Predicting Wireless MmWave Massive MIMO Channel Characteristics Using Machine Learning Algorithms

Lu Bai, Cheng-Xiang Wang*, Jie Huang, Qian Xu, Yuqian Yang, George Goussetis, Jian Sun, Wensheng Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

49 Citations (Scopus)
157 Downloads (Pure)


This paper proposes a procedure of predicting channel characteristics based on a well-known machine learning (ML) algorithm and convolutional neural network (CNN), for three-dimensional (3D) millimetre wave (mmWave) massive multiple-input multiple-output (MIMO) indoor channels. The channel parameters, such as amplitude, delay, azimuth angle of departure (AAoD), elevation angle of departure (EAoD), azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA), are generated by a ray tracing software. After the data preprocessing, we can obtain the channel statistical characteristics (including expectations and spreads of the above-mentioned parameters) to train the CNN. The channel statistical characteristics of any subchannels in a specified indoor scenario can be predicted when the location information of the transmitter (Tx) antenna and receiver (Rx) antenna is input into the CNN trained by limited data. The predicted channel statistical characteristics can well fit the real channel statistical characteristics. The probability density functions (PDFs) of error square and root mean square errors (RMSEs) of channel statistical characteristics are also analyzed.

Original languageEnglish
Article number9783863
JournalWireless Communications and Mobile Computing
Publication statusPublished - 23 Aug 2018

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


Dive into the research topics of 'Predicting Wireless MmWave Massive MIMO Channel Characteristics Using Machine Learning Algorithms'. Together they form a unique fingerprint.

Cite this