Authors: Shubham sahu, Dr. Dharmbir Yadav
Abstract: The rapid growth of modern telecommunication technologies such as 2G, 4G LTE, and 5G has significantly increased the complexity of telecom network management. Telecom operators continuously generate massive amounts of network data related to traffic usage, bandwidth utilization, latency, throughput, and user activity. Traditional telecom monitoring systems mainly rely on manual analysis and threshold-based alert mechanisms, which are often unable to predict future network congestion, performance degradation, or operational failures effectively. To overcome these limitations, intelligent and automated monitoring solutions are required for efficient telecom network management. This research work, titled “Telecom Network Intelligence System using AI,” proposes an Artificial Intelligence (AI) and Machine Learning (ML) based framework for intelligent telecom network monitoring, traffic prediction, congestion detection, and performance analysis. The proposed system integrates telecom KPI analytics, predictive Machine Learning models, and real-time dashboard visualization to support proactive telecom operations and data-driven decision-making. The system analyzes important telecom Key Performance Indicators (KPIs) such as Call Setup Success Rate (CSSR), Call Drop Rate, LTE Throughput, PRB Utilization, Network Latency, and User Throughput collected from 2G, 4G LTE, and 5G networks. Machine Learning algorithms including Linear Regression, Random Forest, and Long Short-Term Memory (LSTM) are utilized for traffic forecasting, anomaly detection, and congestion prediction. The proposed framework also integrates Python, SQL, Advanced Excel, and Microsoft Power BI for telecom data preprocessing, predictive analytics, and interactive dashboard development. The AI-driven dashboards provide real-time KPI monitoring, network health visualization, congestion alerts, and technology-wise performance comparison. Experimental analysis demonstrates that the proposed system improves prediction accuracy, reduces operational complexity, supports proactive fault management, and enhances telecom network efficiency. The research contributes toward the development of intelligent telecom monitoring systems capable of supporting future AI-driven telecom operations, AIOps integration, self-healing networks, and next-generation 5G/6G communication infrastructures.
