• Sundeep Rangan, William Xia, Marco Mezzavilla, Giuseppe Loianno (NYU)
• Giovanni Geraci, Angel Lozano (UPF, Barcelona)
• Vasilii Semkin (VTT, Finland)

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. Data-driven machine-learning methods provides an attractive methodology that entails minimal assumptions and can naturally capture intricate probabilistic relationships in complex environments. This repository is currently in progress and will eventually provide tools for:

• Parsing large quantities of ray tracing data for generating ML training and test datasets.
• Learning generative neural network for statistical models of the data.
• Access to pre-trained models including UAV ground-to-air channels at 28 GHz.
• Performing simple network simulation studies with these models.

The work is partly based on

• Xia, W., Rangan, S., Mezzavilla, M., Lozano, A., Geraci, G., Semkin, V., & Loianno, G. (2020). Millimeter Wave Channel Modeling via Generative Neural Networks. arXiv preprint arXiv:2008.11006.

W. Xia, M. Mezzavilla and S. Rangan were supportedby NSF grants 1302336, 1564142, 1547332, and 1824434,NIST, SRC, and the industrial affiliates of NYU WIRELESS.A. Lozano and G. Geraci were supported by the ERC grant694974, by MINECO’s Project RTI2018-101040, and by the Junior Leader Program from "la Caixa" Banking Foundation.

All authors are also grateful for the assistance from Remcom that provided the Wireless Insite tool to generate the data.