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opencc-by-4.0Mar 2024View details →

DEDICAT6G-5G testbed measurements-CGB

- MEASUREMENTS: Downlink Throughput (+ packet loss), RTT, Jitter, Latitude and Longitude - NR5G_SA: RSRP, RSRQ, RSSI, SINR, Band, Bandwidth

opencc-by-4.0Jul 2023View details →

Outdoor NB-IoT and 5G coverage and channel information data in urban environments

This dataset includes data for NB-IoT and 5G networks as collected in two cities: Oslo, Norway (NB-IoT only) and Rome, Italy (both NB-IoT and 5G). Data were collected using the Rohde & Schwarz TSMA6 mobile network scanner. 7 measurement campaigns are provided for Oslo, and 6 for Rome. Additional data collected in Rome are provided in the following large-scale dataset, focusing on the two major mobile network operators: <a href="https://ieee-dataport.org/documents/large-scale-dataset-4g-nb-iot-and-5g-non-standalone-network-measurements">https://ieee-dataport.org/documents/large-scale-dataset-4g-nb-iot-and-5g-non-standalone-network-measurements</a>  The dataset includes a metadata file providing the following information for each campaign:  <ul> <li>date of collection;</li> <li>start time and end time of collection;</li> <li>length;</li> <li>type (walking/driving).</li> </ul> Two additional metadata files are provided: two .kml files, one for each city, allowing the import of coordinates of data points organized by campaign in a GIS engine, such as Google Earth, for interactive visualization. The dataset contains the following data for NB-IoT: <ul> <li>Raw data for each campaign, stored in two .csv files. For a generic campaign <X>, the files are: <ul> <li>NB-IoT_coverage_C<X>.csv including a geo-tagged data entry in each row. Each entry provides information on a Narrowband Physical Cell Identifier (NPCI), with data related to the time stamp the NPCI was detected, GPS information, network (NPCI, Operator, Country Code, eNodeB-ID) and RF signal (RSSI, SINR, RSRP and RSRQ values);</li> <li> NB-IoT_RefSig_cir_C<X>.csv, also including a geo-tagged data entry in each row. Each entry provides information on a NPCI, with data related to the time stamp the NPCI was detected, GPS information, network (NPCI, Operator ID, Country Code, eNodeB-ID) and Channel Impulse Response (CIR) statistics, including the maximum delay.</li> </ul> </li> <li>Processed data, stored in a Matlab workspace (.mat) file for each city:  data are grouped in data points, identified by <Latitude, longitude> pairs. Each data point provides RF and CIR maximum delay measurements for each <NPCI, Operator ID, eNodeB-ID> unique combination detected at the coordinates of the data point.</li> <li>Estimated positions of eNodeBs, stored in a csv file for each city;</li> <li>A matlab script and a function to extract and generate processed data from the raw data for each city.</li> </ul> The dataset contains the following data for 5G: <ul> <li>Raw data for each campaign, stored in two .xslx files. For a generic campaign <X>, the files are: <ul> <li>5G_coverage_C<X>.xslx including a geo-tagged data entry in each row. Each entry provides information on a Physical Cell Identifier (PCI), with data related to the time stamp the PCI was detected, GPS information, network (PCI, Beamforming Index, Operator, Country Code) and RF data (SSB-RSSI, SSS-SINR, SSS-RSRP and SSS-RSRQ values, and similar information for the PBCH signal);</li> <li> 5G_RefSig_cir_C<X>.csv, also including a geo-tagged data entry in each row. Each entry provides information on a PCI, with data related to the time stamp the PCI was detected, GPS information, network (PCI, Beamforming Index, Operator ID, Country Code) and Channel Impulse Response (CIR) statistics, including the maximum delay.</li> </ul> </li> <li>Processed data, stored in a Matlab workspace (.mat) file:  data are grouped in data points, identified by <Latitude, longitude> pairs. Each data point provides RF and CIR maximum delay measurements for each <PCI, Beamforming Index, Operator ID> unique combination detected at the coordinates of the data point.</li> <li>A matlab script and a supporting function to extract and generate processed data from the raw data.</li> </ul> In addition, in the case of the Rome data additional matlab workspaces are provided, containing interpolated data in the feature dimensions according to two different approaches: <ul> <li>A campaign-by-campaign linear interpolation (both NB-IoT and 5G);</li> <li>A bidimensional interpolation on all campaigns combined (NB-IoT only).</li> </ul> A function to interpolate missing data in the original data according to the first approach is also provided for each technology. The interpolation rationale and procedure for the first approach is detailed in: L. De Nardis, G. Caso, Ö. Alay, U. Ali, M. Neri, A. Brunstrom and M.-G. Di Benedetto, "Positioning by Multicell Fingerprinting in Urban NB-IoT networks," Sensors, Volume 23, Issue 9, Article ID 4266, April 2023. DOI: <a href="https://doi.org/10.3390/s23094266" target="_blank" rel="noopener">10.3390/s23094266</a>. The second interpolation approach is instead introduced and described in: L. De Nardis, M. Savelli, G. Caso, F. Ferretti, L. Tonelli, N. Bouzar, A. Brunstrom, O. Alay, M. Neri, F. Elbahhar and M.-G. Di Benedetto, " Range-free Positioning in NB-IoT Networks by Machine Learning: beyond WkNN", under major revision in IEEE Journal of Indoor and Seamless Positioning and Navigation. Positioning using the 5G data was furthermore in investigated in:  K. Kousias, M. Rajiullah, G. Caso, U. Ali, Ö. Alay, A. Brunstrom, L. De Nardis, M. Neri, and M.-G. Di Benedetto, "A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements," IEEE Communications Magazine, Volume 62, Issue 5, pp. 44-49, May 2024. DOI:  <a href="https://doi.org/10.1109/MCOM.011.2200707" target="_blank" rel="noopener">10.1109/MCOM.011.2200707</a>. G. Caso, M. Rajiullah, K. Kousias, U. Ali, N. Bouzar, L. De Nardis, A. Brunstrom, Ö. Alay, M. Neri and M.-G. Di Benedetto,"The Chronicles of 5G Non-Standalone: An Empirical Analysis of Performance and Service Evolution", IEEE Open Journal of the Communications Society, Volume 5, pp. 7380 - 7399, 2024. DOI: <a href="https://doi.org/10.1109/OJCOMS.2024.3499370" target="_blank" rel="noopener">10.1109/OJCOMS.2024.3499370</a>. Please refer to the above publications when using and citing the dataset.

opencc-by-4.0Oct 2018View details →

5G-VINNI Webinar for ICT-19 Proposers

The 5G-VINNI project hold a webinar to explain its end-to-end facility to ICT-19 proposers and explain the process for obtaining consent for use of the 5G-VINNI facility. For ICT-19 proposers it is important to correctly understand how they can make use of the 5G-VINNI end-to-end facility. The webinar gave an overview on the facility and answered the questions that proposers had. In our webinar, we informed ICT-19 proposers and other interested participants about the 5G-VINNI facility and how to access and use it for the trials and validations of ICT-19 projects. The target audience for this webinar were proposers for ICT-19 projects in the context of the 5G PPP initiative of the H2020 programme of the European Commission.

opencc-by-4.0Feb 2022View details →

Large-scale dataset for the analysis of outdoor-to-indoor propagation for 5G mid-band operational networks

We present a comprehensive dataset of channel measurements, performed to analyze outdoor-to-indoor propagation characteristics in the mid-band spectrum identified for the operation of 5th Generation (5G) cellular systems. The dataset includes measurements of channel power delay profiles from two 5G networks operating in Band n78, i.e., 3.3--3.8 GHz. Such measurements were collected at multiple locations in a large office building in the city of Rome, Italy, by using the Rohde & Schwarz (R&S) network scanner TSMA6 for several weeks in 2020 and 2021. A primary goal of the dataset is to provide an opportunity for researchers to investigate a large set of 5G channel measurements, aiming at analyzing the corresponding propagation characteristics towards the definition and refinement of empirical channel propagation models.

opencc-by-4.0Jun 2022View details →

Modelos de regresión y su desviación estándar, ajustados para la pérdida de peso en función del tiempo para la hojarasca evaluada en el delta del Rio Ranchería - brazo Riíto. K: tasa de descomposición (g/día). R2: coeficiente de determinación, 5g (días): días que se necesitan para descomponer 5g.

Publicado en: Hernández Escobar, L., Granados-Martínez, C. & Fuentes-Reinés, J. (2022). Descomposición acuática de la hojarasca foliar en tres especies de mangle en la desembocura del río Ranchería (Brazo Riíto) y su relación con los macroinvertebrados en el municipio de Riohacha, departamento de La Guajira. Ciencia e Ingeniería, 9 (1):e6709388. https://www.doi.org/10.5281/zenodo.6709388

opencc-by-4.0Oct 2022View details →

I/Q measurements with 5G SRS signals and receiver 4-port 3D Vector Antenna for positioning studies

This dataset contains the I\Q data of four received signals from a 4-port 3D Vector Antenna (3D VA) as well as *fig and *png examples of the angle of arrival (AoA)/azimuth angle estimation using the MUSIC algorithm based on the raw data. The data was collected from four ports (p5, p6, p7, p8) of a 3D VA provided by ENAC. A single Yagi antenna has been used as a transmitter at 2.1GHz carrier frequency and horizontal polarization.

opencc-by-4.0Jul 2024View details →

5G NR Full Grid Over-the-Air Measurements

This data set is collected for the full resource grid over-the-air measurements of 5G NR downlink tranmission. Some different scenarios including different numerologies, center frequencies, bandwidths and fading are included. Please refer to the PDF file in the folder for the detailed description of the scenarios

5G-Xcast Open Spectrum data 5/6

This dataset is part 5/6 of the open spectrum measurement data from Horizon 2020 -funded 5G-PPP project 5G-Xcast. The whole dataset includes a continuous 8-day period of spectrum data from January 20th to January 27th from four years, 2015 to 2018. The data and the measurement system is described in the Open Spectrum data.pdf file.

opencc-by-sa-4.0Jul 2018View details →

5G-Xcast Open Spectrum data 6/6

This dataset is part 6/6 of the open spectrum measurement data from Horizon 2020 -funded 5G-PPP project 5G-Xcast. The whole dataset includes a continuous 8-day period of spectrum data from January 20th to January 27th from four years, 2015 to 2018. The data and the measurement system is described in the Open Spectrum data.pdf file.

5G-Xcast Open Spectrum data 4/6

This dataset is part 4/6 of the open spectrum measurement data from Horizon 2020 -funded 5G-PPP project 5G-Xcast. The whole dataset includes a continuous 8-day period of spectrum data from January 20th to January 27th from four years, 2015 to 2018. The data and the measurement system is described in the Open Spectrum data.pdf file.

5G-Xcast Open Spectrum data 2/6

This dataset is part 2/6 of the open spectrum measurement data from Horizon 2020 -funded 5G-PPP project 5G-Xcast. The whole dataset includes a continuous 8-day period of spectrum data from January 20th to January 27th from four years, 2015 to 2018. The data and the measurement system is described in the Open Spectrum data.pdf file.

5G-Xcast Open Spectrum data 1/6

This dataset is part 1/6 of the open spectrum measurement data from Horizon 2020 -funded 5G-PPP project 5G-Xcast. The whole dataset includes a continuous 8-day period of spectrum data from January 20th to January 27th from four years, 2015 to 2018. The data and the measurement system is described in the Open Spectrum data.pdf file.

5G-Xcast Open Spectrum data 3/6

This dataset is part 3/6 of the open spectrum measurement data from Horizon 2020 -funded 5G-PPP project 5G-Xcast. The whole dataset includes a continuous 8-day period of spectrum data from January 20th to January 27th from four years, 2015 to 2018. The data and the measurement system is described in the Open Spectrum data.pdf file.

Support data for article "The concept of optimal planning of a linearly oriented segment of the 5G network"

Support data for article V. Kovtun, K. Grochla, E. Zaitseva, and V. Levashenko, “The concept of optimal planning of a linearly oriented segment of the 5G network,” PLOS ONE, vol. 19, no. 4. Public Library of Science (PLoS), p. e0299000, Apr. 17, 2024. doi: 10.1371/journal.pone.0299000. This research is part of the project No. 2022/45/P/ST7/03450 co-funded by the National Science Centre and the European Union Framework Programme for Research and Innovation Horizon 2020 under the Marie Skłodowska-Curie grant agreement No. 945339.

Support data for conference paper "Service-Oriented Model for Handling mMTC Subscribers' Traffic in a 5G Cluster"

Support data for conference paper V. Kovtun, and O. Kovtun, “Service-Oriented Model for Handling mMTC Subscribers’ Traffic in a 5G Cluster.” In Proc. 5th 5th International Workshop on Intelligent Information Technologies & Systems of Information Security, CEUR-WS, vol. 3675, 2024; pp. 236-246. This research is part of the project No. 2022/45/P/ST7/03450 co-funded by the National Science Centre and the European Union Framework Programme for Research and Innovation Horizon 2020 under the Marie Skłodowska-Curie grant agreement No. 945339.

Support data for article "The concept of network resource control of a 5G cluster focused on the smart city's critical infrastructure needs"

Support data for article: <div>V. Kovtun, K. Grochla, and K. Połys, “The concept of network resource control of a 5G cluster focused on the smart city’s critical infrastructure needs,” Alexandria Engineering Journal, vol. 94. Elsevier BV, pp. 248–256, May 2024. doi: 10.1016/j.aej.2024.03.038.</div> This research is part of the project No. 2022/45/P/ST7/03450 co-funded by the National Science Centre and the European Union Framework Programme for Research and Innovation Horizon 2020 under the Marie Skłodowska-Curie grant agreement No. 945339.

Support data for conference paper "The Concept of Efficient Utilization of the Uplink Frequency Resource of a Smart Factory 5G Cluster by IIoT Devices"

upport data for conference paper Kovtun, and O. Kovtun, “The Concept of Efficient Utilization of the Uplink Frequency Resource of a Smart Factory 5G Cluster by IIoT Devices.” In Proc. 8th International Conference on Computational Linguistics and Intelligent Systems. Volume I: Machine Learning Workshop, CEUR-WS, vol. 3664, 2024; pp. 273-283. This research is part of the project No. 2022/45/P/ST7/03450 co-funded by the National Science Centre and the European Union Framework Programme for Research and Innovation Horizon 2020 under the Marie Skłodowska-Curie grant agreement No. 945339.

opencc-by-4.0Jun 2024View details →

Supplementary Data and Software for "Robust NLoS Localization in 5G mmWave Networks: Data-based Methods and Performance"

The file includes supplementary data for "Robust NLoS Localization in 5G mmWave Networks: Data-based Methods and Performance". If you would like to re-use the software provided please cite the following two items. R. Klus, J. Talvitie, J. Equi, G. Fodor, J. Torsner, and M. Valkama, “Robust NLoS Localization in 5G mmWave Networks: Data-based Methods and Performance,” IEEE Transactions on Vehicular Technology, 2024. R Klus et al. (2024). Supplementary materials for “Robust NLoS Localization in 5G mmWave Networks: Data-based Methods and Performance”. version v1, 25.06.2024, [Online]. Available: https://doi.org/10.5281/zenodo.12204892 If you have any questions about this package, please do not hesitate to contact Roman Klus (roman.klus@tuni.fi).

opencc-by-4.0Sep 2024View details →

NANCY SNS JU Project - VR Video Streaming & iPerf3 on O-RAN 5G Testbed Dataset

This dataset was developed in the context of the NANCY project and it is the output of the experiments involving streaming a virtual reality (VR) video in a 5G coverage expansion scenario. Additionally, iPerf3 experiments in both TCP and UDP modes were carried out. The coverage expansion scenario involves a main operator and a micro-operator which extends the main operator’s coverage and can also provide additional services. The dataset includes network traffic, which was captured and stored in a .pcap files, as well as various performance metrics that were collected by an xApp running in the near-real-time Radio Access Network Intelligent Controller. The NANCY project has received funding from the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union's Horizon Europe research and innovation programme under Grant Agreement No 101096456.