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

TL;DR

This paper investigates LLM-enabled XL-MIMO design by introducing a generative AI agent that combines Large Language Models with Retrieval Augmented Generation to produce tailored content for performance analysis, signal processing, and resource allocation. It surveys the development and challenges of next-generation MIMO (XL-MIMO) and presents a concrete framework for leveraging GAI to assist modeling, optimization, and evaluation in complex MIMO configurations. Two case studies demonstrate how the generative AI agent can formulate optimization problems, extract relevant constraints, and yield insights such as capacity trends with transceiver rotation and EDoF-based shaping. The work highlights the potential of AI-assisted design to improve efficiency, reduce modeling errors, and guide future research toward Explainable AI, persistent memory, and digital-twin validation.

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.

Figures (4)

• Figure 1: Representative characteristics and application scenarios for next-generation MIMO.
• Figure 2: Overview of generative AI agent: definition, feature, advantage of generative AI agent, and life cycle of generative AI agent empowered next-generation MIMO networks.
• Figure 3: Generative AI agent assisted capacity maximization for non-parallel UPA-based XL-MIMO systems. One square transmitting UPA surface and one square receiving UPA surface with similar physical sizes $10\lambda \times 10\lambda$ are considered, where the point antennas are uniformly distributed along the surfaces and $\lambda$ is the wavelength. The transmitting distances between the center point of the transmitter and the center point of the receiver for (a) and (b) are $30\lambda$ and $4\lambda$, respectively. And the Signal-to-Noise Ratio (SNR) $P/N_0=10 \ \mathrm{dB}$.
• Figure 4: Generative AI agent assisted EDoF maximization for rectangular UPA-based XL-MIMO systems with various shapes. We have $L=12\lambda$ and transmitting distance $d=10\lambda$.