Large Language Model Empowered Next-Generation MIMO Networks: Fundamentals, Challenges, and Visions
This work addresses the need for more adaptive and efficient MIMO systems in telecommunications, though it appears incremental as it builds on existing LLM and RAG techniques.
The paper tackles the challenge of making next-generation MIMO networks intelligent and scalable by proposing a generative AI agent framework that leverages LLMs and RAG to enhance performance analysis, signal processing, and resource allocation, with case studies demonstrating its effectiveness in complex scenarios.
Next-generation Multiple-Input Multiple-Output (MIMO) is expected to be intelligent and scalable. In this paper, we study Large Language Model (LLM)-enabled next-generation MIMO networks. Firstly, we provide an overview of the development, fundamentals, and challenges of the next-generation MIMO. Then, we propose the concept of the generative AI agent, which is capable of generating tailored and specialized contents with the aid of LLM and Retrieval Augmented Generation (RAG). Next, we comprehensively discuss the features and advantages of the generative AI agent framework. More importantly, to tackle existing challenges of next-generation MIMO, we discuss generative AI agent-enabled next-generation MIMO networks from the perspective of performance analysis, signal processing, and resource allocation. Furthermore, we present two compelling case studies that demonstrate the effectiveness of leveraging the generative AI agent for performance analysis in complex configuration scenarios. These examples highlight how the integration of generative AI agents can significantly enhance the analysis and design of next-generation MIMO systems. Finally, we discuss important potential research future directions.