一种新型的能量检测方法及性能分析

计算机科学 ›› 2018, Vol. 45 ›› Issue (6A): 266-269.

一种新型的能量检测方法及性能分析

曹开田^1,2,杭燚灵²

南京邮电大学宽带无线通信与传感网技术教育部重点实验室南京210003¹
南京邮电大学通信与信息工程学院南京210003²

出版日期:2018-06-20 发布日期:2018-08-03
作者简介:曹开田(1978-),男,博士,副教授,主要研究方向为无线通信与网络信号处理、机器学习理论等,E-mail:xckt007@163.com(通信作者);杭燚灵(1993-),女,硕士生,主要研究方向为无线通信与网络信号处理,E-mail:hangyiling00@163.com。
基金资助:
国家自然科学基金(61671252,61201161,61571233)资助

Novel Energy Detection Method and Detection Performance Analysis

CAO Kai-tian^1,2,HANG Yi-ling²

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

摘要/Abstract

摘要： 针对当前小样本情况下的能量检测(Energy Detection,ED)方法只对AWGN(Additive White Gaussian Noise)非衰落信道上的检测性能进行了近似分析的不足,利用广义Marcum Q函数的最新研究成果对小样本条件下的ED方法进行研究,推导出在瑞利衰落信道下易处理的、精确的ED检测概率闭式解表达式,并对其检测性能进行了分析。理论分析和仿真结果表明,与目前采用中心极限定理(Central Limit Theorem,CLT)、多维高斯(Cube-of-Gaus-sian,CoG)近似法及其他近似法分析ED检测性能相比,所提方法在小样本情况下具有更稳定、更精确的检测性能。

关键词: Meijer’s G函数, 检测性能, 能量检测, 频谱感知, 认知无线电

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

中图分类号:

TN301

曹开田, 杭燚灵. 一种新型的能量检测方法及性能分析[J]. 计算机科学, 2018, 45(6A): 266-269. https://doi.org/

CAO Kai-tian, HANG Yi-ling. Novel Energy Detection Method and Detection Performance Analysis[J]. Computer Science, 2018, 45(6A): 266-269. https://doi.org/

参考文献

[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.

相关文章 15

[1]	房婷, 宫傲宇, 张帆, 林艳, 贾林琼, 张一晋. 一种传输时限下认知无线电网络的动态广播策略 Dynamic Broadcasting Strategy in Cognitive Radio Networks Under Delivery Deadline 计算机科学, 2021, 48(7): 340-346. https://doi.org/10.11896/jsjkx.200900001
[2]	丁诗铭, 王天荆, 沈航, 白光伟. 基于能量分类器的抗SSDF攻击协作频谱感知算法 Energy Classifier Based Cooperative Spectrum Sensing Algorithm for Anti-SSDF Attack 计算机科学, 2021, 48(2): 282-288. https://doi.org/10.11896/jsjkx.191100124
[3]	田苗苗, 王祖林, 徐迈. 基于认知无线电的DVB-S2信号接收与解析 DVB-S2 Signal Receiving and Analysis Based on Cognitive Radio 计算机科学, 2020, 47(4): 226-232. https://doi.org/10.11896/jsjkx.190700210
[4]	薛玲玲, 樊秀梅. 基于分簇结构的车联网认知频谱分配机制 Cognitive Spectrum Allocation Mechanism in Internet of Vehicles Based on Clustering Structure 计算机科学, 2019, 46(9): 143-149. https://doi.org/10.11896/j.issn.1002-137X.2019.09.020
[5]	张煜培, 赵知劲, 郑仕链. 融合学习差分进化和粒子群优化算法的认知决策引擎 Cognitive Decision Engine of Hybrid Learning Differential Evolution and Particle Swarm Optimization 计算机科学, 2019, 46(6): 95-101. https://doi.org/10.11896/j.issn.1002-137X.2019.06.013
[6]	吉毅, 贾俊铖, 盛凯. 基于跳频的认知无线电网络中的时隙优化策略 Time Slot Optimization for Channel Hopping in CRN 计算机科学, 2019, 46(11): 58-64. https://doi.org/10.11896/jsjkx.181001865
[7]	高鹏, 刘芸江, 高维廷, 李曼, 陈娟. 基于可信度的双门限DMM协作频谱感知算法 Double Thresholds DMM Cooperative Spectrum Sensing Algorithm Based on Credibility 计算机科学, 2018, 45(9): 166-170. https://doi.org/10.11896／j.issn.1002-137X.2018.09.027
[8]	庄陵,尹耀虎. 认知异构网络中基于不完全频谱感知的资源分配算法 Resource Allocation Algorithm for Cognitive Heterogeneous Networks Based on Imperfect Spectrum Sensing 计算机科学, 2018, 45(5): 49-53. https://doi.org/10.11896/j.issn.1002-137X.2018.05.008
[9]	王露, 白光伟, 沈航, 王天荆. 认知无线电网络中的频谱感知安全机会路由协议 Spectrum-aware Secure Opportunistic Routing Protocol in Cognitive Radio Networks 计算机科学, 2018, 45(10): 166-171. https://doi.org/10.11896／j.issn.1002-137X.2018.10.031
[10]	高宝建, 王少迪, 任宇辉, 王玉洁. 基于无线OFDM系统的调制方式保护算法 Modulation Protection Algorithm Based on Wireless OFDM Systems 计算机科学, 2018, 45(10): 64-68. https://doi.org/10.11896／j.issn.1002-137X.2018.10.013
[11]	孙卫,黄金科. 基于QoS的分布式认知无线电网络多信道MAC协议 Multi-channel MAC with QoS Provisioning for Distributed Cognitive Radio Networks 计算机科学, 2017, 44(Z6): 288-293. https://doi.org/10.11896/j.issn.1002-137X.2017.6A.066
[12]	张阳,赵杭生,赵小龙. 基于频谱预测的不完美条件下的能量有效性设计 Energy-efficient Design under Imperfect Condition Based on Spectrum Prediction 计算机科学, 2017, 44(Z6): 258-262. https://doi.org/10.11896/j.issn.1002-137X.2017.6A.059
[13]	周雪倩,吴晓富,余训健. 融入LDPC纠错机制的认知无线电物理层认证分析 LDPC Coded Primary Transmitter Authentication Schemes in Cognitive Radio Networks 计算机科学, 2017, 44(7): 89-93. https://doi.org/10.11896/j.issn.1002-137X.2017.07.016
[14]	赵澄,郝茵茵,华惊宇,姚信威,王万良. 认知无线电中能效优化的子载波匹配策略 Subcarrier Pairing Strategy for Energy Efficiency Optimization in Cognitive Radio 计算机科学, 2017, 44(6): 108-113. https://doi.org/10.11896/j.issn.1002-137X.2017.06.019
[15]	韩杰,宋晓勤,董莉,金慧. 一种基于OFDM认知无线电网络的次优动态资源分配算法 Suboptimal Dynamic Resource Allocation Algorithm in OFDM Based Cognitive Radio Network 计算机科学, 2017, 44(5): 48-52. https://doi.org/10.11896/j.issn.1002-137X.2017.05.009

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed