Index of /datasets/ashu/traces/LensFD

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LensFD dataset

Codebase for "LensFD: Using Lenses for Improved Sub-6 GHz Massive MIMO Full-Duplex ", which is published on IEEE Transactions on Vehicular Technology 2023.

This dataset was collected by Zhican (West) Chen from Rice University, and Clayton Shepard from Skylark Wireless using the Argos V3 platform from both outdoor and indoor environments at Rice Univerisity campus.

In this project, we applied 4 different lens configurations, i.e., no lens (baseline), small lens array, medium lens array, large lens array, at the massive MIMO base-stations to directly compare their performance. The massive MIMO base-station consisted of a rectangular Uniform Planar Array (UPA) with 80 3.5 GHz (CBRS band) patch antennas interfaced to 40 radios. Each radio was slant linearly polarized and thus provided two independent (±45 degree) antenna ports. For each data, we provide both the raw IQ samples and processed Channel State Information (CSI). Note that data from both functioning radios and radios experiencing hardware failure (see description in Figure 7 of this paper) are collected and included in this dataset. More details about the base-station and client radio setup can be found in Section IV-A in this paper and Section 4.1 in this paper.

For any questions about this dataset, please contact the paper authors (czc0129@gmail.com) or the RENEW team.

Data Structure

For each lens configuration, we measured the following channels:

1. self-interference channel from the base-station (both indoor and outdoor)

Naming convention: (lens)-(environment)-(radio chains).(hdf5/mat)

lens: this corresponds to the lens configuration applied at the base-station. More specifically, we use "lg", "md", "sm", "no" as abbreviation for "large lens array", "medium lens array", "small lens array", and "no lens", respectively.

environment: this corresponds to the transmission environment where the data is collect. More specifically, we use "stadium" and "indoor" to refer to the outdoor environment at Rice University foot stadium and the indoor environment inside Rice Univerisity Duncan Hall, respectively.

radio chains: this corresponds to the radio chains included in this data. Due to practical constraints in the backhaul throughput, it's impossible to collect and store the self-interfrence data from all 80 antennas at the base-station in one run. Alternatively, we divide the 40 base-station radios into 5 chains, and collect self-interference data from 2 chains at a time.

As shown in Figure 7 of this paper, each row of the base-station is considered a radio hub and is indexed from bottom to top. A detailed illustration can be found as the following:

hdf5/mat: raw data is in .hdf5 format, processed data is in .mat format.

For example, "lg-stadium-chain12.hdf5" is the raw self-interference data among 32 antennas from the radio chains 1 and 2, with large lens array applied at the base-station from the stadium.

2. static array-to-client (a2c) channel from 14 different user locations distributed in the scene (both indoor and outdoor)

Naming convention: (lens)-(environment)-(client location).(hdf5/mat)

lens: same as above.

environment: same as above.

client location: client location index 1-14.

hdf5/mat: same as above.

3. mobile array-to-client channel with client radio moving at human walking speed (outdoor only)

lens: same as above.

direction: this corresponds to the moving direction of the mobile node. As shown in Fig 4.2 of this paper, the mobile radio is carried by researcher and moved at walking speed. The direction "n2s", i.e., meaning "north to south" corresponds to the mobile radio moved from the left end of the red line in Fig 4.2(c) to the right end; and "s2n" corresponds to the opposite direction.

There are 5 antennas included in each data, antennas 1-2 corresponds to the dual-polarized antennas interfaced to the stationary radio; antennas 3-4 corresponds to those interfaced to the mobile client, and "antenna 5" is used for noise power measurement as no antenna is transmitting and the base-station is listenning to the noise signal.

Python API

 
  hdf5analysis.py
  1. process the raw IQ sample in hdf5 file and store it into mat file.
  2. plot self-coupling plot from measured self-interference channels (see example as Fig.8 from this paper).

Reference

If you use the code or dataset from the project, please cite both of the following paper:

  @ARTICLE{10029875,
  author={Chen, Zhican and Barati, C. Nicolas and Veihl, Jonand Shepard, Claytonand Sabharwal, Ashutosh},
  journal={IEEE Transactions on Vehicular Technology},
  title={LensFD: Using Lenses for Improved Sub-6 GHzand Massive MIMO Full-Duplex},
  year={2023},
  volume={},
  number={},
  pages={1-13},
  doi={10.1109/TVT.2023.3240558}}