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Wireless Personal Communications

, Volume 106, Issue 1, pp 41–70 | Cite as

Machine Learning for Wireless Communication Channel Modeling: An Overview

  • Saud Mobark AldossariEmail author
  • Kwang-Cheng Chen
Article

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.

Keywords

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

Notes

Acknowledgements

Saud Aldossari expresses a great appreciation to Prince Sattam bin Abdulaziz University for their support of providing scholarship. Kwang-Cheng Chen appreciates FC2 Collaborative Seed Grant for the support of research.

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical EngineeringUniversity of South FloridaTampaUSA

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