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Millimeter Wave and Massive MIMO Systems
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5G NR Channel Models
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- A Framework for Generating Large-Scale MIMO Datasets based on Accurate Remcom 3D Ray-tracing
- A DeepMIMO Dataset is Completely Defined by (i) the Ray-tracing Scenario and (ii) the Set of Parameters
- DeepMIMO Enables a Wide Range of gate io app
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Scenario 'R' and the set of Parameters S
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- Enabling the Development and Evaluation of Various Wireless Machine/Deep Learning Applications
- Allowing Dataset Reproducibility for Benchmarking and Comparisons
- Generating Datasets Compatible with 5G NR Channel Models and Numerologies (in DeepMIMO 5G NR)
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Step 3
- Unzip the scenario folder
- Add the unzipped scenario to the DeepMIMO/ray_tracing path
- Choose the DeepMIMO parameters in DeepMIMO_params.m
- Run the DeepMIMO generator script following the example
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In order to use the DeepMIMO dataset/scripts or any (modified) part of them, please cite:
1. The DeepMIMO paper: A. Alkhateeb, “DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications,” in Proc. of The Information Theory and Applications Workshop (ITA), San Diego, CA, Feb. 2019.
@InProceedings{Alkhateeb2019,
author = {Alkhateeb, A.},
title = {{DeepMIMO}: A Generic Deep Learning Dataset for Millimeter Wave and Massive {MIMO} Applications},
booktitle = {Proc. of Information Theory and Applications Workshop (ITA)},
year = {2019},
pages = {1-8},
month = {Feb},
Address = {San Diego, CA}, }
2. The Remcom Wireless InSite website: RemCom, Wireless InSite, “https://www.remcom.com/wireless-insite”.
@Article{Remcom,
author = {Remcom},
title = {{Wireless InSite}},
note = {\url{http://www.remcom.com/wireless-insite}.},}
