基于深度置信网络的超宽带信道环境的分类算法
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摘要
对超宽带信道环境进行分类、识别,对于实现特定场景分析以及无线网络的优化具有重要意义。针对这一背景提出了一种基于深度置信网络(deep belief network,DBN)的信道环境分类方法,对不同的信道场景特征进行提取、分类。实验结果表明:提出的算法能有效地实现信道环境的分类识别。
Classifying and identifying the ultra wideband (UWB) channel environment for specific scene analysis and optimization of wireless networks are of great significance. Aiming at this background,a channel environment classification method based on deep belief net (DBN) algorithm was proposed to extract the features of different channel scenes and classify them. Experimental results show that the proposed algorithm can effectively realize the classification and recognition of the channel environment.
Classifying and identifying the ultra wideband (UWB) channel environment for specific scene analysis and optimization of wireless networks are of great significance. Aiming at this background,a channel environment classification method based on deep belief net (DBN) algorithm was proposed to extract the features of different channel scenes and classify them. Experimental results show that the proposed algorithm can effectively realize the classification and recognition of the channel environment.
引文
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12刘方园,王水花,张煜东.深度置信网络模型及应用研究综述[J].计算机工程与应用,2018,54(1):11-18Liu Fangyuan,Wang Shuihua,Zhang Yudong.Survey on deep belief network model and its applications[J].Computer Engineering and Applications,2018,54(1):11-18
13张绍辉.集成参数自适应调整及隐含层降噪的深层RBM算法[J].自动化学报,2017,43(5):855-865Zhang Shaohui.Deep RBM algorithm with adaptive adjustment parameters and denoising in hidden[J].Acta Automatica Sinica,2017,43(5):855-865
14 Jun C,Qin Y,Yi Z.Speech signals identification base on improved DBN[C]//IEEE 2nd Advanced Information Technology,Electronic and Automation Control Conference(IAEAC).Chongqing,China:IEEE,2017:1144-1148
15 Molisch A F,Cassioli D,Chong C C,et al.A comprehensive standardized model for ultrawideband propagation channels[J].IEEETransactions on Antennas Propagation,2006,54(11):3151-3166
2 Yajnanarayana V,Handel P.Joint estimation of TOA and PPM symbols using sub-nyquist sampled IR-UWB signal[J].IEEE Communications Letters,2017,21(4):949-952
3翟双,钱志鸿,王雪,等.IR-UWB系统中基于归一化门限的TOA估计[J].北京邮电大学学报,2015,38(4):19-23Zhai Shuang,Qian Zhihong,Wang Xue,et al.TOA estimation based on normalized threshold in IR-UWB systems[J].Journal of Beijing University of Posts and Telecommunications,2015,38(4):19-23
4 Wu D,Chatzigeorgiou D,Youcef-Toumi K.Node localization in robotic sensor networks for pipeline inspection[J].IEEE Transactions on Industrial Informatics,2016,12(2):809-819
5 SáA O D,Nedjah N,Mourelle L D M.Distributed and resilient localization algorithm for swarm robotic systems[J].Applied Soft Computing,2016,57:738-750
6 Abouzar P,Michelson D G,Hamdi M.RSSI-based distributed selflocalization for wireless sensor networks used in precision agriculture[J].IEEE Transactions on Wireless Communications,2016,15(10):6638-6650
7陈瑜,张铁民,孙道宗,等.基于无线传感器网络的设施农业车辆定位系统设计与试验[J].农业工程学报,2015,31(10):190-197Chen Yu,Zhang Tiemin,Sun Daozong,et al.Design and experiment of locating system for facilities agricultural vehicle based on wireless sensor network[J].Transactions of the Chinese Society of Agricultural Engineering,2015,31(10):190-197
8孙希延,刘健,纪元法,等.基于似然比检验的超宽带信道检测与定位算法[J].电子与信息学报,2017,39(3):590-597Sun Xiyan,Liu Jian,Ji Yuanfa,et al.UWB channel detection and location algorithm based on likelihood ratio test[J].Journal of Electronics&Information Technology,2017,39(3):590-597
9 Albaidhani A,Morell A,Vicario J L.Ranging in UWB using commercial radio modules:experimental validation and NLOSmitigation[C]//2016 International Conference on Indoor Positioning and Indoor Navigation.Piscataway,USA:IEEE,2016:1-7
10 Muqaibel A H,Landolsi M A,Mahmood M N.Practical evaluation of NLOS/LOS parametric classification in UWB channels[C]//1st International Conference on Communications,Signal Processing,and their Applications(ICCSPA).Sharjah,United Arab Emirates:IEEE,2013:1-6
11张浩,梁晓林,吕婷婷,等.一种新颖的基于偏度的非视距区分算法[J].电讯技术,2015,55(5):484-490Zhang Hao,Liang Xiaolin,LüTingting,et al.A novel non-line-ofsight identification algorithm based on skewness[J].Telecommunication Engineering,2015,55(5):484-490
12刘方园,王水花,张煜东.深度置信网络模型及应用研究综述[J].计算机工程与应用,2018,54(1):11-18Liu Fangyuan,Wang Shuihua,Zhang Yudong.Survey on deep belief network model and its applications[J].Computer Engineering and Applications,2018,54(1):11-18
13张绍辉.集成参数自适应调整及隐含层降噪的深层RBM算法[J].自动化学报,2017,43(5):855-865Zhang Shaohui.Deep RBM algorithm with adaptive adjustment parameters and denoising in hidden[J].Acta Automatica Sinica,2017,43(5):855-865
14 Jun C,Qin Y,Yi Z.Speech signals identification base on improved DBN[C]//IEEE 2nd Advanced Information Technology,Electronic and Automation Control Conference(IAEAC).Chongqing,China:IEEE,2017:1144-1148
15 Molisch A F,Cassioli D,Chong C C,et al.A comprehensive standardized model for ultrawideband propagation channels[J].IEEETransactions on Antennas Propagation,2006,54(11):3151-3166