An Open Mobile Communications Drive Test Data Set and Its Use for Machine Learning

Abstract

The capability to provide guarantees for network metrics, such as latency, data rate, and reliability will be an important factor for widespread adoption of next generation mobile networks and hence, such metrics play a central role in standards for new wireless communication technologies. However, due to the inherently stochastic nature of mobile communications, any guarantees can only be of statistical nature and are highly dependent on the actual physical environment. To analyze the stochastic behavior, this paper presents a tool chain for measurement, collection, evaluation, and prediction of controlled mobile communications drive test data. We also publish the underlying data set of measurements covering two years' worth of highway traffic on a 25 km long section comprising 267 198 data points. We statistically evaluate the data set and validate it with a corresponding data set from another source. Applying machine learning to the data set illustrates possible use cases: Feed-forward neural networks to predict the data rate in five application scenarios, LIME to explain the behavior of the model, and an autoencoder to describe the interaction of five signal strength parameters. The data set and the tool chain show how machine learning can be applied to wireless networks and provide fellow researchers with the means to make further experiments.

Machine Learning With Computer Networks: Techniques, Datasets, and Models

“…In Farthofer et al [128] an LTE dataset for the use of ML is described. The dataset is measured on an Austrian highway and contains over 2000 measurement points per month over a time period of two years.…”

“…There are plenty of use cases to apply XAI in communication networks [238]. These use cases include network planning and engineering [239], resource allocation [240], [241], performance management [128], [242], and security management [243], [244]. Most of these works use the methods presented in this chapter to make their models explainable.…”

The Story of QoS Prediction in Vehicular Communication: From Radio Environment Statistics to Network-Access Throughput Prediction

“…Besides throughput, ML is also used to predict latency [5,36,37] and handovers [38]. Recently, several datasets intended for ML-based studies have been made publicly available [39,40]. Those works focus on introducing the datasets instead of applying ML methods.…”

A New Mobile RF Drive Test Application

“…The outcomes of our study lay the foundation for future extensions of our research. One avenue involves the training of AI models leveraging mobile data to proactively predict and address service anomalies, thus allowing for preemptive parameter adjustments [19]. Moreover, we advocate for the development of a web application to enhance data analysis capabilities, alongside refining the user interface of our mobile application for an even more userfriendly experience.…”