马尔可夫模型于无线信道异常检测中的应用
|
摘要
无线信道异常检测中,现有基于大尺度衰落建模的能量检测法简便、迅速,然而其在检测过程中忽略了阴影衰落的实时、随机变化的特性。马尔可夫模型在无线信道建模中具有良好的应用前景,能够有效地应用于阴影衰落的动态分析。通过统计分析先验马尔可夫模型矩阵的相似度变化阈值,计算先验与实时马尔可夫模型矩阵相似度,检测阴影衰落的变化规律是否发生变化,实现无线信道环境的异常检测。该方法作为大尺度衰落建模能量检测法的补充,能够完善检测覆盖面,提高检测的准确率。多次仿真实验结果表明,在高斯白噪声入侵时,该方法可实现准确的检测。
Among wireless channel abnormal detection methods, the way of constructing large scale fading model for energy detection is easy and fast. However, this method has ignored shadow fading which has characteristics of real-time and random changing. Using Markov model as a way to analyze random process has good application prospects in wireless channel modeling. It can be effectively used to analyze the change of shadow fading. First, the threshold of the prior Markov model matrix's similarity should be statistically analyzed. Then, the similarity between the prior and the real-time Markov model matrix is calculated. Whether the regular pattern of shadow fading has changed can be found to compare the two similarities mentioned above, then the abnormality of wireless channel environment detection has finished. Plenty of experimental results based on the simulation show that this method can achieve accurate detection for the Gaussian white noise intrusions.
Among wireless channel abnormal detection methods, the way of constructing large scale fading model for energy detection is easy and fast. However, this method has ignored shadow fading which has characteristics of real-time and random changing. Using Markov model as a way to analyze random process has good application prospects in wireless channel modeling. It can be effectively used to analyze the change of shadow fading. First, the threshold of the prior Markov model matrix's similarity should be statistically analyzed. Then, the similarity between the prior and the real-time Markov model matrix is calculated. Whether the regular pattern of shadow fading has changed can be found to compare the two similarities mentioned above, then the abnormality of wireless channel environment detection has finished. Plenty of experimental results based on the simulation show that this method can achieve accurate detection for the Gaussian white noise intrusions.
引文
[1] 周生奎,戴秀超,朱秋明,等.无线衰落信道模拟方法及仪器研制[J].电子测量与仪器学报,2015,29(7):988-994.ZHOU SH K,DAI X CH,ZHU Q M,et al.Simulation method for fading channel and emulator development[J].Journal of Electronic Measurement and Instrumentation,2015,29(7):988-994.
[2] 宴潇.无线监测中的通用信号检测与识别算法研究[D].北京:北京邮电大学,2015.YAN X.Signal detection and classification algorithms in spectrum monitoring[D].Beijing:Beijing University of Posts and Telecommunications,2015.
[3] ERGIN D,FATIH A,OZGUR B A.Path-loss and correlation analysis for space and polarization diversity in surface ducts[J].IEEE Transactions on Antennas and Propagation,2016,64(10):4498-4503.
[4] 胡石,李光辉,卢文伟,等.基于神经网络的无线传感器网络异常数据检测方法[J].计算机科学,2014,41(11):208-211.HU SH,LI G H,LU W W,et al.Outlier detection methods based on neural network in wireless sensor networks[J].Computer Science,2014,41(11):208-211.
[5] 黄荷,俞亚萍,张之江.基于神经网络的密集人群视频异常检测[J].电子测量技术,2017,40(11):103-107.HUANG H,YU Y P,ZHANG ZH J.Anomaly detection in dense crowd video based on neural network[J].Electronic Measurement Technology,2017,40(11):103-107.
[6] 蔡方萍.基于相关向量机的无线通信系统信道检测[J].计算机工程与设计,2017,38(10):2642-2645.CAI F P.Channel detection in wireless communication system based on relevance vector machine[J].Computer Engineering and Design,2017,38(10):2642-2645.
[7] 段青,赵建国,马艳.优化组合核函数相关向量机电力负荷预测模型[J].电机与控制学报,2010,14(6):33-38.DUAN Q,ZHAO J G,MA Y.Relevance vector machine based on particle swarm optimization of compounding kernels in electricity load forecasting[J].Electric Machines and Control,2010,14(6):33-38.
[8] AHMED M A S,THAREK A R,MARWAN H A,et al.Large-scale path loss models and time dispersion in an outdoor line-of-sight environment for 5G wireless communications[J].International Journal of Electronics and Communications,2016(70):1515-1521.
[9] 曾建奎,何子述.基于隐马尔可夫模型的 MIMO雷达目标检测[J].电子测量与仪器学报,2008,22(4):17-20.ZENG J K,HE Z SH.New detection method for MIMO radar based on hidden Markov mode[J].Journal of Electronic Measurement and Instrument,2008,22(4):17-20.
[10] INDRAKSHI D,GEOFFREY G M,SEBASTIAN M.Joint fading and shadowing model for large office indoor WLAN environments [J].IEEE Transactions on Antennas and Propagation,2014,62(4):2209-2222.
[11] 赵建华,张春晓.基于无线通信的环境检测系统[J].计算机与数字工程,2016,44(10):1963-1966.ZHAO J H,ZHANG CH X.An Environment detection system based on wireless communication[J].Computer and Digital Engineering,2016,44(10):1963-1966.
[12] 钟敦昊,张冬梅,张玉.一种基于相似度计算的无线传感器网络入侵检测方法[J].信息网络安全,2016(2):22-27.ZHONG D H,ZHANG D M,ZHANG Y.A method of intrusion detection in wireless sensor network based on similarity algorithm[J].Netinfo Security,2016(2):22-27.
[13] ANAND S,JIM K,DENNIS G.A Markovian model for coarse-timescale channel variation in wireless networks[J].IEEE Transactions on Vehicular Technology,2016,65(3):1701-1710.
[14] GUSTAVO M A,JORG K L B B.An optimized Markov model to predict link quality in mobile wireless sensor networks[C].IEEE Symposium on Computers ANA Communications,2012:307-312.
[15] DAPHNEY S Z,URBASHI M.Active state tracking with sensing costs:Analysis of two-states and methods for n-states [J].IEEE Transactions on Signal Processing,2017,65(11):2828-2843.
[16] BIN D,ZHENG M,YUAN L.Finite state Markov wiretap channel with delayed feedback[J].IEEE Transactions on Information Forensics and Security,2017,12(3):746-760.
[17] 刘玉磊,梁俊,肖楠,等.基于马尔科夫模型的认知无线电动态双门限能量检测策略[J].电子与信息学报,2016,38(10):2590-2597.LIU Y L,LIANG J,XIAO N,et al.Dynamic double threshold energy detection based on Markov nodel in cognitive Radio[J].Journal of Electronics & Information Technology,2016,38(10):2590-2597.
[2] 宴潇.无线监测中的通用信号检测与识别算法研究[D].北京:北京邮电大学,2015.YAN X.Signal detection and classification algorithms in spectrum monitoring[D].Beijing:Beijing University of Posts and Telecommunications,2015.
[3] ERGIN D,FATIH A,OZGUR B A.Path-loss and correlation analysis for space and polarization diversity in surface ducts[J].IEEE Transactions on Antennas and Propagation,2016,64(10):4498-4503.
[4] 胡石,李光辉,卢文伟,等.基于神经网络的无线传感器网络异常数据检测方法[J].计算机科学,2014,41(11):208-211.HU SH,LI G H,LU W W,et al.Outlier detection methods based on neural network in wireless sensor networks[J].Computer Science,2014,41(11):208-211.
[5] 黄荷,俞亚萍,张之江.基于神经网络的密集人群视频异常检测[J].电子测量技术,2017,40(11):103-107.HUANG H,YU Y P,ZHANG ZH J.Anomaly detection in dense crowd video based on neural network[J].Electronic Measurement Technology,2017,40(11):103-107.
[6] 蔡方萍.基于相关向量机的无线通信系统信道检测[J].计算机工程与设计,2017,38(10):2642-2645.CAI F P.Channel detection in wireless communication system based on relevance vector machine[J].Computer Engineering and Design,2017,38(10):2642-2645.
[7] 段青,赵建国,马艳.优化组合核函数相关向量机电力负荷预测模型[J].电机与控制学报,2010,14(6):33-38.DUAN Q,ZHAO J G,MA Y.Relevance vector machine based on particle swarm optimization of compounding kernels in electricity load forecasting[J].Electric Machines and Control,2010,14(6):33-38.
[8] AHMED M A S,THAREK A R,MARWAN H A,et al.Large-scale path loss models and time dispersion in an outdoor line-of-sight environment for 5G wireless communications[J].International Journal of Electronics and Communications,2016(70):1515-1521.
[9] 曾建奎,何子述.基于隐马尔可夫模型的 MIMO雷达目标检测[J].电子测量与仪器学报,2008,22(4):17-20.ZENG J K,HE Z SH.New detection method for MIMO radar based on hidden Markov mode[J].Journal of Electronic Measurement and Instrument,2008,22(4):17-20.
[10] INDRAKSHI D,GEOFFREY G M,SEBASTIAN M.Joint fading and shadowing model for large office indoor WLAN environments [J].IEEE Transactions on Antennas and Propagation,2014,62(4):2209-2222.
[11] 赵建华,张春晓.基于无线通信的环境检测系统[J].计算机与数字工程,2016,44(10):1963-1966.ZHAO J H,ZHANG CH X.An Environment detection system based on wireless communication[J].Computer and Digital Engineering,2016,44(10):1963-1966.
[12] 钟敦昊,张冬梅,张玉.一种基于相似度计算的无线传感器网络入侵检测方法[J].信息网络安全,2016(2):22-27.ZHONG D H,ZHANG D M,ZHANG Y.A method of intrusion detection in wireless sensor network based on similarity algorithm[J].Netinfo Security,2016(2):22-27.
[13] ANAND S,JIM K,DENNIS G.A Markovian model for coarse-timescale channel variation in wireless networks[J].IEEE Transactions on Vehicular Technology,2016,65(3):1701-1710.
[14] GUSTAVO M A,JORG K L B B.An optimized Markov model to predict link quality in mobile wireless sensor networks[C].IEEE Symposium on Computers ANA Communications,2012:307-312.
[15] DAPHNEY S Z,URBASHI M.Active state tracking with sensing costs:Analysis of two-states and methods for n-states [J].IEEE Transactions on Signal Processing,2017,65(11):2828-2843.
[16] BIN D,ZHENG M,YUAN L.Finite state Markov wiretap channel with delayed feedback[J].IEEE Transactions on Information Forensics and Security,2017,12(3):746-760.
[17] 刘玉磊,梁俊,肖楠,等.基于马尔科夫模型的认知无线电动态双门限能量检测策略[J].电子与信息学报,2016,38(10):2590-2597.LIU Y L,LIANG J,XIAO N,et al.Dynamic double threshold energy detection based on Markov nodel in cognitive Radio[J].Journal of Electronics & Information Technology,2016,38(10):2590-2597.