机器学习在信道建模中的应用综述

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

通信学报 ›› 2021, Vol. 42 ›› Issue (2): 134-153.doi: 10.11959/j.issn.1000-436x.2021001

机器学习在信道建模中的应用综述

刘留¹, 张建华², 樊圆圆¹, 于力², 张嘉驰¹

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

修回日期:2020-09-20 出版日期:2021-02-25 发布日期:2021-02-01
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。
基金资助:
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)

Survey of application of machine learning in wireless channel modeling

Liu LIU¹, Jianhua ZHANG², Yuanyuan FAN¹, Li YU², Jiachi ZHANG¹

¹ School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
² State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China

Revised:2020-09-20 Online:2021-02-25 Published:2021-02-01
Supported by:
The National Key Research and Development Program of China(2018YFB1801101);The National Science Fund for Distinguished Young Scholars of China(61925102);The Beijing Natural Science Foundation-Haidian Original Innova-tion Joint Fund Project(L172030);Key Program of National Natural Science Foundation of China(61931001)

摘要/Abstract

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。

关键词: 信道建模, 机器学习, 神经网络, 聚类, 场景识别

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.

Key words: channel modeling, machine learning, neural network, clustering, channel classification

中图分类号:

TN929.5

刘留, 张建华, 樊圆圆, 于力, 张嘉驰. 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, Li YU, Jiachi ZHANG. Survey of application of machine learning in wireless channel modeling[J]. Journal on Communications, 2021, 42(2): 134-153.

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算法	应用范围		先验知识		支持动态MPC
算法	时延域	角度域	簇的数量	聚类中心	支持动态MPC
k-means/KPM	是	是	需要	需要	有可能
GMM	是	是	需要	不需要	有可能
DBSCAN	是	是	不需要	不需要	不支持
KPD	是	是	不需要	不需要	有可能
联合核密度	是	是	不需要	不需要	不支持
密度峰值	是	否	不需要	不需要	不支持
稀疏度	是	否	不需要	不需要	不支持
模糊C均值	是	是	需要	需要	有可能
KPM–卡尔曼滤波	是	是	需要	需要	支持
数据流	是	是	仅在初始化时需要	仅在初始化时需要	支持
最大转移概率	是	是	不需要	不需要	支持
功率谱轮廓识别	是	是	不需要	不需要	支持
谱聚类	是	是	需要	不需要	有可能

类型	特点	文献
不同类型的训练集	输入层的训练集为表征信道的特征参数	文献[17, 60-65, 67-68, 71-72]
	输入层的训练集为与环境有关的变量	文献[59, 68, 69-70, 73-75]
	适用于室内	文献[60-61]
不同建模环境	适用于农村	文献[62]
	适用于城市	文献[63-66, 74-75]
	适用于郊区	文献[67-68]
	适用于矿井环境中UWB信道	文献[69-70]
不同建模频段	适用于毫米波频段	文献[17]
	适用于UHF频段	文献[59, 71-72]
	适合于VHF频段	文献[59, 73]
最佳训练方案的测试	产生最佳性能的神经网络的训练集为纬度、经度、海拔和距离，包含9个隐含层	文献[76-77]

结合的优化算法	特点	文献
降维	降维提高了机器学习模型的预测精度，神经网络的结果优于支持向量机	文献[73]
复合差分进化算法	该算法高效，且易于实现和调整，其结果与射线跟踪模型相比准确性更高	文献[78]
数据挖掘技术	从多墙模型派生而来，用MLP框架以及BP算法训练，适用于包括UMTS、GSM和Wi-Fi在内的多个频段	文献[79]
动态神经网络	用FTDNN、DTDNN、LRNN来预测路径损耗，最大程度减少误差，使用LM和SCG训练算法进行训练	文献[80]

神经网络类型	模型输出的特性参数	特点	文献
FNN	CIR的幅值	对MIMO信道进行建模	文献[53]
	接收功率、均方根时延扩展、角度扩展	针对毫米波信道	文献[56]
CNN	接收功率、时延、到达角、离开角、接收端到达角、发射端离开角	针对毫米波频段的三维MIMO室内信道	文献[57]
BPNN	信道传递函数	适合高铁信道建模	文献[54]
	CIR的幅值和相位	利用BPNN去噪，利用PCA提取CIR在频域的隐藏特征	文献[6, 82]
	接收功率、到达角	对5G毫米波时变信道进行建模	文献[17,81]
RBFNN	多普勒频偏	用于逼近平坦衰落信道	文献[55]
	接收功率、均方根时延扩展、角度扩展	针对毫米波信道	文献[56]
	CIR的幅值	优化了传统的三维射线追踪法	文献[83]

文献	涉及的方法	特征参数	特点
文献[95]	k-means、SVM等	多种信道统计特性参数	先判断属于哪种散射，再判断属于哪类场景，降低了复杂度
文献[96]	SVM	角度、功率	可以解决监测模糊的问题，但无法准确判断传输环境
文献[97]	GBDT	功率、时延、峰度、偏度、上升时间	尤其适用于毫米波信道场景分类，准确率高、复杂度有限
文献[98]	UKFNN、AP	信道增益	先区分再识别，方法简单效率高，但各别场景准确率低
文献[99]	GMM、CNN	时延、功率幅值、多普勒频偏	可自动获取特征，并对场景进行分类
文献[100]	CNN、DBN	频率、CIR、时延	CNN和DBN分别应用到混合干扰场景识别与信道场景识别
文献[101]	k-means、k-NN、GMM、SVM	K因子、时延扩展、角度扩展、路径损耗	对仿真和实测的高铁信道数据进行降维和归一化等预处理，引入了混淆矩阵来评价性能
文献[102]	图论、PCA、RBFNN	功率、时延扩展、频率扩展	准确性较高，但无法集中存储大量数据且模型相对复杂
文献[103]	随机森林	瞬时功率幅值、频率、相位	可以统计、处理较大的数据量
文献[104]	决策树	信道系数	可以准确地识别到信道的特征

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

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