Convergence of AI and 6G for Intelligent network management

Abstract

Abstract: The convergence of Artificial Intelligence (AI) and 6G networks presents unprecedented opportunities for enhancing intelligent network management, promising to revolutionize communication systems. As 5G networks mature and 6G emerges on the horizon, AI plays a pivotal role in addressing the increasing complexity and demands of future networks. AI-driven network management frameworks can enable dynamic, real-time adaptation of network resources, ensuring seamless connectivity, low latency, and optimized performance. With the increasing volume of connected devices, as well as the need for ultra-reliable low-latency communication (URLLC) and massive machine-type communications (mMTC), AI technologies such as machine learning (ML), deep learning (DL), and reinforcement learning (RL) are key enablers for automating network planning, orchestration, and self-healing mechanisms in 6G systems. AI empowers network operators to predict failures, optimize spectrum usage, allocate resources efficiently, and minimize operational costs, thus fostering an intelligent and autonomous network. Additionally, AI facilitates the adaptive management of heterogeneous 6G infrastructure, encompassing edge computing, cloud-based platforms, and distributed antenna systems. Integrating AI with 6G can also help in improving energy efficiency, a key challenge for future networks. This paper explores the potential convergence of AI and 6G for intelligent network management, delving into the underlying principles, methodologies, and applications.