3GPP 38.901 Indoor mmWave Channel Dataset in the Presence of Dynamic Blockers

Citation Author(s):
Andrea
Bonfante
Trinity College Dublin, CONNECT Centre
Lorenzo
Galati Giordano
Nokia Bell Labs Stuttgart, DE
Irene
Macaluso
Trinity College Dublin, CONNECT Centre
Nicola
Marchetti
Trinity College Dublin, CONNECT Centre
Submitted by:
Andrea Bonfante
Last updated:
Mon, 09/13/2021 - 05:50
DOI:
10.21227/14mf-5724
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Abstract 

This dataset includes the measured Downlink (DL) signal-to-noise ratios (SNRs) at the User Equipments (UEs), adopting one of the beams of the beamforming codebook employed at the Base Stations (BSs). First, we configured a system-level simulator that implements the most recent Third Generation Partnership Project (3GPP) 3D Indoor channel models and the geometric blockage Model-B to simulate an indoor network deployment of BSs and UEs adopting Uniform Planar Arrays (UPAs) and a codebook based transmission. Next, we configured the simulation scenario with one blocker moving on a straight path and intersecting the BS Transmitter (Tx) beams steered towards the UE locations. Finally, we collected SNR measurements over time for each Tx beam and BS with the blocker moving at two different speeds, which correspond to v=1 m/s and v=2 m/s, respectively, and we repeated the same movement pattern for 100 network drops.

Instructions: 
  • Download the current version of the dataset. The download comprises the 2.5 GB dataset compressed in zip format. The uncompressed size of the dataset is 23 GB.
  • Navigate to one of the speed's folders and access one of the network drop.
  • We saved all the SNR measurements for each beam and BS in a different comma-separated values (CSV) file.
  • Read a csv file taking into account that the SNR measurements are formatted in vectors, which have dimensions 200x275. The raws represent the simulation steps in time and the columns represent the resource blocks (RBs) of the system bandwidth.
  • For more detailed parameter settings, please refer to the documentation file and the paper "Performance of Predictive Indoor mmWave Networks with Dynamic Blockers." arXiv preprint arXiv:2104.04623 (2021).