Machine Learning for Wireless Communication Channel Modeling: An Overview

Saud Mobark Aldossari, Kwang Cheng Chen

Research output: Contribution to journalArticlepeer-review

100 Scopus citations

Abstract

Channel modeling is fundamental to design wireless communication systems. A common practice is to conduct tremendous amount of channel measurement data and then to derive appropriate channel models using statistical methods. For highly mobile communications, channel estimation on top of the channel modeling enables high bandwidth physical layer transmission in state-of-the-art mobile communications. For the coming 5G and diverse Internet of Things, many challenging application scenarios emerge and more efficient methodology for channel modeling and channel estimation is very much needed. In the mean time, machine learning has been successfully demonstrated efficient handling big data. In this paper, applying machine learning to assist channel modeling and channel estimation has been introduced with evidence of literature survey.

Original languageEnglish
Pages (from-to)41-70
Number of pages30
JournalWireless Personal Communications
Volume106
Issue number1 Special Issue
DOIs
StatePublished - May 2019
Externally publishedYes

Keywords

  • 5G
  • Channel modeling
  • Deep neural network
  • Indoor/outdoor communication systems
  • Machine learning
  • MmWave
  • Mobile communications
  • Regression

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