Intelligent QOS and Traffic Management for 5g MVNO Networks

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@article{1723791,
      author = {Karthick Cherladine},
      title = {Intelligent QOS and Traffic Management for 5g MVNO Networks},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {12},
      pages = {877-883},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723791.pdf},
      abstract = {The rapid expansion of 5G networks and mobile applications has created increasing demand for reliable and efficient Quality of Service (QoS) and traffic management in Mobile Virtual Network Operator (MVNO) environments. Since MVNOs depend on infrastructure provided by Mobile Network Operators (MNOs), dynamically managing network resources and maintaining service quality can be challenging under changing traffic conditions. This paper proposes an Intelligent QoS and Traffic Management Framework for 5G MVNO Networks using Artificial Intelligence (AI) and Machine Learning (ML) techniques. The proposed framework analyzes network parameters such as bandwidth, latency, packet loss, throughput, signal strength, traffic load, congestion, resource utilization, and application requirements. Machine learning models including Random Forest, Support Vector Machine (SVM), XGBoost, and Artificial Neural Network (ANN) are used to classify traffic conditions and predict QoS degradation. An intelligent traffic-management mechanism subsequently prioritizes services and dynamically allocates available network resources according to predicted traffic conditions and QoS requirements. The proposed approach aims to reduce latency and packet loss, improve throughput and resource utilization, minimize congestion, and provide consistent QoS for MVNO subscribers. The framework provides a scalable foundation for intelligent and adaptive QoS management in 5G MVNO networks.},
      keywords = {5G, MVNO, Quality of Service, Artificial Intelligence, Machine Learning, Traffic Management, Network Optimization, QoS Prediction, Network Slicing, Resource Allocation.},
      month = {June},
      doi = {https://doi.org/10.64388/IREV7I12-1723791}
  }