Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks

TL;DR

This paper presents a data-driven edge architecture for 5G networks that leverages machine learning to improve user prediction and traffic routing, demonstrating enhanced accuracy through controller-based spatial correlation.

Contribution

It introduces an edge-controller architecture for 5G networks that enables ML-driven predictions and dynamic clustering based on mobility patterns, improving network management.

Findings

ML algorithms improve user number prediction accuracy.

Controller-based spatial correlation enhances prediction over local data.

The architecture supports dynamic clustering and traffic routing.

Abstract

The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy the ultra-low latency demand of future applications. In this paper, we argue that such deployments can also be used to enable advanced data-driven and Machine Learning (ML) applications in mobile networks. We propose an edge-controller-based architecture for cellular networks and evaluate its performance with real data from hundreds of base stations of a major U.S. operator. In this regard, we will provide insights on how to dynamically cluster and associate base stations and controllers, according to the global mobility patterns of the users. Then, we will describe how the controllers can be used to run ML algorithms to predict the number of users in each base station, and a use case in which these predictions are exploited by a higher-layer application to route vehicular traffic according to network Key Performance Indicators (KPIs). We show that the prediction accuracy improves when based on machine learning algorithms that rely on the controllers' view and, consequently, on the spatial correlation introduced by the user mobility, with respect to when the prediction is based only on the local data of each single base station.