Large Language Model Empowered Next-Generation MIMO Networks: Fundamentals, Challenges, and Visions

TL;DR

This paper explores how Large Language Models can empower next-generation MIMO networks by enabling intelligent content generation, performance analysis, and resource management, addressing key challenges and proposing innovative AI-driven solutions.

Contribution

It introduces the concept of generative AI agents for MIMO networks, integrating LLMs with RAG for tailored content generation and system optimization, a novel approach in this domain.

Findings

Generative AI agents improve performance analysis in complex MIMO scenarios.

LLM-enabled frameworks enhance signal processing and resource allocation.

Case studies demonstrate significant benefits of AI integration in MIMO systems.

Abstract

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.