Generative AI Agent for Next-Generation MIMO Design: Fundamentals,
  Challenges, and Vision
Zhe Wang Jiayi Zhang Hongyang Du Ruichen Zhang Dusit Niyato Bo Ai Khaled B. Letaief
Next-generation multiple input multiple output (MIMO) is expected to be
intelligent and scalable. In this paper, we study generative artificial
intelligence (AI) agent-enabled next-generation MIMO design. 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 large language model (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 design, 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.