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Semantic MIMO: Revisiting Linear Precoding in the Generative AI Era

Chunmei Xu, Yi Ma, Rahim Tafazolli

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.

Semantic MIMO: Revisiting Linear Precoding in the Generative AI Era

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.

Paper Structure

This paper contains 14 sections, 25 equations, 4 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. Channel Model
  4. Signal Model
  5. Generative Inference Model
  6. Linear Precoding Revisit
  7. MF Precoding
  8. ZF Precoding
  9. Theoretical Analysis
  10. SINR Dependence
  11. High-SINR Inferiority
  12. Simulation Results and Discussions
  13. Key Findings and Design Insights
  14. Conclusion

Figures (4)

  • Figure 1: The system model of the multi-user semantic MIMO system.
  • Figure 2: Perfect CSI case: Semantic performance comparison across different transmit SNR per data stream.
  • Figure 3: Imperfect CSI case: Semantic performance comparisons between MF and ZF precoding techniques, where $\mathrm{SNR}=15$ dB.
  • Figure 4: Visual illustration of reconstructions under (a) perfect CSI case, and (b) imperfect CSI case.