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.
@article{arxiv.2008.11006,
title = {Millimeter Wave Channel Modeling via Generative Neural Networks},
author = {William Xia and Sundeep Rangan and Marco Mezzavilla and Angel Lozano and Giovanni Geraci and Vasilii Semkin and Giuseppe Loianno},
journal= {arXiv preprint arXiv:2008.11006},
year = {2020}
}
Comments
Submitted to IEEE GLOBECOM 2020 Workshop on Wireless Propagation Channels for 5G and B5G