## 2 Evolution of AI-Driven 6G Network Architecture
### E2E AI-Driven Network Framework  
In the 6G era, AI capabilities will not be limited to the RAN but will also be deployed across the CN, network management systems, the cloud, and even terminals. Therefore, an E2E framework designed to coordinate AI functionalities across different domains is another key requirement for AI-driven 6G technology. Figure 3 presents a reference framework for this E2E AI integration. The network infrastructure consists of GPC platforms and dedicated hardware, including radio-frequency devices, storage devices, computing devices (e.g., Central Processing Units (CPUs), Graphics Processing Units (GPUs)), and transmission equipment. General-purpose hardware can leverage virtualization technologies to construct virtual resource pools, enabling the deployment of network functions across different domains on top of the virtualization layer. However, some network functions, such as DUs and RUs, will continue to rely on dedicated hardware to meet specific performance requirements. The cloud-network functional domains encompass the cloud, RAN, CN, and network management systems, each embedded with AI capabilities. In the 3 GPP framework, the CN introduces a new network function called the Network Data Analytics Function (NWDAF) [12] to enable data analytics. On the RAN side, AI functionalities are implemented through gNB [9], while Operations, Administration, and Maintenance (OAM) systems are responsible for AI management and utilize the Management Data Analytics Function (MDAF) for management data analysis [11]. The O-RAN introduces additional network functions, such as near-real-time RIC and non-real-time RIC, to enable AI functionalities in the RAN domain. Looking ahead to the evolution of networks, it is anticipated that, in the 6G era, components such as the CU control plane, DU, and certain CN functions will natively integrate AI capabilities. This end-to-end integration will enhance network intelligence, providing a unified platform for efficient and adaptive operation. Figure 3: AI-driven E2E next-generation framework. In addition to AI integration, digital twin technology is likely to become a critical feature of 6G networks. By enabling real-time monitoring and modeling of network environments, topologies, resources, and services, the digital twin domain provides an ideal testing environment for the execution of AI strategies and the realization of autonomous network operations. One significant challenge of applying AI in networks is its inherent lack of explainability [22]. Directly executing AI-driven strategies poses unforeseen risks to network performance and stability. By leveraging digital twin technology to pre-validate AI strategies, these potential risks can be mitigated. This capability makes digital twin technology an indispensable tool for enhancing the reliability and robustness of AI-enabled 6G networks, thereby facilitating efficient network management and operation [23]. To enable the coordination of AI capabilities across different network domains, this paper further proposes the concept of the AI Layer. The AI Layer logically connects AI functionalities distributed across the network and is responsible for AI lifecycle management and orchestration. It ensures the seamless integration of AI-related processes across the cloud, CN, RAN, and terminals, providing a unified capability for managing AI resources and tasks. The AI Layer oversees the lifecycle management of data, models, and resources. For data, it constructs flexible pipelines and datasets tailored to the requirements of training and inference tasks, covering data collection, processing, classification, storage, and usage. For models, it manages training, validation, testing, deployment, and inference, while also supporting advanced operations such as splitting and merging for large language models (LLMs).