Version 1
: Received: 27 October 2024 / Approved: 27 October 2024 / Online: 28 October 2024 (11:02:44 CET)
How to cite:
Bikkasani, D. C. Data Science and Machine Learning for Network Management in Telecommunication Systems: Trends and Opportunities. Preprints2024, 2024102091. https://doi.org/10.20944/preprints202410.2091.v1
Bikkasani, D. C. Data Science and Machine Learning for Network Management in Telecommunication Systems: Trends and Opportunities. Preprints 2024, 2024102091. https://doi.org/10.20944/preprints202410.2091.v1
Bikkasani, D. C. Data Science and Machine Learning for Network Management in Telecommunication Systems: Trends and Opportunities. Preprints2024, 2024102091. https://doi.org/10.20944/preprints202410.2091.v1
APA Style
Bikkasani, D. C. (2024). Data Science and Machine Learning for Network Management in Telecommunication Systems: Trends and Opportunities. Preprints. https://doi.org/10.20944/preprints202410.2091.v1
Chicago/Turabian Style
Bikkasani, D. C. 2024 "Data Science and Machine Learning for Network Management in Telecommunication Systems: Trends and Opportunities" Preprints. https://doi.org/10.20944/preprints202410.2091.v1
Abstract
This paper examines the transformative impact of data science, machine learning (ML), and artificial intelligence (AI) on network management in telecommunications, focusing on techniques such as network monitoring, predictive maintenance, anomaly detection, automated network configuration, and self-healing mechanisms. We analyze specific methodologies, including deep learning for anomaly detection and federated learning for predictive maintenance, and address current challenges such as data quality, system integration, and model interpretability. Emerging technologies like edge computing, federated learning, and quantum computing are explored for their potential to enhance predictive maintenance and network management. The paper provides an overview of how AI-driven solutions are revolutionizing telecom networks, offering unprecedented efficiency, reliability, and performance while highlighting the need for ongoing research to tackle complex issues of explainability and privacy.
Keywords
Data Science; Network Management; Telecommunication Systems; Network Operations
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
