CSI Compression for Massive MIMO-OFDM: Mismatch-Aware Rate-Distortion Trade-offs
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866908978221416448 |
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| author | Park, Bumsu Park, Youngmok Park, Chanho Lee, Namyoon |
| author_facet | Park, Bumsu Park, Youngmok Park, Chanho Lee, Namyoon |
| contents | We study channel state information (CSI) compression for wideband frequency division duplex massive multiple-input multiple-output (MIMO) when the base station (BS) reconstructs CSI using an imperfect covariance model. Under matched second-order statistics, remote rate--distortion theory yields transform coding with reverse water-filling (RWF) over covariance eigenmodes. With decoder-side covariance mismatch, however, this allocation is no longer end-to-end optimal. We derive an achievable mismatched Gaussian rate--distortion characterization based on a Gaussian test channel and a mismatched minimum mean square error (MMSE) reconstruction rule. In a shared-eigenvector regime (common eigenbasis, mismatched eigenvalues), the problem decouples across modes and leads to a robust reverse water-filling (RRWF) allocation computable via bisection and per-mode root finding. Simulations using wideband massive MIMO covariance models show that RRWF consistently improves reconstruction distortion and end-to-end mean square error relative to conventional RWF under mismatch. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_17426 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CSI Compression for Massive MIMO-OFDM: Mismatch-Aware Rate-Distortion Trade-offs Park, Bumsu Park, Youngmok Park, Chanho Lee, Namyoon Information Theory We study channel state information (CSI) compression for wideband frequency division duplex massive multiple-input multiple-output (MIMO) when the base station (BS) reconstructs CSI using an imperfect covariance model. Under matched second-order statistics, remote rate--distortion theory yields transform coding with reverse water-filling (RWF) over covariance eigenmodes. With decoder-side covariance mismatch, however, this allocation is no longer end-to-end optimal. We derive an achievable mismatched Gaussian rate--distortion characterization based on a Gaussian test channel and a mismatched minimum mean square error (MMSE) reconstruction rule. In a shared-eigenvector regime (common eigenbasis, mismatched eigenvalues), the problem decouples across modes and leads to a robust reverse water-filling (RRWF) allocation computable via bisection and per-mode root finding. Simulations using wideband massive MIMO covariance models show that RRWF consistently improves reconstruction distortion and end-to-end mean square error relative to conventional RWF under mismatch. |
| title | CSI Compression for Massive MIMO-OFDM: Mismatch-Aware Rate-Distortion Trade-offs |
| topic | Information Theory |
| url | https://arxiv.org/abs/2604.17426 |