The convergence of artificial intelligence (AI) with the physical world is reshaping the future of intelligent systems through real-time perception, interaction, and control within physical environments. To support this new paradigm, 6G networks are envisioned as critical enablers, offering ultra-low latency, high reliability, and service-aware intelligence to facilitate seamless human-machine collaboration. This paper proposes a functional framework that integrates Agentic AI into the 6G architecture, introducing the concept of Agentic AI-Enabled 6G Network Services (AA6NS). In this framework, user intents are translated and processed across the application layer, core network (CN), and radio access network (RAN), where Agentic AI dynamically manages task-level quality of service/quality of experience (QoS/QoE), orchestrates multi-device service groups, and enables real-time network adaptation. The proposed architecture with the new 6G techniques establishes a foundation for future physical AI applications across domains such as autonomous mobility, smart manufacturing, and remote robotics.

As the wireless communication network undergoes continuous expansion, the challenges associated with network management and optimization are becoming increasingly complex. To address these challenges, the emerging artificial intelligence (AI) and machine learning (ML) technologies have been introduced as a powerful solution. They empower wireless networks to operate autonomously, predictively, on-demand, and with smart functionality, offering a promising resolution to intricate optimization problems. This paper aims to delve into the prevalent applications of AI/ML technologies in the optimization of wireless networks. The paper not only provides insights into the current landscape but also outlines our vision for the future and considerations regarding the development of an intelligent 6G network.

To meet the key performance requirement of the 5G network and the demand of the growing number of mobile subscribers, millions of base stations are being constructed. 5G New Radio is designed to enable denser network deployments, which raises significant concerns about network energy consumption. Machine learning (ML), as a kind of artificial intelligence (AI) technologies, can enhance network optimization performance and energy efficiency. In this paper, we propose AI/ML-assisted energy-saving strategies to achieve optimal performance in terms of cell shutdown duration and energy efficiency. To realize network intelligence, we put forward the concept of intrinsic AI, which integrates AI into every aspect of wireless communication networks.

The mobile Internet and Internet of Things are considered the main driving forces of 5G, as they require an ultra-dense deployment of small base stations to meet the increasing traffic demands. 5G new radio (NR) access is designed to enable denser network deployments, while leading to a significant concern about the network energy consumption. Energy consumption is a main part of network operational expense (OPEX), and base stations work as the main energy consumption equipment in the radio access network (RAN). In order to achieve RAN energy efficiency (EE), switching off cells is a strategy to reduce the energy consumption of networks during off-peak conditions. This paper introduces NR cell switching on/off schemes in 3GPP to achieve energy efficiency in 5G RAN, including intra-system energy saving (ES) scheme and inter-system ES scheme. Additionally, NR architectural features including central unit/distributed unit (CU/DU) split and dual connectivity (DC) are also considered in NR energy saving. How to apply artificial intelligence (AI) into 5G networks is a new topic in 3GPP, and we also propose a machine learning (ML) based scheme to save energy by switching off the cell selected relying on the load prediction. According to the experiment results in the real wireless environment, the ML based ES scheme can reduce more power consumption than the conventional ES scheme without load prediction.