A Predictive Analytics Approach to Network Management in 5G Using Machine Learning Techniques

Abstract

The advent of Fifth Generation (5G) mobile networks has introduced unprecedented challenges in maintaining optimal network performance due to their highly dynamic, heterogeneous, and dense architectures. Traditional rule-based management systems are increasingly inadequate for handling the complex interactions and real-time decision-making required in such environments. To address these limitations, this study proposes a predictive analytics approach to network management using machine learning (ML) techniques.

The proposed framework integrates supervised learning, deep learning, and reinforcement learning models to enable proactive resource allocation, traffic prediction, and fault detection within 5G infrastructures. Historical and real-time network data are analyzed to forecast network congestion, optimize handover decisions, and dynamically manage bandwidth distribution. The predictive model leverages Long Short-Term Memory (LSTM) networks for time-series traffic forecasting and Reinforcement Learning (RL) for adaptive resource optimization under fluctuating conditions.