基于隐马尔可夫模型的动态跳频信道接入算法
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摘要
针对认知无线电信道接入中存在主用户和次级用户相互干扰、吞吐量下降的问题,提出基于隐马尔可夫模型的动态跳频信道接入算法。由于长时段的数据传输造成次级用户无法及时切换信道,将数据传输时段分为多个跳频时段,构建隐马尔可夫模型预测可用信道,建立跳频信道集合,设计动态跳频序列,使信道接入的效率更高。理论分析与仿真结果表明,该算法能够有效降低各级用户之间干扰概率并显著改善系统吞吐量,增加系统可靠性和信道利用率。
A dynamic frequency hopping channel access algorithm is proposed based on the hidden Markov model.It helps to improve the interference of primary users and secondary users,and the decrease of throughput.Long-time data transmission leads to the fact that secondary users cannot switch channels timely,so we divide the long transmission time into several frequency hopping periods,build the hidden Markov model to predict the available channels,set up frequency hopping channel set,and design dynamic frequency hopping sequence.Theoretical analysis and simulation results show that the algorithm reduces the interference of primary users and secondary users obviously,and improves system throughput effectively,and it increases system reliability and channel utilization.
A dynamic frequency hopping channel access algorithm is proposed based on the hidden Markov model.It helps to improve the interference of primary users and secondary users,and the decrease of throughput.Long-time data transmission leads to the fact that secondary users cannot switch channels timely,so we divide the long transmission time into several frequency hopping periods,build the hidden Markov model to predict the available channels,set up frequency hopping channel set,and design dynamic frequency hopping sequence.Theoretical analysis and simulation results show that the algorithm reduces the interference of primary users and secondary users obviously,and improves system throughput effectively,and it increases system reliability and channel utilization.
引文
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[23]XU L,FANG H,LIN Z.Evolutionarily stable opportunistic spectrum access in cognitive radio networks[J].Iet Communications,2016,10(17):2290-2299.
[24]WANG Q,REN K,NING P,et al.Jamming-resistant multiradio multichannel opportunistic spectrum access in cognitive radio networks[J].IEEE Trans.on Vehicular Technology,2016,65(10):8331-8344.
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[27]LIU X,GONG C,LIU B,et al.Hidden markov model-based signal characterization for visible light communication under weak illuminance[J].Journal of Lightwave Technology,2018,36(9):1730-1738.
[28]MITCHELL C,HARPER M,JAMIESON L.On the complexity of explicit duration HMMs[J].IEEE Trans.on Acoustics Speech&Signal Processing,1995,3(3):213-217.
[2]任晓岳,陈长兴.认知无线电传感器网络自适应接入控制策略[J].系统工程与电子技术,2017,39(9):2100-2108.REN X Y,CHEN C X.Adaptive access control strategy of cognitive radio wireless sensor network[J].Systems Engineering and Electronics,2017,39(9):2100-2108.
[3]杨双懋,郭伟,唐伟.认知无线网络中基于时间序列预测的冲突分解算法[J].通信学报,2011,32(11):51-58.YANG S M,GUO W,TANG W.Collision resolution algorithm based on time-series forecasting for cognitive wireless networks[J].Journal on Communications,2011,32(11):51-58.
[4]ROY A.A novel multivariate fuzzy time series based forecasting algorithm incorporating the effect of clustering on prediction[J].Soft Computing,2016,20(5):1991-2019.
[5]MA X,LIU Z,WEI Y,et al.A novel kernel regularized nonlinear GMC(1,n)model and its application[J].Journal of Grey System,2016,28(3):97-109.
[6]LOPEZ O L A,SANCHEZ S M,MAFRA S B,et al.Power control and relay selection in cognitive radio ad hoc networks using game theory[J].IEEE Systems Journal,2016,12(3):2854-2865.
[7]SUPRAJA P,PITCHAI R,RAJ A.Spectrum prediction in cognitive radio with hybrid optimized neural network[J].Mobile Networks&Applications,2017,2(9):1-8.
[8]AZMAT F,CHEN Y,STOCKS N.Analysis of spectrum occupancy using machine learning algorithms[J].IEEE Trans.on Vehicular Technology,2016,65(9):6853-6860.
[9]WANG W,LIU J,YANG F,et al.A method of dynamic spectrum access in hd radio based on bp neural network[C]∥Proc.of the International Conference on Wireless Communication and Sensor Network,2015:180-183.
[10]WANG J W,ADRIMAN R.Analysis of opportunistic spectrum access in cogni-tive radio networks using hidden Markov model with state prediction[J].Eurasip Journal on Wireless Communications&Networking,2015,10(1):1-8.
[11]SAAD A,STAEHLE B,KNORR R.Spectrum prediction using hidden Markov models for industrial cognitive radio[C]∥Proc.of the 12th IEEE International Conference on Wireless and Mobile Computing,Networking and Communications,2016:1-7.
[12]HEYDARI R,ALIREZAEE S,MAKKI S V,et al.Cognitive radio channel behavior prediction using the hidden Markov model[C]∥Proc.of the 7th International Symposium on Telecommunications,2015:993-998.
[13]CHATZIANTONIOU E,ALLEN B,VELISAVLJEVIC V.An HMM-based spectrum occupancy predictor for energy efficient cognitive radio[C]∥Proc.of IEEE International Symposium on Personal Indoor and Mobile Radio Communications,2013:601-605.
[14]ELTOM H,KANDEEPAN S,LIANG Y C,et al.HMM based cooperative spectrum occupancy prediction using hard fusion[C]∥Proc.of the IEEE International Conference on Communications Workshops,2016:669-675.
[15]ANDERSON R H,KROLIK J L.Over-the-horizon radar target localization using a hidden Markov model estimated from ionosonde data[J].Radio Science,2016,33(4):1199-1213.
[16]HEYDARI R,ALIREZAEE S,AHMADI A,et al.Primary user activity prediction using the hidden Markov model in cognitive radio networks[C]∥Proc.of the International Symposium on Signals,Circuits and Systems,2015:1-4.
[17]李彤,苗成林,吕军.基于功率谱对消的时域相关认知跳频谱检测算法[J].兵工学报,2017,38(9):1754-1760.LI T,MIAO C L,LYU J.Time-domain correlation algorithm of cognitive frequency hopping based on power spectrum cancellation[J].Acta Armamentarii,2017,38(9):1754-1760.
[18]WILLKOMM D,BOHGE M,HOLLOS D,et al.Double hopping:a new approach for dynamic frequency hopping in cognitive radio networks[C]∥Proc.of the 19th IEEE International Symposium on Personal,Indoor and Mobile Radio Communications,2008:1-6.
[19]陶乃顺,鲜永菊,徐昌彪.基于动态跳频的频谱接入[J].计算机应用研究,2010,27(12):4684-4685.TAO N S,XIAN Y J,XU C B.Spectrum access based on dynamic frequency hopping[J].Application Research of Computers,2010,27(12):4684-4685.
[20]MELIAN-GUTIERREZ L,MODI N,MOY C,et al.Hybrid UCB-HMM:a machine learning strategy for cognitive radio in HF band[J].IEEE Trans.on Cognitive Communications&Networking,2016,1(3):347-358.
[21]IEEE Std 802.22.Cognitive wireless ran medium access control and physical layer specifications[S].2011.
[22]FERREIRA P V R,PAFFENROTH R,WYGLINSKI A M,et al.Multi-objective reinforcement learning for cognitive satellite communications using deep neural network ensembles[J].IEEE Journal on Selected Areas in Communications,2018,36(5):1030-1041.
[23]XU L,FANG H,LIN Z.Evolutionarily stable opportunistic spectrum access in cognitive radio networks[J].Iet Communications,2016,10(17):2290-2299.
[24]WANG Q,REN K,NING P,et al.Jamming-resistant multiradio multichannel opportunistic spectrum access in cognitive radio networks[J].IEEE Trans.on Vehicular Technology,2016,65(10):8331-8344.
[25]TANG W,SHAKIR M.Throughput analysis for cognitive radio networks with multiple primary users and imperfect spectrum sensing[J].IET Communications,2012,6(17):2787-2795.
[26]ZHANG J,WANG F,ZHONG Z,et al.Continuous phase modulation classification via Baum-Welch algorithm[J].IEEECommunications Letters,2018,22(7):1390-1393.
[27]LIU X,GONG C,LIU B,et al.Hidden markov model-based signal characterization for visible light communication under weak illuminance[J].Journal of Lightwave Technology,2018,36(9):1730-1738.
[28]MITCHELL C,HARPER M,JAMIESON L.On the complexity of explicit duration HMMs[J].IEEE Trans.on Acoustics Speech&Signal Processing,1995,3(3):213-217.