NB-IoT deep-indoor measurements in an underground tunnel system

Citation Author(s):
Krzysztof
Malarski
Technical University of Denmark
Jakob
Thrane
Technical University of Denmark
Henrik
Lehrmann Christiansen
Technical University of Denmark
Sarah
Ruepp
Technical University of Denmark
Submitted by:
Krzysztof Malarski
Last updated:
Tue, 05/17/2022 - 22:17
DOI:
10.21227/xvrx-6r04
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Abstract 

Underground UE statistics measurements captured on u-blox SARAN211 NB-IoT device, frequency band 20. Signal waveform captured by means of Rohde&Schwartz TSMW device. The samples were taken along ca. 1600m of level -2 underground tunnel system under Lyngby Campus of Technical University of Denmark.

Instructions: 

The dataset constist of 2 files:

 

tun_meas.zip - raw measurements grouped into 9 folders (measurement intervals - corridors). Each folder contains .mat files with UE statistics and the waveform captured at the given measurement position. 

nbiot_meas.csv - a dataset combining the measurements and the engineered parametres corresponding to the UE-eNB distance or tunnel properties.

 

Feature explanation:

 

d2d - 2D distance between the UE and the eNB

d3d - 3D distance between the UE and the eNB

nc  - number of close corridors (corridor crossings within the threshold distance of 20 metres)

pd_2d - 2D penetration distance

pd_3d - 3D penetration distance

x_ind, y_ind, z_ind - indoor coordinates, the edgepoint of the tunnel towards the eNB

x_out, y_out, z_out - outdoor coordinates the edgepoint of the ground surface along the straight line between the UE and the eNB

d2d_wall - 2D indoor distance

d3d_wall - 3D indoor distance

phi - elevation angle

theta - bearing angle

dcorner - distance to the farthest tunnel corner

acorner - angle to the farthest tunnel corner

dwallx - distance to the farther tunnel wall in x-axis

dwally - distance to the farther tunnel wall in y-axis

dwallz - distance to the tunnel ceiling