Export Citation
Export Citation
Maheshwari, MK 
ORCID: 0000-0002-2843-9758, Raschella, A ORCID: 0000-0002-1626-8947, Mackay, M ORCID: 0000-0001-9013-7884, Eiza, MH ORCID: 0000-0001-9114-8577, Wetherall, J and Laing, J
5G High Density Demand Dataset in Liverpool City Region, UK.
Scientific data.
ISSN 2052-4463
(Accepted)
Preview |
Text
5G High Density Demand Dataset in Liverpool City Region UK.pdf - Accepted Version Available under License Creative Commons Attribution. Download (612kB) | Preview |
Abstract
The wireless network data are a feasible way to understand the user behavior in a given environment and may be utilized for analysis, prediction and optimization. On the other hand, datasets from wireless service providers are not publicly available, and obtaining a dataset in real time is challenging. In this work, we present a 5G dense deployment dataset obtained from the Liverpool City Region High Density Demand (LCR HDD) project. The project involves network deployment and assessment at Salt & Tar and the ACC Arena event venues located in the city of Liverpool. Digital twin technology is considered to generate the dataset, which is inputted to a system level simulator for data modeling and analysis. The data set consists of 3, 000 users in the Salt & Tar venue and 12, 000 users in the ACC Arena venue with features including users' position, traffic type, Radio Unit (RU) association, Signal to Interference and Noise Ratio (SINR), Physical Resource Blocks (PRB), throughput, Block Error Rate (BLER), and a total length of 10, 000 samples. The dataset is validated through experimental measurements and is released in a simple format for easy access.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 46 Information and Computing Sciences; 4006 Communications Engineering; 40 Engineering |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) |
| Divisions: | Computer Science and Mathematics |
| Publisher: | Springer |
| Date of acceptance: | 4 November 2025 |
| Date of first compliant Open Access: | 22 December 2025 |
| Date Deposited: | 22 Dec 2025 16:31 |
| Last Modified: | 22 Dec 2025 16:31 |
| DOI or ID number: | 10.1038/s41597-025-06282-0 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/27764 |
 |
View Item |