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111 results for “5g”
NL, Remote Driving, Remote driving using 5G positioning
<p>Use Case Category: <strong>Remote Driving</strong><br> User Story: <strong>Remote driving using 5G positioning</strong><br> Location: Dutch (NL) trial site</p> <p>According to 3GPP TS 22.186 R16, Remote Driving “enables a remote driver or a V2X application to operate a remote vehicle for those passengers who cannot drive themselves or a remote vehicle located in dangerous environments. For a case where variation is limited, and routes are predictable, such as public transportation, driving based on cloud computing can be used. In addition, access to cloud-based back-end service platform can be considered for this use case group”.</p> <p>User Story: <strong>Remote driving using 5G positioning</strong></p> <p>In situation where the AD vehicle is unable to automatically drive further (due to a failure or unexpected driving condition), a remote operator takes over control of the vehicle and drives it to a point where AD can be resumed. As an example, this can be in situations like border control, construction zones and inclement weather. To tele-operate a vehicle, the data from multiple sensors should stream their information (synchronised and with low latency) to the operator and at the same time have low latency in the control task of manoeuvring the vehicle in real time.</p> <p>One example for tele-operation is when an AD vehicle is automatically manoeuvred to a bay/slot assigned by border control remotely/via 5G and local edge computing for monitoring actions of the autonomous car by the border agents. For automated manoeuvring in a complex border-post environment, precise localization (of the car in surroundings by the car, plus potentially of the car by the infrastructure) is needed. In the NL trial, tele-operation will be tested by driving the vehicle from the emergency lane to an assigned slot on a rest area along the A270. However, testing of localization services will be carried out at the TU/e campus as it needs special infrastructure, e.g., fibre optical backbone and multiple 5G small cells, available at the TU/e campus for high accuracy localisation services and low latency. New 5G technology based on adaptation and integration of 5G beam steering and MIMO will be used for localization and positioning. The 5G (mm-wave) based location can serve as a redundant localization system and useful in scenarios such as border customs control with gates with a roof covering.</p>
Generalized LDPC codes for ultra reliable low latency communication in 5G and beyond
<p>Fifth-generation (5G) systems aim to increase the capacity of existing mobile networks by a factor of 1000, supporting an extremely high user density, as well as numerous device- to-device and machine communications. Ultra Reliable Low Latency Communication (URLLC) constitutes one of the critical operating regimes in 5G, since it will enable low-cost and power-efficient anywhere and anytime signalling services </p> <div> <div> <p>Generalized low-density parity-check (GLDPC) codes, where single parity-check constraints on the code bits are replaced with generalized constraints (an arbitrary linear code), are a promising class of codes for low-latency communication. We have constructed quasi-cyclic GLDPC codes, where the proportion of generalized constraints is determined by an asymptotic analysis. We have analyzed the complexity and performance of the message passing decoder with various update rules (including standard full-precision sum-product and min-sum algorithms) and quantization schemes for a GLDPC code over the additive white Gaussian noise (AWGN) channel and determined a constraint-to-variable update rule based on the specific codewords of the component codes. This data set includes the simulated GLDPC code constructions and the block error rate performance, which is shown to outperform a variety of state- of-the-art code and decoder designs with suitable lengths and rates for the 5G ultra-reliable low-latency communication regime over an AWGN channel with quadrature PSK modulation.</p> </div> </div>
Reusable 5G Private Campus Network Dataset
<p>In the current literature, the measurements on<br> deployed and operational Fifth-Generation (5G) networks are<br> still lacking. This study offers a dataset of New Radio (NR) signal<br> measurements made using the RPTU Kaiserslautern’s private<br> campus 5G network as well as other public 5G networks to close<br> this gap. The dataset includes measurements of signal strengths<br> such as Synchronization Signal Reference Signal Received Power<br> (SS-RSRP) and Synchronization Signal Signal-to-Interference-<br> plus-Noise ratio (SS-SINR) of serving cells, as well as signal<br> strengths of the neighboring public 5G Standalone (SA) and<br> Non-Standalone (NSA) networks, i.e., within the perimeter of the<br> campus. The measurements described here are taken at various<br> indoor and outdoor locations using the Rohde & Schwarz’s<br> (R&S) TSME6 and TSMA6 scanners. The primary goal of the<br> dataset is to allow researchers to reuse data from the 5G SA live<br> network in understanding the radio access network (RAN) and<br> Physical Layer characteristics of the network and use them for<br> machine learning purposes to optimize the network. In addition<br> to this data, several areas where the data can be used for learning<br> and optimization tasks in the network are provided.</p>
FUDGE-5G Tracking data
<p>This dataset contains the gathered tracking data from the main communication and dissemination platforms used by the project.</p>
5G Modem recorded data
<p>This dataset was gathered from the 5G Virtual Office vertical tests and trials. The data was collected from the 5G modem and includes Signal-to-Noise Ratio (SINR) and Reference Signal Received Quality (RSRQ) values, among others. Information about the connection status was also recorded. Values from the collected KPIs are also included in this dataset</p>
Generalized LDPC codes for ultra reliable low latency communication in 5G and beyond
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Ericsson 5G NSA network RF and throughput measurements on AERPAW network
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TeamUp5G: A Multidisciplinary Approach to Training and Research on New RAN Techniques for 5G Ultra-Dense Mobile Networks
<p>This video presents a summary of the main research directions being followed in TeamUp5G European Training Network. This project is teaming up a new generation of researchers and entrepreneurs ready to address complex engineering problems and innovation to work both at university and industry in the 5G field. Research is focused on the radio access network (RAN) techniques for 5G, considering ultra-dense mobile networks as a key ingredient of the mobile networks and their evolution. It covers a wide spread of topics from the physical layer and medium access control to applications, looking at spectrum sharing and energy efficiency as important features.</p>
Dataset (active) - The Chronicles of 5G Non-Standalone: An Empirical Analysis of Performance and Service Evolution
<p><strong>%%%% 26/09/2025 - NOTE: This entry only includes the active measurements conducted in the following work. </strong></p> <p><strong>The passive measurements can be found in https://doi.org/10.5281/zenodo.17208870</strong></p> <p> </p> <p>The present dataset is open-sourced with the paper "The Chronicles of 5G Non-Standalone: An Empirical Analysis of Performance and Service Evolution".</p> <p>The paper is authored by Giuseppe Caso, Mohammad Rajiullah, Konstantinos Kousias, Usman Ali, Nadir Bouzar, Luca De Nardis, Anna Brunstrom, Ozgu Alay, Marco Neri, and Maria-Gabriella Di Benedetto</p> <p>If you use the dataset for your own research activities and publications, please consider citing the paper as follows: </p> <p><strong>G. Caso et al., "The Chronicles of 5G Non-Standalone: An Empirical Analysis of Performance and Service Evolution," IEEE Open Journal of the Communications Society(IEEE OJ-COMS), pp. 1-21, 2024.</strong></p> <p><strong>@article{caso2024chronicles, <br>title={{The Chronicles of 5G Non-Standalone: An Empirical Analysis of Performance and Service Evolution}}, <br>author={Caso, G. and Rajiullah, M. and Kousias, K. and Ali, U. and Bouzar, N. and De Nardis, L. and Brunstrom, A. and Alay, O. and Neri, M. and Di Benedetto, M.-G.}, <br></strong><strong>journal={IEEE Open Journal of the Communications Society},<br>pages={1--21}, <br></strong><strong>year={2024}, <br>publisher={IEEE}<br>}</strong></p> <p>A detailed description of the dataset is provided in the paper, and the README file provides additional details.</p> <p>In the paper, an in-depth performance analysis of 5G performance is carried out, also exploiting a dataset previously collected and open-sourced at https://zenodo.org/records/8224890, and fully described in the following publication:</p> <p><strong>@article{kousias2023large,<br> title={{A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements}},<br> author={Kousias, K. and Rajiullah, M. and Caso, G. and Ali, U. and Alay, O. and Brunstrom, A. and De Nardis, L. and Neri, M. and Di Benedetto, M.-G.},<br>journal={IEEE Communications Magazine},<br>volume = {62},<br>number = {5},<br>pages={44--49},<br>year={2024},<br>publisher={IEEE}<br>}</strong></p> <p>Contact Giuseppe Caso (giuseppe.caso@kau.se) for more information on the dataset(s) and potential access to additional data. </p>
Selected processed 5G base station RF-EMF measurement data
<p>This presents the selected processed 5G base station (BS) radio frequency electromagnetic field (RF-EMF) measurement data acquired under measurement campaign in outdoor environment. This data links to the findings shown in Section 5 of D1 report at http://empir.npl.co.uk/5grfex/wp-content/uploads/sites/55/2022/01/updated-EMPIR-18SIP02-5GRFEX-Deliverable-Report-D1.pdf. </p> <p>This work was supported by the EU project 5GRFEX entitled – ‘Metrology for RF exposure from Massive MIMO 5G base station: Impact on 5G network deployment’ (this project has received funding from the support for impact (SIP) programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme), under European Association of National Metrology Institutes (EURAMET) Reference 18SIP02.</p>
5G NR Full Grid Measurements
<p>This data set is collected for the full resource grid 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</p>
SHIFT Project Series "7 Myths About Beyond-5G Debunked with Evidence"
<p>SHIFT's conrotium has addressed and debunked some of the most common misconceptions surrounding Beyond-5G technology. To wrap up this enlightening series, a video was released that summarizes all the myths and the research-based facts.<br>Watch this and get a comprehensive overview of how Beyond-5G is set to transform our world positively and sustainably.</p>
Experimental Evaluation of All-Optical Up- and Down-Conversion of 3GPP 5G NR Signals using an Optomechanical Crystal Cavity Frequency Comb
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[5G-IANA] UC1 - LiDAR information from the vehicle
<p> 360-degree LiDAR distance measurements from the vehicle.</p>
Fabrication of W-band TWT for 5G small cells backhaul
<p>Underlying data corresponding to the paper: F. André, S. Kohler, V. Krozer, Q.T. Le, R. Letizia, C. Paoloni, A. Sabaawi, G. Ulisse, R. Zimmerman, "Fabrication of W-band TWT for 5G small cells backhaul", 18th International Vacuum Electronics Conference (IVEC 2017), London, United Kingdom April 2017.</p>
Evaluating the Safety and Effectiveness of 5G Cloud Follow-up for Cardiovascular Implantable Electronic Devices
ClinicalTrials.gov study NCT06652750. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
The Effects of 5G Radiation on Skin
ClinicalTrials.gov study NCT05933954. IPD Sharing: NO. Countries: 1. Publications: 1.
Assessment of Distal Limbs Fractures With Cone-Beam CT Newtom 5G
ClinicalTrials.gov study NCT01796600. IPD Sharing: Not stated. Countries: 1. Publications: 1.
5G-PICTURE_Smart_City_Demo_mmWave_60GHz_dataset
<p>In the context of 5G-PICTURE, three demos were performed, one being the Smart City demo. For this demonstration, three 60 GHz devices (one AP and two STA) were deployed to provide the high throughout Point-to-MultiPoint (P2MP) wireless connectivity for two use cases, the Virtual Reality and the Safety Camera. The two links were stationary with a distance of 60 metres and 80 metres, respectively, providing the wireless connectivity to the tents, specifically installed in the Millennium Square for the abovementioned use cases. In particular, for the mmWave (60GHz) backhaul links, the end-to-end throughput and latency have been measured in the field testing in days before the demo.</p> <p>For more details, interested reader should refer to the project website (https://www.5g-picture-project.eu) and deliverable D6.3.</p>
5G-PICTURE WP6 Stadium Massive MIMO Demo Results
<p>This data set comprises of the Massive MIMO demo results and KPIs, as demonstrated in real-time on 13 March 2020, as part of the 5G-PICTURE WP6 Stadium Demo.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.