Blind MIMO Semantic Communication

In this paper, we propose a novel blind multi-input multi-output (MIMO) semantic communication (SC) framework named Blind-MIMOSC that consists of a deep joint source-channel coding (DJSCC) transmitter and a diffusion-based blind receiver. The DJSCC transmitter encodes source data into the transmitted signal, while the blind receiver employs a parallel variational diffusion (PVD) model to simultaneously recover the channel and the source data from the received signal without using any pilots. This work presents the first fully pilot-free solution for joint channel estimation and source recovery in block-fading MIMO systems. Extensive experiments show that Blind-MIMOSC with PVD significantly improves channel and source recovery accuracy compared to state-of-the-art approaches, with drastically reduced channel bandwidth ratio (defined as the ratio of the channel input symbol count to source data dimensions).

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Publication Details

Published
2025-08-10
DOI
https://doi.org/10.1109/iccc65529.2025.11148632
Primary Topic
Wireless Signal Modulation Classification
Type
article
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article

Blind MIMO Semantic Communication

Qinghua Guo, Xiaojun Yuan, Yinuo Huang, Hao Jiang
Wireless Signal Modulation Classification
article

Blind MIMO Semantic Communication

Qinghua Guo, Xiaojun Yuan, Yinuo Huang, Hao Jiang
article en

Abstract

In this paper, we propose a novel blind multi-input multi-output (MIMO) semantic communication (SC) framework named Blind-MIMOSC that consists of a deep joint source-channel coding (DJSCC) transmitter and a diffusion-based blind receiver. The DJSCC transmitter encodes source data into the transmitted signal, while the blind receiver employs a parallel variational diffusion (PVD) model to simultaneously recover the channel and the source data from the received signal without using any pilots. This work presents the first fully pilot-free solution for joint channel estimation and source recovery in block-fading MIMO systems. Extensive experiments show that Blind-MIMOSC with PVD significantly improves channel and source recovery accuracy compared to state-of-the-art approaches, with drastically reduced channel bandwidth ratio (defined as the ratio of the channel input symbol count to source data dimensions).

University of Electronic Science and Technology of China (CN), University of Wollongong (AU)
Openalex Percentile: Top 87%
Wireless Signal Modulation Classification
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Blind MIMO Semantic Communication — Qinghua Guo, Xiaojun Yuan, et al. · (2025) | TGRS Research Map | TGRS