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AI-Based Smart Network Traffic Management System for Telecommunication
Subject area: Science,Engineering and Technology · Area of research: Scope of AIML in Telecommunication
DOI: 10.64388/IREV10I1-1719364
Abstract
The telecommunication industry is experiencing rapid growth due to 5G, the Internet of Things (IoT), and an increasing number of internet users. This surge in network traffic introduces challenges like congestion, latency, packet loss, and poor Quality of Service (QoS). Traditional, rule-based traffic management systems are insufficient for dynamic conditions. This study presents an AI-Based Smart Network Traffic Management System that utilizes Artificial Intelligence and Machine Learning (ML) to analyze patterns, predict congestion probability, and optimize network resource allocation, significantly improving network efficiency over traditional approaches.
References
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[2] S. Haykin, Neural Networks and Machine Learning, 3rd ed. New Delhi, India: Pearson Education, 2018.
[3] M. Chen, Y. Miao, Y. Hao, and K. Hwang, “Artificial Intelligence in Telecommunication Networks: Applications and Challenges,” IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3502–3530, 2019.
[4] T. O’Shea and J. Hoydis, “An Introduction to Deep Learning for the Physical Layer,” IEEE Transactions on Cognitive Communications and Networking, vol. 3, no. 4, pp. 563–575, 2017.
[5] Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.
How to cite this paper
@article{1719364,
author = {Imran Ahmad Khan},
title = {AI-Based Smart Network Traffic Management System for Telecommunication},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {1},
pages = {250-251},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1719364.pdf},
abstract = {The telecommunication industry is experiencing rapid growth due to 5G, the Internet of Things (IoT), and an increasing number of internet users. This surge in network traffic introduces challenges like congestion, latency, packet loss, and poor Quality of Service (QoS). Traditional, rule-based traffic management systems are insufficient for dynamic conditions. This study presents an AI-Based Smart Network Traffic Management System that utilizes Artificial Intelligence and Machine Learning (ML) to analyze patterns, predict congestion probability, and optimize network resource allocation, significantly improving network efficiency over traditional approaches.},
month = {July},
doi = {https://doi.org/10.64388/IREV10I1-1719364}
}