## 2 Evolution of AI-Driven 6G Network Architecture
### RAN Architecture Evolution  
As the network edge, the RAN is closer to end users, enabling the provision of more real-time services. Therefore, the evolution of RAN architecture upon the introduction of AI is critical to AI-driven communication systems. Both Open RAN (O-RAN) and virtualized RAN (vRAN) leverage virtualization and cloud-native technologies, adopting open and modular designs to enhance network flexibility and reduce deployment costs. vRAN primarily focuses on using general-purpose hardware and virtualization, while O-RAN emphasizes open and standardized interfaces. O-RAN introduces near-real-time RAN Intelligent Controllers (RIC) and non-real-time RIC to enable intelligent functionalities within the RAN. However, discussions at this stage have largely centered on applying AI to optimize RAN performance, often referred to as AI for RAN. With the establishment of the AI RAN Alliance, the scope of AI and RAN integration has expanded significantly. Beyond AI for RAN, new models include AI on RAN, which explores leveraging RAN capabilities to support edge AI applications, and AI and RAN, which focuses on the infrastructure-level integration of AI and RAN. These developments highlight the increasing role of RAN as a critical enabler in the AI-driven communication ecosystem, paving the way for more intelligent services at the network edge. It appears that adopting general-purpose computing (GPC) platforms and cloud-based solutions is an important direction for the evolution of the RAN to natively support AI capabilities. However, compared to dedicated hardware, GPC platforms still face many challenges. As shown in Table 1, GPC platforms use transparent protocols and decouple software from hardware, offering better openness [20; 21]. However, replacing dedicated baseband processing units with a general-purpose processor results in significant shortcomings in performance and energy efficiency. Dedicated hardware, on the other hand, has low energy consumption and high technological maturity, making it more suitable for current network requirements. However, it lacks openness and flexibility and is heavily dependent on specific vendors. The next-generation RAN is likely to continue adopting the architecture composed of the Centralized Unit (CU), Distributed Unit (DU), and Radio Unit (RU). Which part of the \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  & **Dedicated Hardware** & **GPC** \\ \hline Performance & Highly optimized & Relatively high \\ \hline Latency & Ultra-low latency & Higher latency due to virtualization and software stack limitation \\ \hline Energy Efficiency & Higher—Optimized hardware consumes less power for the same workload & Lower—Higher power consumption due to software-based processing overhead \\ \hline Advantages & High reliability and stability & Flexible deployment, ideal for dynamic environments, lower cost \\ \hline Disadvantages & Long development cycles, higher costs, limited flexibility & Inferior performance and power efficiency, higher latency \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between GPC and dedicated hardware. functions needs to adopt a cloud-based architecture depends on the demand for computing resources and deployment density. Compared to in 5G networks, the deployment of DUs will become denser in 6G networks, and the demand for computing resources is not high. DUs will mainly perform tasks with high real-time requirements but low complexity. Therefore, DUs based on dedicated hardware are more cost effective. On the other hand, CUs are deployed in a more centralized manner and have higher demands for computing resources. By cloudifying CUs, it is possible to better support AI applications at the network edge and improve the flexibility of the RAN. Since AI heavily relies on data and computing resources, the openness of computing resources and data will profoundly influence the scope and impact of AI applications in next-generation networks.