From Specialist to Large Models: A Paradigm Evolution Towards Semantic-Aware MIMO

2026-02-25

AI Summary

This work addresses the challenges faced by 6G massive MIMO systems, including high physical-layer overhead, excessive latency, and the demands of immersive communication and environmental sensing that strain conventional signal processing. To overcome these limitations, the paper introduces a novel semantic-aware MIMO paradigm, which for the first time integrates semantic communication principles into MIMO design. By leveraging task-specific expert models and unifying multi-task modeling through large foundation models, the proposed framework enables joint semantic coordination between channel and source information. This approach significantly enhances key functionalities such as random access detection, channel feedback, and precoding, while simultaneously reducing signaling overhead and strengthening integrated sensing and communication capabilities. The results validate the effectiveness and scalability of semantics-driven physical-layer design.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Agent Communication
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

The sixth generation (6G) network is expected to deploy larger multiple-input multiple-output (MIMO) arrays to support massive connectivity, which will increase overhead and latency at the physical layer. Meanwhile, emerging 6G demands such as immersive communications and environmental sensing pose challenges to traditional signal processing. To address these issues, we propose the ``semantic-aware MIMO'' paradigm, which leverages specialist models and large models to perceive, utilize, and fuse the inherent semantics of channels and sources for improved performance. Moreover, for representative MIMO physical-layer tasks, e.g., random access activity detection, channel feedback, and precoding, we design specialist models that exploit channel and source semantics for better performance. Additionally, in view of the more diversified functions of 6G MIMO, we further explore large models as a scalable solution for multi-task semantic-aware MIMO and review recent advances along with their advantages and limitations. Finally, we discuss the challenges, insights, and prospects of the evolution of specialist models and large models empowered semantic-aware MIMO paradigms.

Problem

• 6G
• MIMO
• semantic-aware
• physical layer
• signal processing

Innovation

• semantic-aware MIMO
• specialist models
• large models
• 6G networks
• semantic communication