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2025 | OriginalPaper | Buchkapitel

AI-Driven Traffic Optimization in 5G and Beyond: Challenges, Strategies, Solutions, and Prospects

verfasst von : Ezekiel Ehime Agbon, Aminu Chiroma Muhammad, Christopher Akinyemi Alabi, Agburu Ogah Adikpe, Sena Timothy Tersoo, Agbotiname Lucky Imoize, Samarendra Nath Sur

Erschienen in: Advances in Communication, Devices and Networking

Verlag: Springer Nature Singapore

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Abstract

As 5G networks continue to evolve and pave the way for future telecommunication technologies, the role of Artificial Intelligence (AI) and Machine Learning (ML) in optimizing traffic management becomes increasingly crucial. This paper explores the integration of AI and ML in telecommunication networks, focusing on their applications, challenges, and potential solutions for traffic optimization in 5G and beyond. The paper looks into specific use cases, such as network congestion management, quality of service (QoS) enhancement, and energy efficiency improvements. Additionally, the paper discusses the implications of AI-driven traffic optimization on network performance, user experience, and the broader telecommunication industry landscape. Through this review, the paper shed light on the transformative potential of AI and ML in shaping the future of telecommunication networks.

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Titel: AI-Driven Traffic Optimization in 5G and Beyond: Challenges, Strategies, Solutions, and Prospects
verfasst von: Ezekiel Ehime Agbon
Aminu Chiroma Muhammad
Christopher Akinyemi Alabi
Agburu Ogah Adikpe
Sena Timothy Tersoo
Agbotiname Lucky Imoize
Samarendra Nath Sur
Verlag: Springer Nature Singapore
Buch: Advances in Communication, Devices and Networking
Print ISBN: 978-981-9764-64-8

Electronic ISBN: 978-981-9764-65-5

Copyright-Jahr: 2025
DOI: https://doi.org/10.1007/978-981-97-6465-5_40

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