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Machine Learning for Wireless Communication Channel Modeling: An Overview

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.

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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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Correspondence to Saud Mobark Aldossari.

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Aldossari, S.M., Chen, K. Machine Learning for Wireless Communication Channel Modeling: An Overview. Wireless Pers Commun 106, 41–70 (2019). https://doi.org/10.1007/s11277-019-06275-4

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Keywords

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