## 4 Use Cases Analysis
### Embodied AI  
Embodied intelligence, a critical component of future intelligent systems, currently relies heavily on terminal-based intelligence. This dependency results in high device costs, limited AI capabilities, and suboptimal energy efficiency, significantly constraining user experience. The integration of AI with RAN introduces a transformative paradigm, enabling a redistribution of intelligence with cloud-edge-terminal collaboration. By offloading general-purpose AI functionalities to the RAN, this approach reduces terminal complexity and cost while enhancing overall user experience and scalability.  
As shown in Figure 6, terminal AI focuses on executing latency-sensitive and context-aware tasks, such as real-time sensor data processing, localized decision-making, and direct user interaction. RAN AI, on the other hand, handles computationally intensive yet generalized functions, including multi-device coordination, mobility management, and adaptive resource allocation, leveraging its proximity to end users for improved responsiveness. Cloud AI, positioned at the top of the hierarchy, performs high-level functions such as model training, large-scale data analysis, and global optimization, ensuring system-wide intelligence enhancement and sustainability. This hierarchical collaboration enhances energy efficiency at the terminal level, enables dynamic adaptability and scalability through RAN-based intelligence, and ensures continuous learning and improvement via cloud integration. Together, these innovations establish a robust framework for supporting intelligent and cost-effective embodied systems in future AI-driven networks.  
Figure 7 illustrates an example of an AI-driven network for robotic guide dogs, demonstrating how cloud-edge-terminal AI collaborates to enhance intelligent assistance services for robotic guide dogs. Approximately 43 million individuals are blind globally; however, only about 22,000 guide dogs are actively employed to assist this population. Robotic guide dogs, enhanced by AI technologies, present a promising alternative. These robotic systems, typically powered by embedded units such as Raspberry Pi or Jetson, handle tasks like obstacle detection and path planning. However, these systems are constrained by limited  
Figure 6: Cloud–edge–device collaboration for embodied intelligence.  
computing resources and insufficient adaptability to dynamic environments. The AI-native RAN offers a possible solution by offloading intensive AI processing tasks to distributed RAN nodes. This approach not only reduces energy consumption and computing pressure on terminals, but also addresses the social challenges of providing more accessible assistance to persons with disabilities. Without the AI-native RAN, all AI capabilities are concentrated on the terminal, requiring it to handle sensing, decision-making, path planning, and human-machine interaction independently. This imposes extremely high requirements on the device's computing capacity and performance. Compared to terminals, the RAN can perceive and acquire environmental information on a larger scale. With the assistance of RAN AI, it can execute AI tasks such as path planning, incident detection, and localization, and provide smarter and safer services for visually impaired individuals.