{"ID":2848290,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.27043","arxiv_id":"2510.27043","title":"Blind MIMO Semantic Communication via Parallel Variational Diffusion: A Completely Pilot-Free Approach","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 aims to compress and map the source data into the transmitted signal by exploiting the structural characteristics of the source data, while the diffusion-based 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. The PVD model leverages two pre-trained score networks to characterize the prior information of the channel and the source data, operating in a plug-and-play manner during inference. This design allows only the affected network to be retrained when channel conditions or source datasets change, avoiding the complicated full-network retraining required by end-to-end methods. 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 achieves superior channel and source recovery accuracy compared to state-of-the-art approaches, with drastically reduced channel bandwidth ratio.","short_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 aims to compress and map the source data into the transmitt...","url_abs":"https://arxiv.org/abs/2510.27043","url_pdf":"https://arxiv.org/pdf/2510.27043v1","authors":"[\"Hao Jiang\",\"Xiaojun Yuan\",\"Yinuo Huang\",\"Qinghua Guo\"]","published":"2025-10-30T23:14:28Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"Diffusion Model\"]","has_code":false}