The Role of Deep Learning in Communication Systems




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Emil Björnson explains the basics of supervised deep learning and two useful applications of it in the physical layer of communication systems.

If you want to learn more, you can read “Two Applications of Deep Learning in the Physical Layer of Communication Systems” by Emil Björnson and Pontus Giselsson (https://arxiv.org/pdf/2001.03350)

In Application 1, the following paper is used as an example:

Trinh Van Chien, Emil Björnson, Erik G. Larsson, “Sum Spectral Efficiency Maximization in Massive MIMO Systems: Benefits from Deep Learning,” IEEE International Conference on Communications (ICC), 2019. https://arxiv.org/pdf/1903.08163.pdf

In Application 2, the following paper is used as an example:

Özlem Tugfe Demir, Emil Björnson, “Channel Estimation in Massive MIMO under Hardware Non-Linearities: Bayesian Methods versus Deep Learning,” IEEE Open Journal of the Communications Society, 2020. https://arxiv.org/pdf/1911.07316.pdf

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Comment List

  • Wireless Future / Communication Systems
    January 21, 2021

    Very good explanations. Thank you, Prof. Emil.

    Any chance of the code from "Spectral Efficiency Maximization in Massive MIMO Systems: Benefits from Deep Learning" being shared on GitHub?

  • Wireless Future / Communication Systems
    January 21, 2021

    In ahr mm wave algorithm, nn & dl are heavily used.

  • Wireless Future / Communication Systems
    January 21, 2021

    Could be useful for forward error correction and multipath compensation. Oh, you already kinda covered FEC for nonlineararity.

  • Wireless Future / Communication Systems
    January 21, 2021

    A very good tutorial about such an interesting topic….I am looking forward to see more videos from you Dr.Emil regarding communication systems hot topics and basic fundamentals as well.
    Overall, thank you so much Dr.Emil for your effort

  • Wireless Future / Communication Systems
    January 21, 2021

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