Beyond Throughput: a 4G LTE Dataset with Channel and Context Metrics
We kindly ask that should you mention any of our datasets, or use our code, in your publication, that you would reference the following paper:
D. Raca, J.J. Quinlan, A.H. Zahran, C.J. Sreenan. "Beyond Throughput: a 4G LTE Dataset with Channel and Context Metrics" In Proceedings of ACM Multimedia Systems Conference (MMSys 2018), Amsterdam, The Netherlands, June 12 - 15, 2018. Further details on our datasets are available in the conference paper.
The following provides a 4G trace dataset composed of client-side cellular key performance indicators (KPIs) collected from two major Irish mobile operators, across different mobility patterns (static, pedestrian, car, tram and train). The 4G trace dataset contains 135 traces, with an average duration of fifteen minutes per trace, with viewable throughput ranging from 0 to 173 Mbit/s at a granularity of one sample per second. Our traces are generated from a well-known non-rooted Android network monitoring application, G-NetTrack Pro. This tool enables capturing various channel related KPIs, context-related metrics, downlink and uplink throughput, and also cell-related information.
To supplement our real-time 4G production network dataset, we also provide a synthetic dataset generated from a large-scale 4G ns-3 simulation that includes one hundred users randomly scattered across a seven-cell cluster. The purpose of this dataset is to provide additional information (such as competing metrics for users connected to the same cell), thus providing otherwise unavailable information about the eNodeB environment and scheduling principle, to end user. In addition to this dataset we also provide the code and context information to allow other researchers to generate their own synthetic datasets.
DATASET 1: Trace-based Dataset obtained from real cellular operational networks
Link: 4G LTE Dataset Download
The dataset contains missing values. This is a typical characteristic of a real dataset. Most of missing values come from GPS coordinates of serving cell as a consequence of not having all tower entries in a database. These values are marked with "-". To alleviate this problem standard practice is to use some imputation method. Many libraries have the implementation of several imputation algorithms, e.g., fancyimpute.
The zip file contains necessary instructions for collecting and parsing network traces. However, you need to download G-NET Track app to successfully perform experiments (link).
Instructions for collecting and parsing traces (link).
Auxiliary traces: Traces collected with different mobile phone manufacturer and sampling interval Download
DATASET 2: Dataset obtained from simulated LTE network (ns-3)
The synthetic dataset is generated by ns-3 simulation of a seven-cell cluster with 100 mobile users. All users have constant velocity of 80kph and use Gauss-Markov mobility pattern.
Link: NS-3 LTE Dataset Download
Instructions for patching NS-3 simulation (link).
Instructions for running NS-3 seven-cell simulation (link).
This work has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number 13/IA/1892.