Computer Science ›› 2018, Vol. 45 ›› Issue (6A): 266-269.

• Network & Communication • Previous Articles     Next Articles

Novel Energy Detection Method and Detection Performance Analysis

CAO Kai-tian1,2,HANG Yi-ling2   

  1. Key Lab of Broadband Wireless Communication and Sensor Network Technology,Ministry of Education, Nanjing University of Posts and Telecommunications,Nanjing 210003,China1
    College of Telecommunications and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China2
  • Online:2018-06-20 Published:2018-08-03

Abstract: In order to overcome the disadvantage that the existing small sample size-based energy detection (ED) me-thods only obtain the approximations of detection performance of ED in AWGN (Additive White Gaussian Noise),a more tractable and more accurate closed-form expression for detection probability of ED in Rayleigh fading channel was derived and its performance was analyzed by exploiting the latest research result of generalized Marcum Q-function in this paper.Both theoretical analysis and simulation results show that compared with the approximate analysis of ED detection performance such as the CLT (Central Limit Theorem)-based approach,the CoG (Cube-of-Gaussian)-based me-thod and other approximations,the proposed scheme has more robust and accurate detection performance.

Key words: Cognitive radio, Detection performance, Energy detection, Meijer’s G function, Spectrum sensing

CLC Number: 

  • TN301
[1]AKYILDIZ I,LEE W Y,VURAN M,et al.Next generation/ dynamic spectrum access/ cognitive radio wireless networks:A survey [J].Computer Networks,2006,50(13):2127-2159.
[2]ZHANG H Y,SUN P,LI C G,et al.Cooperative precoding for wireless energy transfer and secure cognitive radio coexistence systems [J].IEEE Signal Processing Letters,2017,24(5):540-544.
[3]GUIDI F,GUERRA A,DARDARI D,et al.Joint energy detection and massive array design for localization and mapping [J].IEEE Transactions on Wireless Communications,2017,16(3):1359-1371.
[4]SEPIDBAND P,ENTESARI K.A CMOS spectrum sensor based on quasi-cyclo stationary feature detection for cognitive radios [J].IEEE Transactions on Microwave Theory and Techniques,2015,63(12):4098-4109.
[5]GLASS J,BLAIR W.Detection of Rayleigh targets using adjacent matched filter samples [J].IEEE Transactions on Aerospace and Electronic Systems,2015,51(3):1927-1941.
[6]LI Z,WANG D Y,QI P H,et al.Maximum-eigenvalue-based sensing and power recognition for multiantenna cognitive radio system [J].IEEE Transactions on Vehicular Technology,2016,65(10):8218-8229.
[7]曹开田,高西奇,王东林.基于随机矩阵理论的非重构宽带压缩频谱感知方法[J].电子与信息学报,2014,36(12):2828-2834.
[8]ZENG Y H,LIANG Y C.Spectrum sensing algorithms for cognitive radio based on statistical covariances [J].IEEE Transactions on Vehicular Technology,2009,58(4):1804-1815.
[9]FARHANG-BOROUJENY B.Filter bank spectrum sensing for cognitive radios [J].IEEE Transactions on Signal Processing,2008,56(5):1801-1811.
[10]RUGINI L,BANELLI P,LEUS G.Small sample size perfor-mance of the energy detector [J].IEEE Communications Letters,2013,17(9):1814-1817.
[11]SHARMA B V,TELLAMBURA C,JIANG H.Approximations for performance of energy detector and p-norm detector [J].IEEE Communications Letters,2015,19(10):1678-1681.
[12]REISI N,GAZOR S,AHMADIAN M.Distributed cooperative spectrum sensing in mixture of large and small scale fading channels [J].IEEE Transactions on Wireless Communications,2013,12(11):5406-5412.
[13]QUAN Z,CUI S G,SAYED A,et al.Optimal multiband joint detection for spectrum sensing in cognitive radio networks [J].IEEE Transactions on signal processing,2009,57(3):1128-1140.
[14]ATAPATTU S,TELLAMBURA C,JIANG H.Energy detection for spectrum sensing in cognitive radio [M].New York:Springer,2014.
[15]SIMON M,ALOUINI M.Digital communication over fading channels [M].New York,USA:Wiley,2005.
[16]KAY S.Fundamentals of statistical signal processing:detection theory [M].NJ U.S.A.:Prentice-Hall,1998.
[17]ADAMCHIK V S,MARICHEV O I.The algorithm for calcula- ting integrals of hypergeometric type functions and its realization in reduce system [C]∥International Symposium on Symbolic Algebraic Computing.1990:212-224.
[18]GRADSHTEYN I S,RYZHIK I M.Table of Integrals,Series,and Products(8th ed)[M].Amsterdam,the Netherlands:Else-vier,2015.
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