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

Abstract

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
DOIs
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

Workshop

WorkshopIEEE Globecom Workshops, GC Wkshps 2020
Country/TerritoryTaiwan, Province of China
CityTaipei
Period7/12/2011/12/20

Keywords

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

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