Unveiling the Potential of Artificial Intelligence and Machine Learning in the 5G Network Landscape: A Comprehensive Review

Rizvi, Samreen (2023) Unveiling the Potential of Artificial Intelligence and Machine Learning in the 5G Network Landscape: A Comprehensive Review. Asian Journal of Research in Computer Science, 16 (4). pp. 23-31. ISSN 2581-8260

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Abstract

Exploring successful case studies, scholars and industry experts concur that artificial intelligence (AI) and 5G technology, as holistic solutions, exhibit remarkable efficiency. The integration of AI has notably resolved wireless communication dilemmas that defy traditional modeling approaches, substantially diminishing technological uncertainties. Furthermore, 5G technology is poised to amalgamate communication, computation, sensing, and control across diverse industries. However, the convergence of these capabilities also introduces complexity, a challenge that can be effectively addressed through the application of artificial intelligence and machine learning functionalities. These cutting-edge technologies not only ensure data security but also satisfy stringent latency requirements while minimizing the burden on both communication and computation resources. In this context, a thorough examination of machine learning and artificial intelligence applications geared towards optimizing communication, computation, and resource allocation within the realm of 5G technology holds paramount significance.

In pursuit of this objective, the study endeavors to offer a comprehensive outlook on the current landscape of artificial intelligence research within the 5G domain. By scrutinizing recent studies, we aim to encapsulate the contributions and prevailing trends associated with these technologies. Ultimately, our aim is to empower researchers and industry practitioners with insights that will facilitate informed decision-making when selecting the most suitable machine learning and artificial intelligence approaches for their endeavors.

Item Type: Article
Subjects: Academic Digital Library > Computer Science
Depositing User: Unnamed user with email info@academicdigitallibrary.org
Date Deposited: 25 Sep 2023 08:06
Last Modified: 25 Sep 2023 08:06
URI: http://publications.article4sub.com/id/eprint/2182

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