Joint Channel Sounding and Source-Channel Coding for MIMO-OFDM Systems: Deep Unified Encoding and Parallel Flow-Matching Decoding
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | |
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| _version_ | 1866910016659783680 |
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| author | Jiang, Hao Yuan, Xiaojun Guo, Qinghua |
| author_facet | Jiang, Hao Yuan, Xiaojun Guo, Qinghua |
| contents | In this work, we propose a deep unified (DU) encoder that embeds source information in a codeword that contains sufficient redundancy to handle both channel and source uncertainties, without enforcing an explicit pilot-data separation. At the receiver, we design a parallel flow-matching (PFM) decoder that leverages flow-based generative priors to jointly estimate the channel and the source, yielding much more efficient inference than the existing diffusion-based approaches. To benchmark performance limits, we derive the Bayesian Cramér-Rao bound (BCRB) for the joint channel and source estimation problem. Extensive simulations over block-fading MIMO-OFDM channels demonstrate that the proposed DU-PFM approach drastically outperforms the state-of-the-art methods in both channel estimation accuracy and source reconstruction quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_08795 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Joint Channel Sounding and Source-Channel Coding for MIMO-OFDM Systems: Deep Unified Encoding and Parallel Flow-Matching Decoding Jiang, Hao Yuan, Xiaojun Guo, Qinghua Signal Processing In this work, we propose a deep unified (DU) encoder that embeds source information in a codeword that contains sufficient redundancy to handle both channel and source uncertainties, without enforcing an explicit pilot-data separation. At the receiver, we design a parallel flow-matching (PFM) decoder that leverages flow-based generative priors to jointly estimate the channel and the source, yielding much more efficient inference than the existing diffusion-based approaches. To benchmark performance limits, we derive the Bayesian Cramér-Rao bound (BCRB) for the joint channel and source estimation problem. Extensive simulations over block-fading MIMO-OFDM channels demonstrate that the proposed DU-PFM approach drastically outperforms the state-of-the-art methods in both channel estimation accuracy and source reconstruction quality. |
| title | Joint Channel Sounding and Source-Channel Coding for MIMO-OFDM Systems: Deep Unified Encoding and Parallel Flow-Matching Decoding |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2602.08795 |