Millimeter Wave Channel Modeling via Generative Neural Networks

William Xia, Sundeep Rangan, Marco Mezzavilla, Angel Lozano, Giovanni Geraci, Vasilii Semkin, Giuseppe Loianno

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    19 Citations (Scopus)


    Statistical channel models are instrumental to design and evaluate wireless communication systems. In the millimeter wave bands, such models become acutely challenging; they must capture the delay, directions, and path gains, for each link and with high resolution. This paper presents a general modeling methodology based on training generative neural networks from data. The proposed generative model consists of a two-stage structure that first predicts the state of each link (line-of-sight, non-line-of-sight, or outage), and subsequently feeds this state into a conditional variational autoencoder that generates the path losses, delays, and angles of arrival and departure for all its propagation paths. Importantly, minimal prior assumptions are made, enabling the model to capture complex relationships within the data. The methodology is demonstrated for 28GHz air-to-ground channels in an urban environment, with training datasets produced by means of ray tracing.

    Original languageEnglish
    Title of host publication2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings
    PublisherIEEE Institute of Electrical and Electronic Engineers
    ISBN (Electronic)978-1-7281-7307-8
    ISBN (Print)978-1-7281-7308-5
    Publication statusPublished - 5 Mar 2021
    MoE publication typeA4 Article in a conference publication
    EventIEEE Globecom Workshops, GC Wkshps 2020: Online - Virtual, Taipei, Taiwan, Province of China
    Duration: 7 Dec 202011 Dec 2020


    WorkshopIEEE Globecom Workshops, GC Wkshps 2020
    Country/TerritoryTaiwan, Province of China


    • Training
    • Atmospheric modeling
    • Urban areas
    • Millimeter wave tchnology
    • Predictive models
    • Millimeter wave communication


    Dive into the research topics of 'Millimeter Wave Channel Modeling via Generative Neural Networks'. Together they form a unique fingerprint.

    Cite this