Innovative Machine Learning Strategies for Predictive Network Management in 5G - Ateek Mansoori (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Fifth-generation (5G) wireless networks introduce unprecedented data rates, massive device connectivity, and diverse service requirements (e.g. enhanced mobile broadband, ultra-reliable low-latency communications). These advances come with significant resource allocation challenges – dynamic traffic loads, stringent Quality of Service (QoS) demands, and limited spectral resources must be managed efficiently. In this paper, we investigate predictive network resource management in 5G using supervised machine learning models implemented in MATLAB. We formulate resource allocation as a supervised learning problem, where algorithms learn to predict resource needs or performance metrics (such as required bandwidth or congestion level) from real-time network parameters. A range of models – including decision trees, support vector machines (SVMs), neural networks, random forests, and gradient boosting ensembles – are developed and compared on 5G simulation data. Key 5G metrics (e.g. user signal-to-noise ratio, throughput demand, latency requirement, and bandwidth utilization) are used as input features for prediction. Our MATLAB-based simulation generates training data reflecting a 5G cell scenario, and we evaluate each model’s accuracy in forecasting resource allocation needs. The results show that ensemble tree-based models and deep neural networks achieve the highest prediction accuracy. In particular, a gradient boosting model achieves the best performance for continuous resource demand prediction, while a boosted decision tree classifier achieves over 94% accuracy in predicting network congestion states. These models outperform classical approaches such as linear regression or SVM, especially in capturing the complex non-linear relationships inherent in 5G traffic patterns. We present comparative results including performance metrics (accuracy, mean squared error, R2) and discuss the trade-offs (e.g. complexity vs. accuracy) of each approach. The study concludes that ensemble learning (particularly gradient-boosted trees) and deep neural networks are the most effective supervised learning strategies for predictive 5G resource management, enabling proactive and adaptive resource allocation. We also highlight avenues for future work, including integration of reinforcement learning for real-time autonomous optimization and the use of explainable AI to interpret model decisions in live 5G networks.