Dataset Description:

Based on some real-world events, the dataset offers a synthetic representation of 5G network states and metrics during a high traffic event, such as a major sports gathering in a city. Each row corresponds to a unique record capturing the attributes of the network at a particular moment, and each column corresponds to a specific feature or attribute.



Dataset: IQ samples of LTE, 5G NR, WiFi, ITS-G5, and C-V2X PC5

Thes dataset comprises IQ samples captured from ITSG-5, C-V2X PC5, WiFi, LTE, 5G NR and Noise. Six different dataset bunches are collected at sampling rates of 1, 5, 10, 15 , 20, and 25 Msps. In each dataset cluster, 7500 examples are collected from each considered technology. The dataset size at each considered sampling rate is 7500 X M, where M can be 44, 220, 440, 660, 880, and 1100 for a sampling rate of 1, 5, 10, 15 , 20, and 25 Msps,respectively.



The advancements in the field of telecommunications have resulted in an increasing demand for robust, high-speed, and secure connections between User Equipment (UE) instances and the Data Network (DN). The implementation of the newly defined 3rd Generation Partnership Project 3GPP (3GPP) network architecture in the 5G Core (5GC) represents a significant leap towards fulfilling these demands. This architecture promises faster connectivity, low latency, higher data transfer rates, and improved network reliability.


Channel frequency response (CFR) dataset used for "In-Situ Calibration of Antenna Arrays for Positioning With 5G Networks" paper (IEEE Transactions on Microwave Theory and Techniques, in print, doi: 10.1109/TMTT.2023.3256532, preprint link: https://arxiv.org/abs/2303.04470).


Fifth Generation 5G cellular network users are increasing exponentially, where 5G coverage is a challenge for global telecommunications to provide end-users with maximum Quality of Experience (QoE). 5G technology New Radio (NR) is developed  to address high bandwidth, low latency and massive connectivity requirements of enhanced Mobile Broadband (eMBB) compared to Fourth Generation (4G) Long-Term Evolution (LTE).


This dataset includes real-world time-series statistics from network traffic on real commercial LTE networks in Greece. The purpose of this dataset is to capture the QoS/QoE of three COTS UEs interacting with three edge applications. Specifically, the following features are included:  Throughput and Jitter for each UE-Application and Channel Quality Indicator (CQI) for each UE. The interactions were generated from a realistic network behavior in an office by developing multiple network traffic scenarios.


Predicting the behavior of real-time traffic (e.g., VoIP) in mobility scenarios could help the operators to better plan their network infrastructures and to optimize the allocation of resources. Accordingly, we propose a forecasting analysis of crucial QoS/QoE descriptors (some of which neglected in the technical literature) of VoIP traffic in a real mobile environment.


The dataset was collected by performing uplink/downlink throughput measurements in Munich, Germany. The user side device was a vehicle with roof-mounted antenna (approx. 1.5 m height), and on the network side was a base station antenna mounted at the top of a building (height of the antenna with respect to the ground: 21 m). The measurements were collected at the center frequency of 3.41 GHz, with 40 MHz of bandwidth, with antenna gain of 15.5dBi (5dBi) at the base station (vehicle) side. The maximum throughput in the uplink was 40 Mbps.


This dataset includes real-world Channel Quality Indicator (CQI) values from UEs connected to real commercial LTE networks in Greece. Channel Quality Indicator (CQI) is a metric posted by the UEs to the base station (BS). It is linked with the allocation of the UE’s modulation and coding schemes and ranges from 0 to 15 in values. This is from no to 64 QAM modulation, from zero to 0.93 code rate, from zero to 5.6 bits per symbol, from less than 1.25 to 20.31 SINR (dB) and from zero to 3840 Transport Block Size bits.


5G-NR is beginning to be widely deployed in the mmWave frequencies in urban areas in the US and around the world. Due to the directional nature of mmWave signal propagation, improving performance of such deployments heavily relies on beam management and deployment configurations.