AI-Driven 5G Network Optimization: A Comprehensive Review of Resource Allocation, Traffic Management, and Dynamic Network Slicing

Dileesh Chandra Bikkasani; Malleswar Reddy Yerabolu

doi:doi:10.11648/j.ajai.20240802.14

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AI-Driven 5G Network Optimization: A Comprehensive Review of Resource Allocation, Traffic Management, and Dynamic Network Slicing

Dileesh Chandra Bikkasani^*

, Malleswar Reddy Yerabolu

Published in American Journal of Artificial Intelligence (Volume 8, Issue 2)

Received: 24 October 2024 Accepted: 9 November 2024 Published: 28 November 2024

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Abstract

The rapid advancement of 5G networks, coupled with the increasing complexity of resource management, traffic handling, and dynamic service demands, underscores the necessity for more intelligent network optimization techniques. This paper comprehensively reviews AI-driven methods applied to 5G network optimization, focusing on resource allocation, traffic management, and network slicing. Traditional models face limitations in adapting to the dynamic nature of modern telecommunications, while AI techniques—particularly machine learning (ML) and deep reinforcement learning (DRL)—offer scalable and adaptive solutions. These approaches facilitate real-time optimization by learning from network conditions, predicting traffic patterns, and managing resources intelligently across virtual network slices. The integration of AI into 5G networks enhances performance, reduces latency, and ensures efficient bandwidth utilization, which is essential for supporting emerging applications such as the Internet of Things (IoT), autonomous systems, and augmented reality. Furthermore, this paper highlights key AI techniques and their applications to 5G challenges, illustrating their potential to drive future innovations in network management. By laying the groundwork for autonomous network operations in 6G and beyond, this research emphasizes the transformative impact of AI on telecommunications infrastructure and its role in shaping the future of connectivity.

Published in	American Journal of Artificial Intelligence (Volume 8, Issue 2)
DOI	10.11648/j.ajai.20240802.14
Page(s)	55-62
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

5G, Telecommunication, Wireless Communication, Artificial Intelligence, Network Performance

References

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APA Style

Bikkasani, D. C., Yerabolu, M. R. (2024). AI-Driven 5G Network Optimization: A Comprehensive Review of Resource Allocation, Traffic Management, and Dynamic Network Slicing. American Journal of Artificial Intelligence, 8(2), 55-62. https://doi.org/10.11648/j.ajai.20240802.14

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ACS Style

Bikkasani, D. C.; Yerabolu, M. R. AI-Driven 5G Network Optimization: A Comprehensive Review of Resource Allocation, Traffic Management, and Dynamic Network Slicing. Am. J. Artif. Intell. 2024, 8(2), 55-62. doi: 10.11648/j.ajai.20240802.14

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AMA Style

Bikkasani DC, Yerabolu MR. AI-Driven 5G Network Optimization: A Comprehensive Review of Resource Allocation, Traffic Management, and Dynamic Network Slicing. Am J Artif Intell. 2024;8(2):55-62. doi: 10.11648/j.ajai.20240802.14

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@article{10.11648/j.ajai.20240802.14,
  author = {Dileesh Chandra Bikkasani and Malleswar Reddy Yerabolu},
  title = {AI-Driven 5G Network Optimization: A Comprehensive Review of Resource Allocation, Traffic Management, and Dynamic Network Slicing
},
  journal = {American Journal of Artificial Intelligence},
  volume = {8},
  number = {2},
  pages = {55-62},
  doi = {10.11648/j.ajai.20240802.14},
  url = {https://doi.org/10.11648/j.ajai.20240802.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20240802.14},
  abstract = {The rapid advancement of 5G networks, coupled with the increasing complexity of resource management, traffic handling, and dynamic service demands, underscores the necessity for more intelligent network optimization techniques. This paper comprehensively reviews AI-driven methods applied to 5G network optimization, focusing on resource allocation, traffic management, and network slicing. Traditional models face limitations in adapting to the dynamic nature of modern telecommunications, while AI techniques—particularly machine learning (ML) and deep reinforcement learning (DRL)—offer scalable and adaptive solutions. These approaches facilitate real-time optimization by learning from network conditions, predicting traffic patterns, and managing resources intelligently across virtual network slices. The integration of AI into 5G networks enhances performance, reduces latency, and ensures efficient bandwidth utilization, which is essential for supporting emerging applications such as the Internet of Things (IoT), autonomous systems, and augmented reality. Furthermore, this paper highlights key AI techniques and their applications to 5G challenges, illustrating their potential to drive future innovations in network management. By laying the groundwork for autonomous network operations in 6G and beyond, this research emphasizes the transformative impact of AI on telecommunications infrastructure and its role in shaping the future of connectivity.
},
 year = {2024}
}

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TY  - JOUR
T1  - AI-Driven 5G Network Optimization: A Comprehensive Review of Resource Allocation, Traffic Management, and Dynamic Network Slicing

AU  - Dileesh Chandra Bikkasani
AU  - Malleswar Reddy Yerabolu
Y1  - 2024/11/28
PY  - 2024
N1  - https://doi.org/10.11648/j.ajai.20240802.14
DO  - 10.11648/j.ajai.20240802.14
T2  - American Journal of Artificial Intelligence
JF  - American Journal of Artificial Intelligence
JO  - American Journal of Artificial Intelligence
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AB  - The rapid advancement of 5G networks, coupled with the increasing complexity of resource management, traffic handling, and dynamic service demands, underscores the necessity for more intelligent network optimization techniques. This paper comprehensively reviews AI-driven methods applied to 5G network optimization, focusing on resource allocation, traffic management, and network slicing. Traditional models face limitations in adapting to the dynamic nature of modern telecommunications, while AI techniques—particularly machine learning (ML) and deep reinforcement learning (DRL)—offer scalable and adaptive solutions. These approaches facilitate real-time optimization by learning from network conditions, predicting traffic patterns, and managing resources intelligently across virtual network slices. The integration of AI into 5G networks enhances performance, reduces latency, and ensures efficient bandwidth utilization, which is essential for supporting emerging applications such as the Internet of Things (IoT), autonomous systems, and augmented reality. Furthermore, this paper highlights key AI techniques and their applications to 5G challenges, illustrating their potential to drive future innovations in network management. By laying the groundwork for autonomous network operations in 6G and beyond, this research emphasizes the transformative impact of AI on telecommunications infrastructure and its role in shaping the future of connectivity.

VL  - 8
IS  - 2
ER  -

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Author Information

Dileesh Chandra Bikkasani

Department of Technology Management, University of Bridgeport, Bridgeport, USA

Contact Email

http://orcid.org/0009-0009-3182-1921
Malleswar Reddy Yerabolu

Independent Researcher, North Carolina, USA

http://orcid.org/0009-0002-3683-8449

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Plain Text BibTeX RIS

APA Style

Bikkasani, D. C., Yerabolu, M. R. (2024). AI-Driven 5G Network Optimization: A Comprehensive Review of Resource Allocation, Traffic Management, and Dynamic Network Slicing. American Journal of Artificial Intelligence, 8(2), 55-62. https://doi.org/10.11648/j.ajai.20240802.14

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ACS Style

Bikkasani, D. C.; Yerabolu, M. R. AI-Driven 5G Network Optimization: A Comprehensive Review of Resource Allocation, Traffic Management, and Dynamic Network Slicing. Am. J. Artif. Intell. 2024, 8(2), 55-62. doi: 10.11648/j.ajai.20240802.14

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AMA Style

Bikkasani DC, Yerabolu MR. AI-Driven 5G Network Optimization: A Comprehensive Review of Resource Allocation, Traffic Management, and Dynamic Network Slicing. Am J Artif Intell. 2024;8(2):55-62. doi: 10.11648/j.ajai.20240802.14

Copy | Download

@article{10.11648/j.ajai.20240802.14,
  author = {Dileesh Chandra Bikkasani and Malleswar Reddy Yerabolu},
  title = {AI-Driven 5G Network Optimization: A Comprehensive Review of Resource Allocation, Traffic Management, and Dynamic Network Slicing
},
  journal = {American Journal of Artificial Intelligence},
  volume = {8},
  number = {2},
  pages = {55-62},
  doi = {10.11648/j.ajai.20240802.14},
  url = {https://doi.org/10.11648/j.ajai.20240802.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20240802.14},
  abstract = {The rapid advancement of 5G networks, coupled with the increasing complexity of resource management, traffic handling, and dynamic service demands, underscores the necessity for more intelligent network optimization techniques. This paper comprehensively reviews AI-driven methods applied to 5G network optimization, focusing on resource allocation, traffic management, and network slicing. Traditional models face limitations in adapting to the dynamic nature of modern telecommunications, while AI techniques—particularly machine learning (ML) and deep reinforcement learning (DRL)—offer scalable and adaptive solutions. These approaches facilitate real-time optimization by learning from network conditions, predicting traffic patterns, and managing resources intelligently across virtual network slices. The integration of AI into 5G networks enhances performance, reduces latency, and ensures efficient bandwidth utilization, which is essential for supporting emerging applications such as the Internet of Things (IoT), autonomous systems, and augmented reality. Furthermore, this paper highlights key AI techniques and their applications to 5G challenges, illustrating their potential to drive future innovations in network management. By laying the groundwork for autonomous network operations in 6G and beyond, this research emphasizes the transformative impact of AI on telecommunications infrastructure and its role in shaping the future of connectivity.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - AI-Driven 5G Network Optimization: A Comprehensive Review of Resource Allocation, Traffic Management, and Dynamic Network Slicing

AU  - Dileesh Chandra Bikkasani
AU  - Malleswar Reddy Yerabolu
Y1  - 2024/11/28
PY  - 2024
N1  - https://doi.org/10.11648/j.ajai.20240802.14
DO  - 10.11648/j.ajai.20240802.14
T2  - American Journal of Artificial Intelligence
JF  - American Journal of Artificial Intelligence
JO  - American Journal of Artificial Intelligence
SP  - 55
EP  - 62
PB  - Science Publishing Group
SN  - 2639-9733
UR  - https://doi.org/10.11648/j.ajai.20240802.14
AB  - The rapid advancement of 5G networks, coupled with the increasing complexity of resource management, traffic handling, and dynamic service demands, underscores the necessity for more intelligent network optimization techniques. This paper comprehensively reviews AI-driven methods applied to 5G network optimization, focusing on resource allocation, traffic management, and network slicing. Traditional models face limitations in adapting to the dynamic nature of modern telecommunications, while AI techniques—particularly machine learning (ML) and deep reinforcement learning (DRL)—offer scalable and adaptive solutions. These approaches facilitate real-time optimization by learning from network conditions, predicting traffic patterns, and managing resources intelligently across virtual network slices. The integration of AI into 5G networks enhances performance, reduces latency, and ensures efficient bandwidth utilization, which is essential for supporting emerging applications such as the Internet of Things (IoT), autonomous systems, and augmented reality. Furthermore, this paper highlights key AI techniques and their applications to 5G challenges, illustrating their potential to drive future innovations in network management. By laying the groundwork for autonomous network operations in 6G and beyond, this research emphasizes the transformative impact of AI on telecommunications infrastructure and its role in shaping the future of connectivity.

VL  - 8
IS  - 2
ER  -

Copy | Download