Indoor Mobility Channel Measurement for Massive MIMO

We used a 64-antenna RENEW software-defined massive MIMO base station and 7 software-defined clients in a large open area inside a building hall. We fixed six of the clients in a circle, 15m away from the base station. The seventh node was placed on a robot where we moved the robot across the hall starting from the location of the first client to the last. We moved the robot along the path with different speeds, i.e. with 0.5m/s, 1 m/s, and 2 m/s. The mobile node's antenna was facing the base station in all the experiments (LoS channel). We repeated the experiments to measure both LoS and NLoS channels for the fixed clients.

In each measurement, we transmitted time-orthogonal uplink pilots from all clients to the BS. The uplink pilots were based on the 802.11 LTS OFDM signal, which contains 52 non-zero subcarriers. The measurements were conducted on Nov 20, 2022. In total, 6 datasets were collected as listed below this page.

Channel measurement and dataset analysis software in included in the RENEWLab repository. In particular, the plot_hdf5 tool in RENEWLab can be used to visualize and post-process each HDF5 dataset. For more details on how to use this tool, please refer to the RENEW wiki page.

Rice University hereby grants you a non-exclusive, non-transferable license to use the data for commercial, educational, and/or research purposes only. You agree to not redistribute the data without written permission from Rice University.

You agree to acknowledge the source of the data in any publication or product reporting on your use of it.

We provide no warranty whatsoever on any aspect of the data, including but not limited to its correctness, completeness, and fitness. Use at your own risk.

You agree to acknowledge the following publications:

• Qing An, Santiago Segarra, Chris Dick, Ashutosh Sabharwal, Rahman Doost-Mohammady, "A Deep Reinforcement Learning-Based Resource Scheduler for Massive MIMO Networks," in IEEE Transactions on Machine Learning in Communications and Networking, 2023, doi: 10.1109/TMLCN.2023.3313988
• Rahman Doost-Mohammady, Oscar Bejarano, Lin Zhong, Joseph Cavallaro, Edward Knightly, Z. Morley Mao, Wei Wayne Li, Xuemin Chen, Ashutosh Sabharwal, "RENEW: Programmable and Observable Massive MIMO Networks," 2018 52nd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 2018, pp. 1654-1658, doi: 10.1109/ACSSC.2018.8645391.

in any publication or product reporting on your use of the data. If the data is not part of the IEEE Transactions on Machine Learning in Communications and Networking reference data, you also agree to acknowledge the additional source of the data, if applicable.

NOTE: Downloading, obtaining, and/or using the data in any means constitutes your agreement with these terms.

BibTeX entries:

@ARTICLE{10247079,
author={An, Qing and Segarra, Santiago and Dick, Chris and Sabharwal, Ashutosh and Doost-Mohammady, Rahman},
journal={IEEE Transactions on Machine Learning in Communications and Networking},
title={A Deep Reinforcement Learning-Based Resource Scheduler for Massive MIMO Networks},
year={2023},
volume={1},
number={},
pages={242-257},
doi={10.1109/TMLCN.2023.3313988}}

@INPROCEEDINGS{8645391,
author={Doost-Mohammady, Rahman and Bejarano, Oscar and Zhong, Lin and Cavallaro, Joseph R. and Knightly, Edward and Mao, Z. Morley and Li, Wei Wayne and Chen, Xuemin and Sabharwal, Ashutosh},
booktitle={2018 52nd Asilomar Conference on Signals, Systems, and Computers},
title={RENEW: Programmable and Observable Massive MIMO Networks},
year={2018},
pages={1654-1658},
doi={10.1109/ACSSC.2018.8645391}}