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 language | English |
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Title of host publication | 2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings |
Publisher | IEEE Institute of Electrical and Electronic Engineers |
ISBN (Electronic) | 978-1-7281-7307-8 |
ISBN (Print) | 978-1-7281-7308-5 |
DOIs | |
Publication status | Published - 5 Mar 2021 |
MoE publication type | A4 Article in a conference publication |
Event | IEEE Globecom Workshops, GC Wkshps 2020: Online - Virtual, Taipei, Taiwan, Province of China Duration: 7 Dec 2020 → 11 Dec 2020 |
Workshop
Workshop | IEEE Globecom Workshops, GC Wkshps 2020 |
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Country/Territory | Taiwan, Province of China |
City | Taipei |
Period | 7/12/20 → 11/12/20 |
Keywords
- Training
- Atmospheric modeling
- Urban areas
- Millimeter wave tchnology
- Predictive models
- Millimeter wave communication