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

TL;DR

This paper introduces a semantic-aware MIMO paradigm for 6G networks that leverages specialist and large models to enhance performance by utilizing channel and source semantics, addressing challenges of increased overhead and diverse 6G applications.

Contribution

It proposes a novel semantic-aware MIMO framework that integrates specialist and large models for improved physical-layer tasks and multi-task scalability in 6G networks.

Findings

Specialist models exploiting semantics improve task performance.

Large models enable scalable multi-task semantic processing.

Discussion of challenges and future prospects for semantic-aware MIMO.

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.