Harnessing Machine Learning for Intelligent Networking in 5G Technology and Beyond: Advancements, Applications and Challenges

Abstract

A revolutionary age in telecommunications is being ushered in by the confluence of machine learning (ML) with fifth-generation (5G) wireless communication technologies and beyond. This research investigates ML approaches in 5G networks for adaptive spectrum usage, quality of service (QoS) management, predictive maintenance, and network optimization. By leveraging ML algorithms, 5G networks can forecast user behavior, allocate resources optimally, and dynamically adjust to changing conditions, enhancing performance and dependability. Additionally, ML-driven methods improve cybersecurity in 5G settings. Furthermore, the integration of ML in 5G networks is pivotal for advancing intelligent transportation systems, enabling dynamic route optimization, adaptive traffic management, and enhanced vehicular communication. Intelligent networks will transform wireless communication by replacing traditional processing with end-to-end solutions, utilizing cognitive radio systems and deep reinforcement learning for optimized spectrum sharing and efficiency. Despite significant potential, challenges such as interoperability, security, scalability, and energy efficiency must be addressed. This paper discusses these challenges and highlights future trends beyond 5G, emphasizing ML's critical role in shaping the future of wireless communication systems.