An Energy Detection Algorithm Using Generalized Stochastic Resonance under Low Signal-to-Noise Ratios

doi:10.7652/xjtuxb201506005

您当前的位置：

首页 >

文章列表页 >

An Energy Detection Algorithm Using Generalized Stochastic Resonance under Low Signal-to-Noise Ratios

更新时间：2025-07-09

- An Energy Detection Algorithm Using Generalized Stochastic Resonance under Low Signal-to-Noise Ratios
- Vol. 49, Issue 6, Pages: 27-32+59(2015)
- 作者机构：
  
  西安电子科技大学综合业务网理论和关键技术国家重点实验室,西安,710071
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201506005
  CLC： TN911.23
- Online First：10 June 2015，
  
  Published：2015
- 稿件说明：
移动端阅览
An Energy Detection Algorithm Using Generalized Stochastic Resonance under Low Signal-to-Noise Ratios[J]. 2015, 49(6): 27-32+59.
DOI：

An Energy Detection Algorithm Using Generalized Stochastic Resonance under Low Signal-to-Noise Ratios[J]. 2015, 49(6): 27-32+59. DOI： 10.7652/xjtuxb201506005.

摘要

针对低信噪比下认知无线电中采用能量检测(ED)的频谱感知方法错误概率较大的问题

提出了一种采用广义随机共振的改进的能量检测(IED)算法。该算法首先对接收信号添加一个直流分量

并借助偏移系数确定直流分量的最优幅值

使其与信号中的直流产生广义随机共振; 其次

对共振后的信号进行采样和能量累加得到检测统计量

然后根据最小平均错误概率准则确定最佳检测门限并与检测统计量进行比较从而做出判决; 最后

从错误概率、样本检测点数和算法复杂度等几个方面给出算法的性能分析。理论推导和仿真结果表明:在信噪比为-15 dB的低信噪比条件下

IED算法的错误概率性能比传统的ED算法提升了约3 dB; 在相同的错误概率条件下

IED算法所需的检测样本点数比ED算法显著减少。

Abstract

An improved energy detection(IED)algorithm using generalized stochastic resonance(GSR)is proposed to reduce the error probability of the energy detection based spectrum sensing method in cognitive radio systems at low signal-to-noise ratio(SNR). A direct current(dc)component is added to the received signal and the optimal amplitude of the dc component is determined by the offset coefficient so that the GSR between the dc component and the dc in the signal is generated. A test statistic is obtained by sampling and energy accumulation of resonant signals. The optimal detection threshold is determined based on the minimum average error probability criterion and then a decision result is obtained from a comparison between the test statistic and the detection threshold. The performance analyses of the proposed algorithm are given. The theoretical analyses

simulation results and a comparison with the conventional ED algorithm show that the error probability of the IED algorithm improves by about 3 dB when SNR is -15 dB

and less samples are needed for the IED algorithm under same error probability conditions.

关键词

Keywords

references

HAYKIN S. Cognitive radio: brain-empowered wireless communications [J]. IEEE Journal on Selected Areas in Communications, 2005, 23(2): 201-220.

LARSSON E G, SKOGLUND M. Cognitive radio in a frequency-planned environment: some basic limits [J]. IEEE Transactions on Wireless Communications, 2008, 7(12): 4800-4806.

CORDEIRO C, CHALLAPALI K, BIRRU D. IEEE 802.22: an introduction to the first wireless standard based on cognitive radios [J]. Journal of Communications, 2006, 1(1): 2208-2233.

AXELL E, LEUS G, LARSSON E G, et al. Spectrum sensing for cognitive radio: state-of-the-art and recent advances [J]. IEEE Signal Processing Magazine, 2012, 29(3): 101-116.

齐佩汉, 司江勃, 李赞, 等. 新型抗噪声不确定度谱分段对消频谱感知算法 [J]. 西安电子科技大学学报, 2013, 40(6): 19-24.

QI Peihan, SI Jiangbo, LI Zan, et al. Novel anti-noise-uncertainty spectrum sensing algorithm based on power spectral density segment cancellation [J]. Journal of Xidian University, 2013, 40(6): 19-24.

THEILER J, FOY B R. Effect of signal contamination in matched-filter detection of the signal on a cluttered background [J]. IEEE Geoscience and Remote Sensing Letters, 2006, 3(1): 98-102.

URKOWITZ H. Energy detection of unknown deterministic signals [J]. Proceedings of the IEEE, 1967, 55(4): 523-531.

LUNDEN J, KASSAM S A, KOIVUNEN V. Robust nonparametric cyclic correlation-based spectrum sensing for cognitive radio [J]. IEEE Transactions on Signal Processing, 2010, 58(1): 38-52.

BENZI R, SUTERA A, VULPIANI A. The mechanism of stochastic resonance [J]. Journal of Physics: A, 1981, 14(11): L453-L457.

GAMMAITIONNI L, HANGGI P, JUNG P, et al. Stochastic resonance [J]. Reviews of Modern Physics, 1998, 70(1): 223-287.

杨定新, 胡政, 杨拥民. 大参数周期信号随机共振解析 [J]. 物理学报, 2012, 61(8): 080501.

YANG Dingxin, HU Zheng, YANG Yongmin. The analysis of stochastic resonance of periodic signal with large parameters [J]. Acta Physica Sinica, 2012, 61(8): 080501.

HE Di. Chaotic stochastic resonance energy detection fusion used in cooperative spectrum sensing [J]. IEEE Transactions on Vehicular Technology, 2013, 62(2): 620-627.

LIU Jin, LI Zan, GUAN Lei, et al. A novel parameter-tuned stochastic resonator for binary PAM signal processing at low SNR [J]. IEEE Communications Letters, 2014, 18(3): 427-430.

HAO Chen, VARSHNEY P K, KAY S M, et al. Theory of the stochastic resonance effect in signal detection: part I fixed detectors [J]. IEEE Transactions on Signal Processing, 2007, 55(7): 3172-3184.

HAO Chen, VARSHNEY P K. Theory of the stochastic resonance effect in signal detection: part II variable detectors [J]. IEEE Transactions on Signal Processing, 2008, 56(10): 5031-5041.

KAY S. Noise enhanced detection as a special case of randomization [J]. IEEE Signal Processing Letters, 2008, 15: 709-712.

KAY S. Fundamentals of statistical signal processing: detection theory [M]. Upper Saddle River, NJ, USA: Prentice Hall, 1998.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A Spectrum Sensing Scheme with Weighted Collaboration of Dynamical Clustering Using Space-Time Block Code

A Sequential Spectrum Sensing Method Using Grouped Data

Asynchronous Channel Hopping Algorithm for Cognitive Radio Networks

Spectrum Sensing Method of Narrow-Bands Based on Peak Feature of Spectrums

A Robust Power Control Algorithm with Priority of Energy Efficiency

Related Author

高鹏 1

高深 1

董培浩 1

白智全 1

王兵 1

王天荆

姜华

Related Institution

School of Information Science and Engineering, Shandong University

Key Laboratory of Higher Education of Sichuan Province for Enterprise Informationalization and Internet of Things, Sichuan University of Science and Engineering

Department of Information and Computing Sciences, Nanjing University of Technology

State Key Laboratory of Integrated Services Networks, Xidian University

Institute of Information Engineering, PLA Information Engineering University

⁰