DOI: 10.5281/zenodo.16891071
title: mmWave and midband 5G experimental data in an industrial setting

description: This dataset is part of the Industrial Network Repository (IN-Rep).

IN-Rep Metadata:




Ref.
Application Domain
Use case
Technology
Location
  Data


N. Bouzar, L. De Nardis, G. Caso, M. Neri, F. Elbahhar and M.-G. Di Benedetto, "Range-free positioning for Industrial Internet of Things in a mixed public-private midband and mmWave 5G deployment",  IEEE Conference on Standards for Communications and Networking, Workshop on Communication Networks for Next-Generation Industrial Internet of Things, September 15 - 17, 2025, Bologna, Italy. DOI: 10.1109/CSCN67557.2025.11230588
2,3,4 
AGV
5G,UWB*
BI-REX
5G: RF data, ToA;UWB*: Distance,RSSI, CIR




*The UWB data are under preparation for release in an updated version of the dataset.

The dataset includes 5G data collected in the industrial pilot line of the Big Data Innovation & Research Excellence (bi-rex) center, located in Bologna, Italy. The pilot line hosts production machines, robots and industrial demos in an area of approximately 300 m2, covered by a private 5G network operating at both 3.7 GHz and 26 GHz, as well as by public 5G networks at 3.7 GHz.Data were collected at 18 different locations in the area hosting the pilot line using the Rohde & Schwarz TSMA6 Mobile Network Scanner, part of the R&S Mobile network testing products, including a GPS receiver for data geo-mapping and accurate synchronization, and an up/down converter enabling the TSMA6 to collect simultaneously 4G data and 5G data both in in the 698-3800 MHz frequency range and at mmWave (26000 MHz), using two separate antennas. Collected data include RF parameters (RSSI, RSRP, SINR, RSRQ) and Time of Arrival information.Data are suitable for range-free positioning using machine learning algorithms such as the Weighted k-Nearest Neighbors algorithm.In the repository, you will find the 5G raw data, the script to process them, as well as the implementation of the WKNN positioning algorithm.

├── 5G_MNC_Processing.py├── 5G Raw Data     ├── l10_5G.xlsx    ├── l11_5G.xlsx    ├── l12_5G.xlsx    ├── l13_5G.xlsx    ├── l14_5G.xlsx    ├── l15_5G.xlsx    ├── l1_5G.xlsx    ├── l16_5G.xlsx    ├── l17_5G.xlsx    ├── l18_5G.xlsx    ├── l2_5G.xlsx    ├── l3_5G.xlsx    ├── l4_5G.xlsx    ├── l5_5G.xlsx    ├── l6_5G.xlsx    ├── l7_5G.xlsx    ├── l8_5G.xlsx    └── l9_5G.xlsx└── WKNN    ├── apply_commonality_weighting_loo.m    ├── Bi-Rex_Dataset.mat    ├── compute_multi_param_distances_loo.m    ├── compute_single_param_distances_loo.m    ├── create_fingerprint_matrices_loo.m    ├── create_global_unique_pcis.m    ├── display_loo_results.m    ├── main.m    ├── perform_wknn_positioning_loo.m    ├── plot_loo_results.m    └── wknn_leave_one_out_positioning.m

 

Please for more Information refer to the companion paper:N. Bouzar, L. De Nardis, G. Caso, M. Neri, F. Elbahhar and M.-G. Di Benedetto, "Range-free positioning for Industrial Internet of Things in a mixed public-private midband and mmWave 5G deployment",  accepted for the IEEE Conference on Standards for Communications and Networking, Workshop on Communication Networks for Next-Generation Industrial Internet of Things, September 15 - 17, 2025, Bologna, Italy.