Semantic MIMO: Revisiting Linear Precoding in the Generative AI Era
arXiv:2604.01409v1 Announce Type: new 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. T
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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.
Subjects:
Signal Processing (eess.SP)
Cite as: arXiv:2604.01409 [eess.SP]
(or arXiv:2604.01409v1 [eess.SP] for this version)
https://doi.org/10.48550/arXiv.2604.01409
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Chunmei Xu [view email] [v1] Wed, 1 Apr 2026 21:15:40 UTC (14,368 KB)
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A day has passed which is a decade in the ai world - is qwen 3.5 27b q6 still the best model to run on a 5090, or does the new bonsai and gemma models beat it?
Im specifically interested in coding ability. I have the q6 version of the claude opus 4.6 distill with 128k context for local coding (Still using claude opus for planning) and it works amazingly. Im a tech junkie, good enough is never good enough, are these new models better? submitted by /u/ArugulaAnnual1765 [link] [comments]
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