To refer to or use any of our datasets or code, please cite one of our publications:
| Scenario | Aplication | Paper to cite | Link to paper |
|---|---|---|---|
| 1, 2, 3, 4, 5, 6, 7 and 8 | Beam Selection | 5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning | Information Theory and Applications Workshop (ITA), 2018. |
| 9 and 10 | Beam Selection | Generating MIMO Channels For 6G Virtual Worlds Using Ray-Tracing Simulations | IEEE Statistical Signal Processing Workshop (SSP), 2021. |
| 9 and 10 | Beam Selection |
Simulation of machine learning-based 6G systems in virtual worlds |
Arxiv, 2022. |
| 11, 12 and 13 | Multimodal ML Beam Selection | Multimodal Dataset for Machine Learning Applied to Telecommunications | Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT), 2020. |
| 14, 15, 16 and 17 | Beam tracking |
Machine Learning-Based mmWave MIMO Beam Tracking in V2I Scenarios: Algorithms and Datasets |
IEEE Latin-American Conference on Communications (LATINCOM), 2024. |
| 14 and 15 | Beam Tracking | Adaptive and Transition-Aware Beam Tracking for 6G mmWave Systems with Reduced Overhead | Physical Communcations, 2026. |
| 16 and 17 | Beam Tracking | DL-Based Beam Management for mmWave Vehicular Networks Exploring Temporal Correlation | Arxiv, 2025. |
| 20 and 21 | Beam Selection V2V | Ray-Tracing MIMO Channel Dataset for Machine Learning Applied to V2V Communication | IEEE Latin-American Conference on Communications (LATINCOM) , 2022. |
| other | other | 5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning | Information Theory and Applications Workshop (ITA), 2018 |