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lasse@ufpa.br

Raymobtime is a collection of ray-tracing datasets for wireless communications. It considers scenarios with mobility and time evolution, for consistency over time, frequency and space. We have used Remcom’s Wireless Insite for ray-tracing and the open source Simulator of Urban Mobility (SUMO) for mobility simulation (of vehicles, pedestrians, drones, etc). We also use Cadmapper and Open Street Map to simplify importing realistic outdoor scenarios. For more details, please check our publications.

ITU ARTIFICIAL INTELLIGENCE/MACHINE LEARNING IN 5G CHALLENGE

ITU invites you to participate in the ITU Artificial Intelligence/Machine Learning in 5G Challenge, a competition which is scheduled to run from now until the end of the year. Participation in the Challenge is free of charge and open to all interested parties from countries that are a member of ITU. If you are interested in one of the following topics below, please signal your interest by filling out the form on the website.
Detailed information about the Challenge can be found in the document ITU AI/ML 5G Challenge – Applying AI/ML
in 5G networks. A Primer , available on the Challenge website.

DATASETS DESCRIPTION

AVAILABLE RAY-TRACING ONLY DATASETS

Dataset name3D scenarioFrequencyNumber of receivers and typeSampling interval (time between scenes)Number of episodesNumber of scenes per episodeNumber of valid channels
s000Rosslyn60GHz10 Mobile100 ms1165041K
s001Rosslyn2.8; 5 GHz10 Fixed5 ms2001020K
s002Rosslyn2.8; 60 GHz 10 Fixed3 s1800118K
s003Rosslyn2.8; 5 GHz10 Fixed1 ms2001020K
s004Rosslyn 60 GHz10 Mobile1 sec5000135K
s005Rosslyn2.8; 5 GHz10 Fixed10 ms12580100K
s006Rosslyn28; 60 GHz 10 Fixed1 ms2001020K
s007Beijing2.8; 60 GHz 10 Mobile1 s504015K

AVAILABLE RAY-TRACING WITH LIDAR DATASETS

Dataset name3D scenarioFrequencyNumber of receivers and typeSampling interval (time between scenes)Number of episodesNumber of scenes per episodeNumber of valid channels
s008Rosslyn60GHz10 mobile0.1 s2086120K

Please, feel free to visit our GitHub. There we share some basic scripts to read the available Raymobtime dataset for MATLAB and Python users. Here you will find more information about the available dataset.

Example of ray-tracing simulation in a 3D scenario with the received powers of each ray indicated in colors.

CONTACT

raymobtime@gmail.com

LASSE/UFPA Team

WSIL/UT Austin Team

PUBLICATIONS

In order to use the Raymobtime datasets/codes or any (modified) part of them, please cite this paper:

A. Klautau, P. Batista, N. Gonzalez-Prelcic, Y. Wang and R. W. Heath, “5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning” in 2018 Information Theory and Applications Workshop (ITA).

A. Klautau, N. González-Prelcic and R. W. Heath, “LIDAR Data for Deep Learning-Based mmWave Beam-Selection” in IEEE Wireless Communications Letters.