The rapid expansion of data-heavy apps on 5G an‎d new 6G systems creates operational problems that have never been seen before, making old, reactive optimization methods useless. Mobile networks produce petabytes of data, but there is a big gap between the amount of data that is available an‎d the amount that can be used effectively for real-time, user-centered performance monitoring. This study suggests a new framework that uses network data, big data analytics, an‎d artificial intelligence to close this gap by optimizing things automatically an‎d ahead of time. The main part of the suggested solution is a practical, two-tiered analytical system that includes a proactive congestion forecasting model an‎d a real-time anomaly detection engine. The Unified Network Health Score (NHS) is a single, actionable metric that combines these parts in a way that makes it easy to understan‎d for automated control. It turns complicated network statuses into an easy-to-understan‎d signal. There are three main benefits that this work is expected to bring: a big boost in network efficiency, including higher throughput an‎d lower energy costs; a real improvement in user Quality of Experience (QoE) by stopping performance problems before they happen; an‎d a big dro‎p in operational costs (OPEX) for operators. In the end, this study gives us a plan that we can use to make mobile networks smarter an‎d better at optimizing themselves.

10/29/2025 12:00:00 AM

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1404/08/08

Review of Exploring Ways to Use Network Data to Optimize Mobile Phone Networks

Network Optimization , Artificial Intelligence (AI) , 5G/6G Networks , Quality of Experience (QoE), , Predictive Analytics , Big Data Analytics