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Research Article Open access CC BY 4.0

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

Samreen Rizvi

Asian Journal of Research in Computer Science · pp. 23–31 · Published 23 Sep 2023

10.9734/ajrcos/2023/v16i4367

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.

5G networks machine learning artificial intelligence 6G networks

References (25)

  1. 1 Network Anomaly Detection: Methods, Systems and Tools [DOI]
  2. 2 Optimal Design of Energy-Efficient Multi-User MIMO Systems: Is Massive MIMO the Answer? [DOI]
  3. 3 Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues [DOI]
  4. 4 Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions [DOI]
  5. 5 Improving Traffic Forecasting for 5G Core Network Scalability: A Machine Learning Approach [DOI]
  6. 6 A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era [DOI]
  7. 7 5G Vehicular Network Resource Management for Improving Radio Access Through Machine Learning [DOI]
  8. 8 An Online Context-Aware Machine Learning Algorithm for 5G mmWave Vehicular Communications [DOI]
  9. 9 Optimal 5G network slicing using machine learning and deep learning concepts [DOI]
  10. 10 Machine Learning for Intelligent Authentication in 5G and Beyond Wireless Networks [DOI]
  11. 11 Knowledge-Assisted Deep Reinforcement Learning in 5G Scheduler Design: From Theoretical Framework to Implementation [DOI]
  12. 12 Consideration On Automation of 5G Network Slicing with Machine Learning [DOI]
  13. 13 Machine learning‐based IDS for software‐defined 5G network [DOI]
  14. 14 Deep Learning at the Physical Layer: System Challenges and Applications to 5G and Beyond [DOI]
  15. 15 Machine Learning Threatens 5G Security [DOI]
  16. 16 A Manifesto for Energy-Aware Software [DOI]
  17. 17 A comprehensive survey: Small cell meets massive MIMO [DOI]
  18. 18 Physical‐Layer Channel Authentication for 5G via Machine Learning Algorithm [DOI]
  19. 19 Artificial Intelligence and Machine Learning in 5G and beyond: A Survey and Perspectives [DOI]
  20. 20 Artificial Intelligence in Beyond 5G and 6G Reliable Communications [DOI]
  21. 21 Distributed user-to-multiple access points association through deep learning for beyond 5G [DOI]
  22. 22 Proposal of Allocating Radio Resources to Multiple Slices in 5G using Deep Reinforcement Learning [DOI]
  23. 23 IEEE Journal on Selected Areas in Communications [DOI]
  24. 24 IEEE Transactions on Wireless Communications [DOI]
  25. 25 2018 ITU Kaleidoscope: Machine Learning for a 5G Future (ITU K) [DOI]

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