基于隐马尔可夫模型的频率捷变干扰源频谱占用预测方法研究
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摘要
在频谱监测工作中,往往需要掌握感兴趣地域内辐射源的频谱占用状态,从而对电磁环境实施有效管控。本文面向频率捷变干扰源设计了一种基于隐马尔可夫模型的频谱占用预测方法。首先,构建了频谱监测场景下面向频率捷变干扰源的频谱占用预测系统方案;进而,采用前向和后向迭代来计算观测序列似然概率,采用BaumWelch算法来估计隐马尔可夫模型参数;最后,通过预测状态概率估计值来判定频谱占用状态。仿真结果表明,本文方法性能良好,能够在仅有观测数据的条件下,对频率捷变干扰源的下一步频谱占用状态进行有效预测。
In spectrum monitoring work,the spectrum occupancy state is usually needed to knowledge well and then the effective measures are carried to management and control the electromagnetic environment.A HMM(hidden Markov model)based spectrum occupancy prediction method for frequency agile interference source is designed in this paper.Firstly,under the spectrum monitoring scenario,a spectrum occupancy prediction system scheme for frequency agile interference source is constructed.Secondly,the forward and backward recursions are used to calculate the likelihood of the observed signal and the Baum-Welch algorithm is used to estimate the parameters of HMM.Finally,the predicted spectrum occupancy state is estimated.Simulation results show the designed method has good performance of the next-step spectrum occupancy state prediction without any apriori information but the observed data.
In spectrum monitoring work,the spectrum occupancy state is usually needed to knowledge well and then the effective measures are carried to management and control the electromagnetic environment.A HMM(hidden Markov model)based spectrum occupancy prediction method for frequency agile interference source is designed in this paper.Firstly,under the spectrum monitoring scenario,a spectrum occupancy prediction system scheme for frequency agile interference source is constructed.Secondly,the forward and backward recursions are used to calculate the likelihood of the observed signal and the Baum-Welch algorithm is used to estimate the parameters of HMM.Finally,the predicted spectrum occupancy state is estimated.Simulation results show the designed method has good performance of the next-step spectrum occupancy state prediction without any apriori information but the observed data.
引文
[1]XING X S,JING T,CHENG W,et al.Spectrum prediction in cognitive radio networks[J].IEEE Wireless Communication,2013,20(2):90-96.
[2]UNNIKRISHNAN J,VEERAVALLI V.Cooperative sensing for primary detection in cognitive radio[J].IEEE Journal Sel.Topics Signal Process.,2008,2(1):18-27.
[3]张凯.基于隐马尔可夫模型的频谱预测和感知方法研究[D].南京:南京邮电大学,2015.
[4]CHEN Y F,HEE S O.A survey of measurementbased spectrum occupancy modeling for cognitive radios[J].IEEE Communication Surveys&Tutorials,2016,18(1):848-859.
[5]WEN Z G,FAN C X,ZHANG X Y,et al.Autoregressive spectrum hole prediction model for cognitive radio systems.IEEE ICC Workshops,May 19-23,2008[C].Beijing:IEEE,2008.
[6]TUMULURU V,WANG P,NIYATO D.A neural network based spectrum prediction scheme for cognitive radio.2010IEEE International Conference on Communications,May 23-27,2010[C].Cape Town:IEEE,2010.
[7]徐元,鲁华祥,陈旭.基于支持向量机的认知无线电频谱预测方法[J].电信科学,2014,30(11):87-92.
[8]GHOSH C,GORDEIRO C,AGRAWAL D,et al.Markov chain existence and hidden Markov models in spectrum sensing.2009IEEE International Conference on Pervasive Computing and Communications,March 9-13,2009[C].Galveston:IEEE,2009.
[9]HYUN P C,WON K S,RAIN L S.HMM based channel state predictor for cognitive radio.2007AsiaPacific Microwave Conference,December 11-14,2007[C].Bangkok:IEEE,2008.
[10]NGUYEN T,MARK B L,EPHRAIM Y.Spectrum sensing using a hidden bivariate markov model[J].IEEE Transactions On WirelessCommunications,2013,12(9):4582-4591.
[11]NGUYEN,NHU T T.Hidden markov model based spectrum sensing for cognitive radio[D].Fairfax:George Mason University,2013.
[2]UNNIKRISHNAN J,VEERAVALLI V.Cooperative sensing for primary detection in cognitive radio[J].IEEE Journal Sel.Topics Signal Process.,2008,2(1):18-27.
[3]张凯.基于隐马尔可夫模型的频谱预测和感知方法研究[D].南京:南京邮电大学,2015.
[4]CHEN Y F,HEE S O.A survey of measurementbased spectrum occupancy modeling for cognitive radios[J].IEEE Communication Surveys&Tutorials,2016,18(1):848-859.
[5]WEN Z G,FAN C X,ZHANG X Y,et al.Autoregressive spectrum hole prediction model for cognitive radio systems.IEEE ICC Workshops,May 19-23,2008[C].Beijing:IEEE,2008.
[6]TUMULURU V,WANG P,NIYATO D.A neural network based spectrum prediction scheme for cognitive radio.2010IEEE International Conference on Communications,May 23-27,2010[C].Cape Town:IEEE,2010.
[7]徐元,鲁华祥,陈旭.基于支持向量机的认知无线电频谱预测方法[J].电信科学,2014,30(11):87-92.
[8]GHOSH C,GORDEIRO C,AGRAWAL D,et al.Markov chain existence and hidden Markov models in spectrum sensing.2009IEEE International Conference on Pervasive Computing and Communications,March 9-13,2009[C].Galveston:IEEE,2009.
[9]HYUN P C,WON K S,RAIN L S.HMM based channel state predictor for cognitive radio.2007AsiaPacific Microwave Conference,December 11-14,2007[C].Bangkok:IEEE,2008.
[10]NGUYEN T,MARK B L,EPHRAIM Y.Spectrum sensing using a hidden bivariate markov model[J].IEEE Transactions On WirelessCommunications,2013,12(9):4582-4591.
[11]NGUYEN,NHU T T.Hidden markov model based spectrum sensing for cognitive radio[D].Fairfax:George Mason University,2013.