Semantic MIMO: Revisiting Linear Precoding in the Generative AI Era

TL;DR

This paper analyzes linear precoding in semantic MIMO systems with generative AI, showing reduced sensitivity to interference and CSI errors, and demonstrating that match-filter precoding can perform comparably to zero-forcing.

Contribution

It provides a theoretical and simulation-based evaluation of semantic MIMO, revealing its advantages in interference tolerance and scalability over conventional systems.

Findings

MF achieves semantic performance similar to ZF with imperfect CSI

Semantic MIMO reduces need for interference mitigation and accurate CSI

Improves scalability with lower computational complexity

Abstract

This paper revisits linear precoding, namely match-filter (MF) and zero-forcing (ZF), in a semantic multiple-input multiple-output (MIMO) system empowered by generative AI. The aim is to examine whether interference, channel state information (CSI) accuracy, and scalability limitations in conventional MIMO systems remain critical. Theoretical analysis, which is based on the generative inference model and Lipschitz continuous assumptions, reveals reduced sensitivity to interference and channel imperfections, as well as performance inferiority in high-SINR regimes compared to conventional MIMO systems. Simulation results validate the analysis and show that MF achieves semantic performance comparable to ZF under both perfect and imperfect CSI. These findings suggest that semantic MIMO relaxes the needs for aggressive interference mitigation and highly accurate CSI, while improving scalability with reduced computational and implementation complexity.