认知无线电中基于混沌神经网络的频谱预测

计算机应用 ›› 2011, Vol. 31 ›› Issue (12): 3181-3183.

认知无线电中基于混沌神经网络的频谱预测

鲜永菊¹,杨钺¹,徐昌彪¹,郑湘渝²

1. 重庆邮电大学通信与信息工程学院，重庆 400065
2. 重庆市电力公司城区供电局，重庆 400015

收稿日期:2011-05-12 修回日期:2011-06-27 发布日期:2011-12-12 出版日期:2011-12-01
通讯作者: 杨钺
基金资助:
国家自然科学基金资助项目;重庆市教委2009年项目;重庆大学研究生创新重点项目;重庆市教委科学技术研究项目

Spectrum usage prediction based on chaotic neural network model for cognitive radio system

XIAN Yyong-ju¹,YANG Yue¹,XU Chang-biao¹,ZHENG Xiang-yu²

1. School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2. Urban Power Supply Bureau, Chongqing Electric Power Corporation, Chongqing 400015, China

Received:2011-05-12 Revised:2011-06-27 Online:2011-12-12 Published:2011-12-01
Contact: YANG Yue

摘要/Abstract

摘要： 为了在认知无线电系统中提高频谱的利用率，减少切换次数，提出一种针对信道状态剩余时长的混沌神经网络预测机制，利用混沌预测对信道剩余时长进行分析并作出预测。仿真结果显示，预测精度可以达到90%以上，从而验证了此预测机制的有效性。

关键词: 认知无线电系统, 频谱预测, 混沌神经网络, 频谱分配, 信道状态

Abstract: In order to improve spectrum usage in Cognitive Radio System (CRS), and reduce channel switching frequency, a new prediction mechanism was designed, which was used chaotic neural network to analyze and predict the last time of channel status. Simulation results show that the prediction accuracy can reach 90%, thus the effectivess of this new prediction mechanism was proved.

Key words: cognitive radio system, chaos neural network, spectrum prediction, spectrum allocation, channel status

中图分类号:

TN92

鲜永菊杨钺徐昌彪郑湘渝. 认知无线电中基于混沌神经网络的频谱预测[J]. 计算机应用, 2011, 31(12): 3181-3183.

XIAN Yyong-ju YANG Yue XU Chang-biao ZHENG Xiang-yu. Spectrum usage prediction based on chaotic neural network model for cognitive radio system[J]. Journal of Computer Applications, 2011, 31(12): 3181-3183.

参考文献

［1］HAYKIN S. Cognitive radio: brainempowered wireless communications ［J］. IEEE Journal on Selected Areas in Communications, 2005, 23(2)：210-230.

［2］KRISHNA T V, DAS A. A survey on MAC protocols in OSA networks ［J］. Computer Networks, 2009, 53(9)：1377-1394.

［3］SHAH N, KAMAKARIS T, TURELI U, et al. Wideband spectrum sensing probe for distributed measurements in cellular band ［C］// Proceedings of ACM International Workshop on Technology and Policy for Accessing Spectrum. New York: ACM, 2006: 222-228.

［4］HUR Y, PARK J, WOO W, et al.A wide band analog MultiResolution Spectrum Sensing (MRSS) technique for Cognitive Radio (CR) systems ［C］// ISCAS 2006: Proceedings of IEEE International Symposium on Circuits and Systems. Piscataway, NJ: IEEE, 2006: 4090-4093.
［5］TIAN Z, GIANNAKIS G B. A wavelet approach to wideband spectrum sensing for cognitive radios ［C］// CROWNCOM ’06: Proceedings of International Conference on Cognitive Radio Oriented Wireless Networks and Communications. Piscataway, NJ: IEEE, 2006: 1-5.

［6］YARKAN S, ARSLAN S. Binary time series approach to spectrum prediction for cognitive radios ［C］// VTC ’07: Proceedings of IEEE Conference on Vehicular Technology. Piscataway, NJ: IEEE, 2007: 1563-1567.

［7］LI YANG, DONG YUNING, ZHANG HUI, et al. Spectrum usage prediction based on highorder Markov model for cognitive radio networks ［C］// CIT 2010: Proceedings of IEEE Conference on Computer and Information Technology. Washington, DC: IEEE Computer Society, 2010:2784-2788.

［8］AKBAR I A, TRANTER W H. Dynamic spectrum allocation in cognitive radio using hidden Markov models: Poisson distributed case ［C］// Proceedings of IEEE SoutheastCon 2007. Piscataway, NJ: IEEE, 2007: 196-201.

［9］TUMULURU V K, WANG P, NIYATO D. A neural network based spectrum prediction scheme for cognitive radio ［C］// ICC 2010: Proceedings of 2010 IEEE International Conference on Communications. Piscataway, NJ: IEEE, 2010: 1-5.

［10］HOYHTYA M, POLLIN S, MAMMELA. Performance improvement with predictive channel selection for cognitive radios ［C］// 1st International Workshop on Cognitive Radio and Advanced Spectrum Management. Piscataway, NJ: IEEE, 2008: 1-5.

［11］FRANCONE M, DOMENICALI D, di BENEDETTO MG. Timevarying interference spectral analysis for cognitive UWB networks ［C］// IECON 2006: 32nd Annual Conference on IEEE Industrial Electronics. Piscataway, NJ: IEEE, 2006:3205-3210.
［12］吕金虎,陆君安,陈士华.混沌时间序列分析及其应用［M］.武汉:武汉大学出版社,2002:66-71.

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