Authors: Nguyen Phuc Tran, Oscar Delgado, Brigitte Jaumard, Fadi Bishay
Published on: April 01, 2024
Impact Score: 7.8
Arxiv code: Arxiv:2404.01530
Summary
- What is new: Introduction of an ML model to estimate throughput in 5G and B5G networks with E2E network slices.
- Why this is important: Operators face challenges in maintaining and optimizing networks for emerging services and traffic growth.
- What the research proposes: A machine learning model estimates throughput and other KPIs to improve service assurance in network slices.
- Results: The ML model outperforms existing methods in predicting KPIs with the same or nearly the same computational time.
Technical Details
Technological frameworks used: Machine Learning
Models used: Throughput estimation for E2E network slices
Data used: Current network state data and throughput predictions
Potential Impact
Telecommunications, streaming services, IoT, and autonomous driving industries.
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