算法充分考虑了各认知用户的个体特征及其感知贡献，并提出了一种基于能量值的加权算法体现用户特征.实验结果说明该算法对噪声波动具有卓越的鲁棒性，在信噪比低于-15 dB时的检测概率远高于传统方法.

关键词: 认知网络, 智能感知, 网格搜索, 加权融合

Abstract: In the case of noise fluctuation, the performance of the radio signal detection needs to be improved. In this paper, a method for cognitive users to automatically adjust the detection threshold according to the changes of the radio environment is proposed. The fusion center applies coordinate search algorithm to provide the optimal control parameters to cognitive users. Cognitive users set the detection threshold according to the optimal parameters and autonomously learn the optimal threshold for a specific radio environment. In addition, by taking a full consideration of the distinctions and sensing contributions of cognitive users, a new weight calculation method to reflect the distinctions is designed. Simulation results show that the spectrum sensing method has excellent robustness to noise fluctuation. It performs a much higher detection probability than the traditional sensing methods as signal-to-noise ratio (SNR) is below -15 dB.

Key words: cognitive networks, intelligent sensing, grid search, weighted fusion

中图分类号:

TN929.5

黄堂森, 李小武, 曹庆皎. 认知网络中无线电信号智能感知方法研究[J]. 应用科学学报, 2020, 38(3): 410-418.

HUANG Tangsen, LI Xiaowu, CAO Qingjiao. Research on Intelligent Sensing of Radio Signals in Cognitive Networks[J]. Journal of Applied Sciences, 2020, 38(3): 410-418.

参考文献

[1] Wang F, Ma J. Investigating factors influencing moment tensor inversion of induced seismicity in virtual IoT[J]. IEEE Access, 2019(7):34238-34251.
[2] Liu S, Zhang Z. Wavelength-spacing adjustable dual-wavelength dissipative soliton resonance thulium-doped fiber laser[J]. IEEE Photonics Journal, 2019, 2(11):1-9.
[3] Sobron I, Diniz P S R. Energy detection technique for adaptive spectrum sensing[J]. IEEE Transactiond on Communications, 2015, 63(3):617-627.
[4] Arslan H U S. A survey of spectrum sensing algorithms for cognitive radio applications[J]. IEEE Communications Surveys and Tutorials, 2016, 11(1):116-130.
[5] 董莉,宋晓勤,韩杰.基于遗传粒子群优化的认知OFDM网络资源分配算法[J].应用科学学报,2017, 35(3):288-298. Dong L, Song X Q, Han J. Resource allocation based on genetic algorithm and particle swarm optimization for cognitive OFDM network[J]. Journal of Applied Sciences, 2017, 35(3):288-298.(in Chinese)
[6] Li S, Hu B J, Wu X Y. Hierarchical cooperative spectrum sensing based on double thresholds energy detection[J]. IEEE Communications Letters, 2012, 16(7):1096-1099.
[7] Zhao N. A novel two-stage entropy-based robust cooperative spectrum sensing scheme with two-bit decision in cognitive radio[J]. Wireless Personal Communications, 2016, 5(3):1-15.
[8] Salman A, Qureshi I M, Sultan K. Joint spectrum sensing for detection of primary users using cognitive relays with evolutionary computing[J]. IET Communications, 2015, 9(13):1643-1648.
[9] Chen X, Zhang S. A joint scheduling and beamforming scheme for RoF-Aided MC-SSN[J]. IEEE Access, 2019(7):29245-29252.
[10] Hamza D, Aissa S, Aniba G. Equal gain combining for cooperative spectrum sensing in cognitive radio networks[J]. IEEE Transactions on Wireless Communications, 2017, 13(8):4334-4345.
[11] Mousavifar S A, Leung C. Transient analysis for a trust-based cognitive radio collaborative spectrum sensing scheme[J]. IEEE Wireless Communications Letters, 2015, 4(4):377-380.
[12] Zhang S, Dong X, Bao Z. Adaptive spectrum sensing algorithm in cognitive ultra-wideband systems[J]. Wireless Personal Communications, 2013, 68(3):789-810.
[13] Khalid L, Anpalagan A. Reliability-based decision fusion scheme for cooperative spectrum sensing[J]. IET Communications, 2014, 8(14):2423-2432.
[14] Kieu X T, Koo I. A cooperative spectrum sensing scheme using adaptive fuzzy system for cognitive radio networks[J]. Information Sciences, 2016, 220:102-109.
[15] Ding G, Wu Q. Decentralized sensor selection for cooperative spectrum sensing based on unsupervised learning[C]//IEEE International Conference on Communications (ICC), Shanghai, China, 2012:1576-1580.

认知网络中无线电信号智能感知方法研究

Research on Intelligent Sensing of Radio Signals in Cognitive Networks

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 1

编辑推荐

Metrics

本文评价