Artificial Intelligent-Based Resource Management In 6g Networks: A Review

Abstract

This paper discusses how the introduction of 6G networks, which provide extremely fast, low-latency, and massive connections, has the potential to revolutionize wireless communication and create new uses for it, such as holographic communication, autonomous systems, and real-time AI-driven services. However, managing the resources in such complex, dynamic, and varied networks presents a number of challenges. Conventional resource management techniques are no longer sufficient to meet the demands of 6G environments. This article explores how AI may be included into resource management for 6G networks, with a focus on how it can enhance scalability, optimize network performance, and improve resource allocation. AI techniques such as machine learning (ML), deep learning (DL), and reinforcement learning (RL) can enable intelligent decision-making processes for dynamic spectrum allocation, energy-efficient resource consumption, load balancing, and quality of service (QoS) provisioning. AI's ability to analyze vast amounts of real-time network data enables networks to predict network activity, adapt to changing conditions autonomously, and optimize resource utilization to enhance user experience and network efficiency.