## 4 Use Cases Analysis
### Generative AI Within 6G Networks  
With the rapid development of generative AI technologies, large language models (LLMs) and multimodal models have achieved remarkable success in natural language processing (NLP) and image and video generation. Introducing generative AI into communication systems has attracted significant attention from academia and the industry, particularly in areas such as intelligent operations and management and network automation. By leveraging the strong comprehension capabilities of LLMs, generative AI can enhance intent recognition and translation accuracy in intent-driven networks.  
Compared to traditional AI models, generative AI leverages massive parameter scales and extensive data training to improve understanding and generalization capabilities. Consequently, LLMs are typically deployed in data centers or cloud environments. Many companies provide flagship models alongside lightweight versions to cater to diverse user needs. Table 2 summarizes the capabilities and revenue models of several commonly used LLMs and multimodal models. For text-focused LLMs, the predominant pricing model involves token-based billing, where output tokens are priced higher than input tokens. For multimodal models, fees are often associated with converting images or videos into tokens, such as Gemini, or charged based on the number of images and video duration, as seen with Imagen3 and Text to Speech (TTS). Some text-to-image generation models, such as Stable-Diffusion-1.5, charge based on model units and usage duration.  
These business models provide valuable insights into integrating generative AI with communication systems. Deploying large models at central network nodes or lightweight models at the network edge, including the RAN, presents opportunities for the telecom industry to introduce new revenue opportunities. Beyond traditional data traffic income,  
Figure 7: Comparison: (**a**) Robotic guide dog without AI-native RAN. (**b**) Robotic guide dog with AI-native RAN.  
operators could monetize computational power, AI models, or tokens, creating innovative ways to enhance network profitability.  
Generative AI deployed within networks will inevitably fall short of meeting all network intelligence requirements. Therefore, the foundational intelligence provided by large models must collaborate with various small or specialized models to address the diverse intelligent tasks across different network domains [47,48]. Generative AI can act as a selector to match models with tasks based on the specific requirements of intelligent tasks and the capabilities of models deployed within the network. By understanding the task's complexity and resource constraints, large models can allocate appropriate AI models and computational resources to ensure efficient task execution. Conversely, generative AI can also serve as a global decision-maker for small models. In this role, it aggregates the outputs from specialized or small models deployed across different domains and integrates them to provide comprehensive analysis or overarching decisions. This two-way collaboration ensures that generative AI and specialized models work together seamlessly, supporting the diverse and complex scenarios required by intelligent 6G networks.