Deep Learning for Dynamic Resource Management in 5G Networks: A Review

Abstract

Dynamic resource management is important for 5G wireless networks to ensure they are efficient, scalable, and can handle growing connectivity demands while maintaining quality service. The aim of this review is to discuss how deep learning has changed the way complex challenges are being addressed in resource allocation, frequency spectrum management, energy efficiency, and runtime decision-making over 5G wireless networks. It combines the very best of leading-edge research insights into showing, through advanced deep learning techniques like supervised learning, and federated learning, how to allow for intelligent, adaptive solutions that go beyond conventional approaches. The manuscript describes this through a review that compares the strengths of these methodologies in network performance optimization while pointing out some limitations related to computational complexity or lack of extensive real-world testing. It further elaborates on promising future directions, ranging from federated learning for decentralized resource management to enhancing the interpretability of deep learning models and leveraging diverse datasets for improving robustness. The discussion also covers the arrival of 6G networks, which will introduce refined and AI-driven approaches for resource optimization. By establishing the logical links between theoretical developments and practical uses, the presented review will pinpoint the transforming potential of deep learning in re-shaping both the wireless communication networks of the future, but also opening new frontiers well beyond 5G.