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.

To enhance the video quality after encoding and decoding in video compression, a video quality enhancement framework is proposed based on local and non-local priors in this paper. Low-level features are first extracted through a single convolution layer and then processed by several conv-tran blocks (CTB) to extract high-level features, which are ultimately transformed into a residual image. The final reconstructed video frame is obtained by performing an element-wise addition of the residual image and the original lossy video frame. Experiments show that the proposed Conv-Tran Network (CTN) model effectively recovers the quality loss caused by Versatile Video Coding (VVC) and further improves VVC's performance.

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.

In the new radio (NR) access technology, non-terrestrial networks (NTN) are introduced to meet the requirement of anywhere and anytime connections from the world market. With the introduction of NTN, the NR system is able to offer the wide-area coverage and ensure the service availability for users. In this paper, the general aspects of NTN are introduced, including the NTN architecture overview, the impact of NTN on next-generation radio access network (NG-RAN) interface functions, mobility scenarios and other NTN related issues. The current progress in 3GPP Release 17 is also provided.

Quality of Experience (QoE) is used to monitor the user experience of telecommunication services, which has been studied for a long time. In universal terrestrial radio access network (UTRAN), evolved UTRAN (E-UTRA) and Long Term Evolution (LTE), QoE has also been specified for the improvement of user experience. The 5G New Radio (NR) technology is designed for providing various types of new services, and therefore operators have strong demand to continuously upgrade the 5G network to provide sufficient and good QoE for corresponding services. With new emerging 5G services, 5G QoE management collection aims at specifying the mechanism to collect the experience parameters for the multimedia telephony service for IP multimedia subsystem (IMS), multimedia broadcast and multicast service (MBMS), virtual reality (VR), etc. Taking LTE QoE as a baseline, generic NR QoE management mechanisms for activation, deactivation, configuration, and reporting of QoE measurement are introduced in this paper. Additionally, some enhanced QoE features in NR are discussed, such as radio access network (RAN) overload handling, RAN-visible QoE, per-slice QoE measurement, radio-related measurement, and QoE continuity for mobility. This paper also introduces solutions to NR QoE, which concludes the progress of NR QoE in the 3rd Generation Partnership Project (3GPP).

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.

Self-organizing network (SON) and minimization of driver tests (MDT) are functions designed for Long Term Evolution (LTE) system. SON is designed for network deployment by automatic configuration. MDT is designed for network performance evaluation by automatic signalling procedure. However, these functions do not support new features in new radio (NR) access technology, e.g., multiple radio access technology (RAT)-dual connectivity (MR-DC), central unit-distribute unit (CU-DU) split architecture, beam, etc. Therefore, how to support these features is a challenge for the industry. This paper provides analysis for these problems and provides the summary of SON/MDT functions progress in 3GPP. The analysis includes sub functions such as inter/intra system mobility robustness enhancement, inter/intra system mobility load balance, measurement qualities and mechanism of MDT, energy saving mechanism and procedure, RACH procedure optimization, PCI selection optimization, coverage and capacity optimization, and quality of service (QoS) monitoring mechanism. In addition, this paper also provides an initial thought on artificial intelligence (AI) algorithms applied to SON/MDT functions in NR, so called Smart Grid.

The 5G radio access network (RAN) architecture is supposed to be split into the central unit (CU) and the distributed unit (DU) in order to support more flexible transport networks and provide enhanced user experience. However, such functional split may also introduce some new technical issues. In this paper, we study the data fast retransmission issue introduced by this functional split in different scenarios and solutions are provided to handle this issue. With the fast data retransmission mechanism proposed in this paper, the retransmitted data packets could be identified and handled with high priority. In this way, the data delivery between the CU and DU in 5G RAN is assured.

In new radio (NR) access technology, the radio access network (RAN) architecture is split into two kinds of entities, i.e., the centralized unit (CU) and the distributed unit (DU), to enhance the network flexibility. In this split architecture, one CU is able to control several DUs, which enables the function of base-band central control and remote service for users. In this paper, the general aspects of CU-DU split architecture are introduced, including the split method, interface functions (control plane functions and user plane functions), mobility scenarios and other CU-DU related issues. The simulations show the performance of Options 2 and 3 for CU-DU split.