机器学习在信道建模中的应用综述

刘留; 张建华; 樊圆圆; 于力; 张嘉驰

doi:10.11959/j.issn.1000-436x.2021001

您当前的位置：

首页 >

文章列表页 >

机器学习在信道建模中的应用综述

综述 | 更新时间：2024-06-05

- 机器学习在信道建模中的应用综述
- Survey of application of machine learning in wireless channel modeling
- 通信学报 2021年42卷第2期页码：134-153
- 作者机构：
  
  1. 北京交通大学电子信息工程学院，北京 100044
  2. 北京邮电大学网络与交换技术国家重点实验室，北京 100876
- 作者简介：
  
  [ "刘留（1981- ），男，云南昆明人，博士，北京交通大学教授、博士生导师，主要研究方向为无线信道测量与建模、时变信道信号处理、5G关键技术、高铁宽带接入物理层关键技术等。" ]
  [ "张建华（1976- ），女，河北迁安人，博士，北京邮电大学教授、博士生导师，主要研究方向为移动通信信道建模理论和传输技术研究等。" ]
  [ "樊圆圆（1997- ），女，河南信阳人，北京交通大学硕士生，主要研究方向为无线信道建模、无线信道场景识别和机器学习。" ]
  [ "于力（1989- ），男，浙江杭州人，北京邮电大学博士生，主要研究方向为无线信道建模、信道预测和机器学习。" ]
  [ "张嘉驰（1991- ），男，山东潍坊人，北京交通大学博士生，主要研究方向为高铁移动通信、无线信道建模、信道测量和机器学习。" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2018YFB1801101);国家自然科学基金杰出青年基金资助项目(61925102);北京市自然科学基金–海淀原始创新联合基金资助项目(L172030);国家自然科学基金重点资助项目(61931001)
- DOI：10.11959/j.issn.1000-436x.2021001
  中图分类号： TN929.5
- 网络首发：2021-02，
  
  纸质出版：2021-02-25
- 稿件说明：
移动端阅览
刘留, 张建华, 樊圆圆, 等. 机器学习在信道建模中的应用综述[J]. 通信学报, 2021,42(2):134-153.

Liu LIU, Jianhua ZHANG, Yuanyuan FAN, et al. Survey of application of machine learning in wireless channel modeling[J]. Journal on Communications, 2021, 42(2): 134-153.
刘留, 张建华, 樊圆圆, 等. 机器学习在信道建模中的应用综述[J]. 通信学报, 2021,42(2):134-153. DOI： 10.11959/j.issn.1000-436x.2021001.

Liu LIU, Jianhua ZHANG, Yuanyuan FAN, et al. Survey of application of machine learning in wireless channel modeling[J]. Journal on Communications, 2021, 42(2): 134-153. DOI： 10.11959/j.issn.1000-436x.2021001.

摘要

信道建模是设计无线通信系统的基础，传统的信道建模方法无法自动学习特定类型信道的规律，特别是在针对特殊应用场景，如物联网、毫米波通信、车联网等，存在一定的局限性。此外，机器学习具有有效处理大数据、创建模型的能力，基于此，探讨了机器学习如何与信道建模进行有机融合，分别从信道多径分簇、参数估计、模型的构造及信道的场景识别展开了讨论，对当前该领域的重要研究成果进行了阐述，并对未来发展提出了展望。

Abstract

Channel characterization is primary to the design of the wireless communication system.The conventional channel characterization method cannot learn the law of certain types of channels by itself

which limits its application in several special scenarios

such as Internet of things

millimeter wave communication and Internet of vehicles.Machine learning was able to process the big data and establish the model.Based on this

the cooperation between the machine learning and channel characterization was investigated.The channel multipath clustering

parameter estimation

model construction and wireless channel scene recognition were discussed

and recent significant research results in this field were provided.Finally

the future direction of the machine learning in wireless channel modeling was proposed.

关键词

Keywords

references

MOLISCH A F . Wireless communications [M ] . New Jersey : John Wiley ＆ Sons , 2015 .

ZHANG J . The interdisciplinary research of big data and wireless channel:a cluster-nuclei based channel model [J ] . China Communication , 2016 , 13 ( 2 ): 14 - 26 .

HE R S , DING Z G . Applications of machine learning in wireless communications [M ] . London : IET , 2019 .

O’SHEA T , HOYDIS J . An introduction to deep learning for the physical layer [J ] . IEEE Transactions on Cognitive Communications and Networking , 2017 , 3 ( 4 ): 563 - 575 .

WANG C X , BIAN J , SUN J , et al . A survey of 5G channel measurements and models [J ] . IEEE Communications Surveys ＆ Tutorials , 2018 , 20 ( 4 ): 3142 - 3168 .

MA X , ZHANG J , ZHANG Y , et al . A PCA-based modeling method for wireless MIMO channel [C ] // 2017 IEEE Conference on Computer Communications Workshops . Piscataway:IEEE Press , 2017 : 874 - 879 .

HUANG H , GUO S , GUI G , et al . Deep learning for physical-layer 5G wireless techniques:opportunities,challenges and solutions [J ] . IEEE Wireless Communications , 2019 , 27 ( 1 ): 214 - 222 .

刘留 , 陶成 , 陈后金 , 等 . 高速铁路无线传播信道测量与建模综述 [J ] . 通信学报 , 2014 , 35 ( 1 ): 119 - 131 .

LIU L , TAO C , CHEN H J , et al . Survey of wireless channel mea-surement and characterization for high-speed railway scenarios [J ] . Journal on Communications , 2014 , 35 ( 1 ): 119 - 131 .

WANG C , GHAZAL A , AI B , et al . Channel measurements and models for high-speed train communication systems:a survey [J ] . IEEE Communications Surveys and Tutorials , 2016 , 18 ( 2 ): 974 - 987 .

UWAECHIA A N , MAHYUDDIN N M . A comprehensive survey on millimeter wave communications for fifth-generation wireless networks:feasibility and challenges [J ] . IEEE Access , 2020 , 8 : 62367 - 62414 .

SHAFI M , ZHANG J , TATARIA H , et al . Microwave vs.millimeter-wave propagation channels:key differences and impact on 5G cellular systems [J ] . IEEE Communications Magazine , 2018 , 56 ( 12 ): 14 - 20 .

田磊 , 张建华 . IMT-2020 信道模型标准综述 [J ] . 北京邮电大学学报 , 2018 , 41 ( 5 ): 66 - 72 .

TIAN L , ZHANG J H . Overview of IMT-2020 channel model stan-dard [J ] . Journal of Beijing University of Posts and Telecommunica-tions , 2018 , 41 ( 5 ): 66 - 72 .

ZHANG J , TANG P , YU L , et al . Channel measurements and models for 6G:current status and future outlook [J ] . Frontiers of Information Technology ＆ Electronic Engineering , 2020 , 21 ( 1 ): 39 - 61 .

MOLISCH A F , TUFVESSON F , KAREDAL J , et al . A survey on vehicle-to-vehicle propagation channels [J ] . IEEE Wireless Communications , 2009 , 16 ( 6 ): 12 - 22 .

KHUWAJA A A , CHEN Y , ZHAO N , et al . A survey of channel modeling for UAV communications [J ] . IEEE Communications Surveys and Tutorials , 2018 , 20 ( 4 ): 2804 - 2821 .

ZHENG K , OU S , YIN X . Massive MIMO channel models:a survey [J ] . International Journal of Antennas and Propagation , 2014 , 3 : 1 - 10 .

ZHAO X , DU F , GENG S , et al . Neural network and GBSM based time-varying and stochastic channel modeling for 5G millimeter wave communications [J ] . China Communications , 2019 , 16 ( 6 ): 80 - 90 .

杜德涛 . 基于测量的3D MIMO信道分簇建模及高频段信道建模研究 [D ] . 北京:北京邮电大学 , 2015 .

DU D T . 3D MIMO channel clustering modeling and high-frequency channel modeling based on measurement [D ] . Beijing:Beijing University of Posts and Telecommunications , 2015 .

薛汉卿 . 面向5G信道的新型聚簇算法研究 [D ] . 北京:北京邮电大学 , 2019 .

XUE H Q . Research on a new clustering algorithm for 5G channels [D ] . Beijing:Beijing University of Posts and Telecommunications , 2019 .

CZINK N , CERA P , SALO J , et al . Automatic clustering of MIMO channel parameters using the multi-path component distance measure [M ] . Washington,D.C. : Electronics Letters , 2005 .

MOHRI M , ROSTAMIZADEH A , TALWALKAR A . Foundations of machine learning [M ] . Cambridge : The MIT Press , 2012 .

LI J , AI B , HE R , et al . Cluster-based 3D channel modeling for massive MIMO in subway station environment [J ] . IEEE Access , 2018 , 6 : 6257 - 6272 .

LI Y , ZHANG J , MA Z , et al . Clustering analysis in the wireless propagation channel with a variational Gaussian mixture model [J ] . IEEE Transactions on Big Data , 2018 , 6 ( 2 ): 223 - 232 .

LI Y , ZHANG J , MA Z . Clustering in wireless propagation channel with a statistics-based framework [C ] // 2018 IEEE Wireless Communications and Networking Conference . Piscataway:IEEE Press , 2018 : 1 - 6 .

HE R , AI B , MOLISCH A F , et al . Clustering enabled wireless channel modeling using big data algorithms [J ] . IEEE Communications Magazine , 2018 , 56 ( 5 ): 177 - 183 .

HE R , LI Q , AI B , et al . A kernel-power-density-based algorithm for channel multipath components clustering [J ] . IEEE Transactions on Wireless Communications , 2017 , 16 ( 11 ): 7138 - 7151 .

DU F , ZHAO X , GENG S , et al . An improved KPD algorithm of multipath components clustering for 5G millimeter wave radio channels [C ] // 2018 12th International Symposium on Antennas,Propagation and EM Theory . Piscataway:IEEE Press , 2018 : 1 - 4 .

HUANG C , HE R , ZHONG Z , et al . A power-angle-spectrum based clustering and tracking algorithm for time-varying radio channels [J ] . IEEE Transactions on Vehicular Technology , 2018 , 68 ( 1 ): 291 - 305 .

HUANG C , HE R , ZHONG Z , et al . A novel target recognition based radio channel clustering algorithm [C ] // 2018 10th International Conference on Wireless Communications and Signal Processing . Piscataway:IEEE Press , 2018 : 1 - 6 .

GUO B , TIAN L , ZHANG J , et al . A clustering algorithm based on joint kernel density for millimeter wave radio channels [C ] // 2019 13th European Conference on Antennas and Propagation . Piscataway:IEEE Press , 2019 : 1 - 5 .

HE R , CHEN W , AI B , et al . On the clustering of radio channel impulse responses using sparsity-based methods [J ] . IEEE Transactions on Antennas and Propagation , 2016 , 64 ( 6 ): 2465 - 2474 .

WANG C , ZHANG J , YU G . Cluster analysis of pedestrian mobile channels in measurements and simulations [J ] . Applied Sciences , 2019 , 9 ( 5 ): 886 .

WANG Q , AI B , HE R , et al . A framework of automatic clustering and tracking for time-variant multipath components [J ] . IEEE Communications Letters , 2016 , 21 ( 4 ): 953 - 956 .

SCHNEIDER C , BAUER M , NARANDZIC M , et al . Clustering of MIMO channel parameters-performance comparison [C ] // VTC Spring 2009-IEEE 69th Vehicular Technology Conference . Piscataway:IEEE Press , 2009 : 1 - 5 .

GENTILE C . Using the kurtosis measure to identify clusters in wireless channel impulse responses [J ] . IEEE Transactions on Antennas and Propagation , 2013 , 61 ( 6 ): 3392 - 3395 .

XUE H , TIAN L , ZHANG J , et al . A novel clustering method based on density peaks and its validation by channel measurement [C ] // 2018 12th European Conference on Antennas and Propagation . Piscataway:IEEE Press , 2018 : 1 - 5 .

HUANG C , HE R , ZHONG Z , et al . A novel tracking-based multipath component clustering algorithm [J ] . IEEE Antennas and Wireless Propagation Letters , 2017 , 16 : 2679 - 2683 .

LI J , AI B , HE R , et al . A cluster-based channel model for massive MIMO communications in indoor hotspot scenarios [J ] . IEEE Transactions on Wireless Communications , 2019 , 18 ( 8 ): 3856 - 3870 .

张建华 , 张平 , 高新颖 , 等 . 一种信道参数估计方法和系统:CN201110251597.3 [P ] .(2011-08-29)[2020-06-01 ] .

ZHANG J H , ZHANG P , GAO X X , et al . A channel parameter esti-mation method and system:CN201110251597.3 [P ] .(2011-08-29)[2020-06-01 ] .

BAI F , VIDAL-CALLEJA T , HUANG S . Robust incremental SLAM under constrained optimization formulation [J ] . IEEE Robotics and Automation Letters , 2018 , 3 ( 2 ): 1207 - 1214 .

EBRAHIMI A , RAHIMIAN A . Estimation of channel parameters in a multipath environment via optimizing highly oscillatory error functions using a genetic algorithm [C ] // 2007 15th International Conference on Software,Telecommunications and Computer Networks . Piscataway:IEEE Press , 2007 : 1 - 5 .

HUA L , WEI Z ， QING-HUA Z , et al . Blind estimation of MIMO channels using genetic algorithm [C ] // 2009 Fifth International Conference on Natural Computation . Piscataway:IEEE Press , 2009 : 163 - 167 .

SUN Y , ZHANG J , PAN C , et al . Optimal and computational-efficient detection and estimation of multi-paths in channel sounding [C ] // Vehicular Technology Conference . Piscataway:IEEE Press , 2013 : 1 - 6 .

SHUTIN D , FLEURY B H . Sparse variational Bayesian SAGE algorithm with application to the estimation of multipath wireless channels [J ] . IEEE Transactions on Signal Processing , 2011 , 59 ( 8 ): 3609 - 3623 .

刘留 , 李慧婷 , 张嘉驰 , 等 . 基于神经网络的时变无线信道仿真 [J ] . 北京交通大学学报 , 2020 , 44 ( 2 ): 74 - 82 .

LIU L , LI H T , ZHANG J C , et al . Time-varying wireless channel si-mulation based on neural network [J ] . Journal of Beijing Jiaotong Uni-versity , 2020 , 44 ( 2 ): 74 - 82 .

YANG Y , LI Y , ZHANG W , et al . Generative-adversarial-networkbased wireless channel modeling:challenges and opportunities [J ] . IEEE Communications Magazine , 2019 , 57 ( 3 ): 22 - 27 .

杨大成 . 移动传播环境 [M ] . 北京 : 机械工业出版社 , 2003 .

YANG D C . Mobile communication environment [M ] . Beijing : Ma-chinery Industry Press , 2003 .

ERPEK T , O’SHEA T J , SAGDUYU Y E , et al . Deep learning for wireless communications [M ] . Berlin : Springer , 2020 .

魏畅 . 无线信道的确定性建模与分析 [D ] . 南京:南京邮电大学 , 2013 .

WEI C . Deterministic modeling and analysis of wireless channels [D ] . Nanjing:Nanjing University of Posts and Communications , 2013 .

高波 . 基于几何基础上的随机 MIMO 信道模型及仿真研究 [D ] . 北京:华北电力大学(北京) , 2016 .

GAO B . Model and simulation of random MIMO channel based on geometry [D ] . Beijing:North China Electric Power University (Bei-jing) , 2016 .

CHEN L , HAIHAN L I , YUNZHOU L I , et al . A learning-based channel model for synergetic transmission technology [J ] . China Communications , 2015 , 12 ( 9 ): 83 - 92 .

尤肖虎 , 张川 , 谈晓思 , 等 . 基于 AI 的 5G 技术研究方向与范例 [J ] . 中国科学:信息科学 , 2018 , 48 ( 12 ): 1589 - 1602 .

YOU X H , ZHANG C , TAN X S , et al . Research directions and exam-ples of AI-based 5G technology [J ] . Science in China:Information Science , 2018 , 48 ( 12 ): 1589 - 1602 .

SARMA K K , MITRA A . Multiple-input-multiple-output channel modelling using multi-layer perceptron with finite impulse response and infinite impulse response synapses [J ] . IET Communications , 2013 , 7 ( 14 ): 1540 - 1549 .

LIU J , JIN X , DONG F , et al . Fading channel modelling using single-hidden layer feed forward neural networks [J ] . Multidimensional Systems and Signal Processing , 2017 , 28 ( 3 ): 885 - 903 .

SHA Y , XU X , YAO N . Wireless channel model based on RBF neural network [C ] // 2008 Fourth International Conference on Natural Computation . Piscataway:IEEE Press , 2008 : 605 - 609 .

HUANG J , WANG C X , BAI L , et al . A big data enabled channel model for 5G wireless communication systems [J ] . IEEE Transactions on Big Data , 2018 , 6 ( 2 ): 211 - 222 .

BAI L , WANG C , HUANG J , et al . Predicting wireless mmWave massive MIMO channel characteristics using machine learning algorithms [J ] . Wireless Communications and Mobile Computing , 2018 , 2018 : 1 - 12 .

周志华 . 机器学习 [M ] . 北京 : 清华大学出版社 , 2016 .

ZHOU Z H . Machine learning [M ] . Beijing : Tsinghua University Press , 2016 .

FARUK N , POPOOLA S I,SURAJUDEEN-BAKINDE N T , et al . Path loss predictions in the VHF and UHF bands within urban environments:experimental investigation of empirical,heuristics and geospatial models [J ] . IEEE Access , 2019 , 7 : 77293 - 77307 .

NESKOVIC A , NESKOVIC N , PAUNOVIC D . Indoor electric field level prediction model based on the artificial neural networks [J ] . IEEE Communications Letters , 2000 , 4 ( 6 ): 190 - 192 .

ROMO J A , ANITZINE I F , FONTAN F P . Application of neural networks to field strength prediction for indoor environments [C ] // 2006 First European Conference on Antennas and Propagation . Piscataway:IEEE Press , 2006 : 1 - 6 .

OSTLIN E , ZEPERNICK H J , SUZUKI H . Macrocell path-loss prediction using artificial neural networks [J ] . IEEE Transactions on Vehicular Technology , 2010 , 59 ( 6 ): 2735 - 2747 .

POPESCU I , NIKITOPOULOS D , CONSTANTINOU P , et al . ANN prediction models for outdoor environment [C ] // 2006 IEEE 17th International Symposium on Personal,Indoor and Mobile Radio Communications . Piscataway:IEEE Press , 2006 : 1 - 5 .

STANKOVIC Z , MILOVANOVIC B , VELJKOVIC M , et al . The hybrid-neural empirical model for the electromagnetic field level prediction in urban environments [C ] // 7th Seminar on Neural Network Applications in Electrical Engineering . Piscataway:IEEE Press , 2004 : 189 - 192 .

CERRI G , CINALLI M , MICHETTI F , et al . Feed forward neural networks for path loss prediction in urban environment [J ] . IEEE Transactions on Antennas and Propagation , 2004 , 52 ( 11 ): 3137 - 3139 .

SOTIROUDIS S P , SIAKAVARA K . Mobile radio propagation path loss prediction using artificial neural networks with optimal input information for urban environments [J ] . AEU-International Journal of Electronics and Communications , 2015 , 69 ( 10 ): 1453 - 1463 .

EICHIE J O , OYEDUM O D , AJEWOLE M O , et al . Comparative analysis of basic models and artificial neural network based model for path loss prediction [J ] . Progress In Electromagnetics Research M , 2017 , 61 : 133 - 146 .

POPESCU I , NAFORNITA I , CONSTANTINOU P . Comparison of neural network models for path loss prediction [C ] // IEEE International Conference on Wireless And Mobile Computing,Networking And Communications . Piscataway:IEEE Press , 2005 : 44 - 49 .

KALAKH M , KANDIL N , HAKEM N . Neural networks model of an UWB channel path loss in a mine environment [C ] // 2012 IEEE 75th Vehicular Technology Conference . Piscataway:IEEE Press , 2012 : 1 - 5 .

ZAAROUR N , KANDIL N , HAKEM N . An accurate neural network approach in modeling an UWB channel in an underground mine [C ] // 2013 IEEE Antennas and Propagation Society International Symposium . Piscataway:IEEE Press , 2013 : 1608 - 1609 .

FERREIRA G P , MATOS L J , SILVA J M M . Improvement of outdoor signal strength prediction in UHF band by artificial neural network [J ] . IEEE Transactions on Antennas and Propagation , 2016 , 64 ( 12 ): 5404 - 5410 .

AYADI M , ZINEB A B , TABBANE S . A UHF path loss model using learning machine for heterogeneous networks [J ] . IEEE Transactions on Antennas and Propagation , 2017 , 65 ( 7 ): 3675 - 3683 .

SALMAN M A , POPOOLA S I , FARUK N , et al . Adaptive neuro-fuzzy model for path loss prediction in the VHF band [C ] // 2017 International Conference on Computing Networking and Informatics . Piscataway:IEEE Press , 2017 : 1 - 6 .

LEE J G Y , KANG M Y , KIM S C . Path loss exponent prediction for outdoor millimeter wave channels through deep learning [C ] // 2019 IEEE Wireless Communications and Networking Conference . Piscataway:IEEE Press , 2019 : 1 - 5 .

POPESCU I , NAFOMITA I , CONSTANTINOU P , et al . Neural networks applications for the prediction of propagation path loss in urban environments [C ] // Vehicular Technology Conference . Piscataway:IEEE Press , 2001 : 387 - 391 .

IGNACIO F A , ANTONIO R A J , PEREZ F F . Influence of training set selection in artificial neural network-based propagation path loss predictions [J ] . International Journal of Antennas and Propagation , 2012 , 2012 : 178 - 193 .

POPOOLA S I , JEFIA A , ATAYERO A A , et al . Determination of neural network parameters for path loss prediction in very high frequency wireless channel [J ] . IEEE Access , 2019 , 7 : 150462 - 150483 .

SOTIROUDIS S P , GOUDOS S K , GOTSIS K A , et al . Application of a composite differential evolution algorithm in optimal neural network design for propagation path-loss prediction in mobile communication systems [J ] . IEEE Antennas and Wireless Propagation Letters , 2013 , 12 : 364 - 367 .

ZINEB A B , AYADI M . A multi-wall and multi-frequency indoor path loss prediction model using artificial neural networks [J ] . Arabian Journal for Science and Engineering , 2016 , 41 ( 3 ): 987 - 996 .

BHUVANESHWARI A , HEMALATHA R , SATYASAVITHRI T . Performance evaluation of dynamic neural networks for mobile radio path loss prediction [C ] // IEEE Uttar Pradesh Section International Conference on Electrical,Computer and Electronics Engineering . Piscataway:IEEE Press , 2016 : 461 - 466 .

SUN N , GENG S , LI S , et al . Channel modeling by RBF neural networks for 5G mm-wave communication [C ] // 2018 IEEE/CIC International Conference on Communications in China . Piscataway:IEEE Press , 2018 : 768 - 772 .

MA X , ZHANG J , ZHANG Y , et al . Data scheme-based wireless channel modeling method:motivation,principle and performance [J ] . Journal of Communications and Information Networks , 2017 , 2 ( 3 ): 41 - 51 .

AZPILICUETA L , RAWAT M , RAWAT K , et al . A ray launching-neural network approach for radio wave propagation analysis in complex indoor environments [J ] . IEEE Transactions on Antennas and Propagation , 2014 , 62 ( 5 ): 2777 - 2786 .

ALDOSSARI S M , CHEN K C . Machine learning for wireless communication channel modeling:an overview [J ] . Wireless Personal Communications , 2019 , 106 ( 1 ): 41 - 70 .

CHEN X , CHENG J , ZHANG Z , et al . Data-rate driven transmission strategy for deep learning based communication systems [J ] . arXiv Preprint,arXiv:1812.08869 , 2018 .

O'SHEA T J , ERPEK T , CLANCY T C . Deep learning based MIMO communications [J ] . arXiv Preprint,arXiv:1707.07980 , 2017 .

ZHAO X , HOU C , WANG Q . A new SVM-based modeling method of cabin path loss prediction [J ] . International Journal of Antennas and Propagation , 2013 , 2013 : 718 - 710 .

相征 , 张太镒 , 孙建成 . 基于最小二乘支持向量机的非线性系统建模 [J ] . 系统仿真学报 , 2006 , 18 ( 9 ): 2684 - 2687 .

XIANG Z , ZHANG T Y , SUN J C . Modeling of nonlinear system based on least squares support vector machines [J ] . Journal of System Simulation , 2006 , 18 ( 9 ): 2684 - 2687 .

郭世坤 . 基于支持向量机的矿井无线信道建模与精确预测 [D ] . 西安:西安科技大学 , 2015 .

GUO S K . Modeling and accurate prediction of mine wireless channel based on support vector machine [D ] . Xi’an:Xi’an University of Science and Technology , 2015 .

丁世飞 , 齐丙娟 , 谭红艳 . 支持向量机理论与算法研究综述 [J ] . 电子科技大学学报 , 2011 , 40 ( 1 ): 1 - 10 .

DING S F , QI B J , TAN H Y . A review of support vector machine theory and algorithm research [J ] . Journal of University of Electronic Science and Technology of China , 2011 , 40 ( 1 ): 1 - 10 .

CHEN L C , PAPANDREOU G , KOKKINOS I , et al . Semantic image segmentation with deep convolutional nets and fully connected crfs [J ] . arXiv Preprint,arXiv:1412.7062 , 2014 .

PAPANDREOU G , CHEN L C , MURPHY K , et al . Weakly-and semi-supervised learning of a DCNN for semantic image segmentation [J ] . arXiv Preprint,arXiv:1502.02734 , 2015 .

谢晓娇 . 超宽带通信系统室内密集多径信道模型识别与参数估计 [D ] . 哈尔滨:哈尔滨工业大学 , 2018 .

XIE X J . Indoor dense multipath channel model identification and pa-rameter estimation of UWB communication system [D ] . Harbin:Har-bin Institute of Technology , 2018 .

侯晓赟 , 吴欢桐 , 赵水静 , 等 . 一种基于特征矩阵的无线信道场景分类方法:CN201810257114.2 [P ] .(2018-09-04)[2020-06-01 ] .

HOU X Y , WU H T , ZHAO S J , et al . A wireless channel scene classi-fication method based on feature matrix:CN201810257114.2 [P ] .(2018-09-04)[2020-06-01 ] .

周涛 , 陶成 , 刘留 , 等 . 一种高速铁路无线信道场景的分类方法及识别方法:CN201811452875.X [P ] .(2019-03-26)[2020-06-01 ] .

ZHOU T , TAO C , LIU L , et al . A classification method and identifica-tion method of high-speed railway wireless channel scene:CN201811452875.X [P ] .(2019-03-26)[2020-06-01 ] .

黄晨 , 何睿斯 , 郑青碧 , 等 . 一种基于支持向量机的无线信道场景识别方法:CN201811155551.X [P ] .(2019-02-01)[2020-06-01 ] .

HUANG C , HE R S , ZHENG Q B , et al . A wireless channel scene recognition method based on support vector machines:CN201811155551.X [P ] .(2019-02-01)[2020-06-01 ] .

HUANG A , TIAN L , JIANG T , et al . NLOS Identification for wideband mmWave system at 28 GHz [C ] // 2019 IEEE 89th Vehicular Technology Conference . Piscataway:IEEE Press , 2019 : 1 - 6 .

李太福 , 姚立忠 , 黄迪 , 等 . 基于 UKFNN 的无线信道场景识别方法:CN201610141612.1 [J ] .(2016-06-29)[2020-06-01 ] .

LI T F , YAO L Z , HUANG D , et al . Wireless channel scene recogni-tion method based on UKFNN:CN201610141612.1 [J ] .(2016-06-29)[2020-06-01 ] .

LI H , LI Y , ZHOU S , et al . Wireless channel feature extraction via GMM and CNN in the tomographic channel model [J ] . Journal of Communications and Information Networks , 2017 , 2 ( 1 ): 41 - 51 .

刘祥 . 基于深度学习的无线通信场景识别研究 [D ] . 西安:西安电子科技大学 , 2018 .

LIU X . Research on wireless communication scene recognition based on deep learning [D ] . Xi’an:Xidian University , 2018 .

ZHANG J , LIU L , FAN Y , et al . Wireless channel propagation scenarios identification:a perspective of machine learning [J ] . IEEE Access , 2020 , 8 : 47797 - 47806 .

姚碧圆 . 基于图论算法的无线信道特征提取与场景识别研究 [D ] . 海口:海南大学 , 2017 .

YAO B Y . Research on wireless channel feature extraction and scene recognition based on graph theory algorithm [D ] . Haikou:Hainan University , 2017 .

陈能美 , 刘晓静 . 基于随机森林算法的信道场景分类模型研究 [J ] . 重庆理工大学学报:自然科学版 , 2017 , 31 ( 4 ): 134 - 140 .

CHEN N M , LIU X J . Research on channel scene classification model based on random forest algorithm [J ] . Journal of Chongqing University of Technology:Natural Science Edition , 2017 , 31 ( 4 ): 134 - 140 .

WU R K . Identification and region division of wireless channel characteristics based on machine learning method [J ] . Journal of Antennas , 2016 , 5 ( 1 ): 1 - 7 .

浏览量

4957

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

对流层-电离层跨介质天波超视距传播统一信道建模方法

基于数据特征的成员推理增强攻击方法

基于mRMR-GA的太赫兹信道场景识别研究

IP地理定位技术综述：理论、方法与应用创新

基于阵列的神经网络水声通信信号多参数联合估计算法