Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks
| Autor: | Ke Zhang, Supratim Deb, Rittwik Jana, Michele Zorzi, Michele Polese, Velin Kounev |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Networks and Communications Computer science Computer Science - Artificial Intelligence Big data Cloud computing 02 engineering and technology Machine learning computer.software_genre Data-driven Machine Learning (cs.LG) Computer Science - Networking and Internet Architecture Base station 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Cluster analysis Networking and Internet Architecture (cs.NI) business.industry 020206 networking & telecommunications Artificial Intelligence (cs.AI) Cellular network Artificial intelligence Performance indicator business computer Software 5G |
| Popis: | 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. 15 pages, 10 figures, 5 tables. IEEE Transactions on Mobile Computing |
| Databáze: | OpenAIRE |
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