## 4 Use Cases Analysis
### AI for Wearable Devices  
With the rapid growth of AI technology and edge computing, network intelligence is gradually moving towards the edge, giving rise to AI-enabled wearable devices. 'Wearable devices' typically refers to electronic devices worn on the body, which are employed to monitor and transmit collected information about consumer behaviors and activities in an environment. Wearable devices often integrate various sensors, such as temperature sensors, accelerometers, gyroscopes, and heart rate sensors, to collect personal data and improve daily life. As shown in Figure 5, the base station collects data from the wearable devices, which may encompass earphones, watches, glasses, and footwear. Empowered by AI chips embedded at the base station, user terminals will be provided with real-time intelligent analysis, such as route advice on avoiding a traffic accident ahead. In the smart wearable device use case, the advantages of the proposed AI-native RAN mainly lie in two aspects. First, traditional wearable devices are equipped with sensors and cameras to collect data, and the RAN only provides connectivity to route data to the cloud for processing. In contrast, with the AI-native RAN, AI models deployed within the RAN itself can process RAN-side data and extend the environmental perception function of wearable devices beyond what the onboard sensors can capture. Furthermore, different from the conventional edge computing technique which allocates computation tasks to the MEC, the AI-native RAN can directly process the data at the base station without UPF-based data offloading. Thus, the transmission latency can be saved and the real-time AI service efficiency can be significantly improved. In the 6G era, smart wearables will be further accelerated by the ultra-low-latency service provided by 6G networks, which is important for some real-time applications such as medical equipment and health monitoring. Moreover, the high data speed of 6G networks can give the users immersive experiences with the help of smart wearable devices, such as smart glasses, which facilitate AR/VR applications. Additionally, as a key feature of 6G networks, holographic communication could revolutionize the way wearable devicesare used for communication, and enable real-time interaction such as immersive virtual meetings and real-time holographic displays. The recent literature on AI-driven wearable devices has been devoted to utilizing AI technology to enhance the reliability, accuracy, and efficiency of wearable devices. Convolutional neural networks have demonstrated significant advantages in human activity recognition and health monitoring, providing real-time health indicator analysis and improving recognition accuracy. Moreover, recurrent neural networks (RNNs) are employed in processing time series data and handle long-term dependencies through their memory mechanisms, making them suitable for analyzing continuous motion data collected by accelerometers and gyroscopes. Further, as the data captured by sensors may come from different sources, including vision, sound, motion, physiological signals, multimodal characteristics have shown great potentiality for developing AI algorithms. AI-driven wearable devices integrate data from multiple modalities and leverage the complementarity of multimodal data to enhance perception and interaction capabilities. We take smart glasses as an example to illustrate the coordination of AI models in the terminal, edge, and cloud domain based on our proposed framework. The smart glasses first collect real-time data, including images, texts, audios, and posture data, and upload the data to the edge server. The user can select the AI services on the smart glasses. The edge server, typically deployed near the CU, employs tiny AI models to process the data and execute real-time tasks such as image recognition and text translation. The AI Layer serves as the service orchestrator for multiple AI services from different users.