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

arXiv: https://arxiv.org/abs/1808.07647 · HTML: https://mlsanity.com/paper/1808.07647/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2018.

## Built on by (most cited first, 6 of 6)

- [ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms](https://mlsanity.com/paper/2112.09559/) (2021)
- [OrchestRAN: Network Automation through Orchestrated Intelligence in the Open RAN](https://mlsanity.com/paper/2201.05632/) (2022)
- [Actor-Critic Network for O-RAN Resource Allocation: xApp Design, Deployment, and Analysis](https://mlsanity.com/paper/2210.04604/) (2022)
- [Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffs](https://mlsanity.com/paper/2110.01686/) (2021)
- [Canoe : A System for Collaborative Learning for Neural Nets](https://mlsanity.com/paper/2108.12124/) (2021)
- [Choose, not Hoard: Information-to-Model Matching for Artificial Intelligence in O-RAN](https://mlsanity.com/paper/2208.04229/) (2022)

## Abstract (arXiv)

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.