AI-Based Predictive Analytics for Telecom Network Quality of Experience

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@article{1723757,
      author = {Karthick Cherladine},
      title = {AI-Based Predictive Analytics for Telecom Network Quality of Experience},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {2},
      number = {7},
      pages = {310-316},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723757.pdf},
      abstract = {Telecommunication networks are becoming increasingly complex due to the rapid growth of connected devices, data traffic, and diverse services such as video streaming, online gaming, and real-time communication. Maintaining high Quality of Experience (QoE) is essential for ensuring user satisfaction and reliable network performance. This paper proposes an AI-based predictive analytics framework for forecasting telecom network QoE using network performance and service-level parameters. The proposed framework analyzes key indicators, including latency, throughput, packet loss, jitter, signal strength, network congestion, and resource utilization. Machine learning techniques are employed to identify hidden patterns, predict future QoE degradation, and generate proactive alerts. The framework enables network operators to detect potential service quality issues before they significantly affect users and supports timely resource optimization. By integrating predictive intelligence with telecom network monitoring, the proposed approach can improve service reliability, reduce performance degradation, and enhance user satisfaction. The framework provides a scalable foundation for intelligent and proactive QoE management in next-generation telecom networks.},
      keywords = {Artificial Intelligence, Predictive Analytics, Telecom Networks, Quality of Experience, Machine Learning, Network Optimization.},
      month = {January},
  }