CSI Feedback Under Basis Mismatch: Rate-Splitting Transform Coding for FDD Massive MIMO
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866911614211457024 |
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| author | Park, Youngmok Park, Bumsu Lee, Namyoon |
| author_facet | Park, Youngmok Park, Bumsu Lee, Namyoon |
| contents | In frequency division duplex massive multiple-input multiple-output systems, downlink channel state information must be fed back within a limited uplink budget. While transform coding with Karhunen-Loeve transform and reverse water-filling is rate-distortion optimal for Gaussian channels, its performance is limited by basis mismatch between the user and base station. We analyze this mismatch and propose a practical architecture separating long-term basis feedback from short-term coefficient quantization. Using a random vector quantization, we derive a closed-form end-to-end mean square error expression. This allows us to characterize the optimal rate split and identify a phase transition threshold for basis updates. Simulations on correlated Gaussian and COST2100 channels demonstrate near-optimal performance, robustness to update overhead, and significant complexity reduction compared to deep-learning-based autoencoders. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_20380 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CSI Feedback Under Basis Mismatch: Rate-Splitting Transform Coding for FDD Massive MIMO Park, Youngmok Park, Bumsu Lee, Namyoon Information Theory Signal Processing In frequency division duplex massive multiple-input multiple-output systems, downlink channel state information must be fed back within a limited uplink budget. While transform coding with Karhunen-Loeve transform and reverse water-filling is rate-distortion optimal for Gaussian channels, its performance is limited by basis mismatch between the user and base station. We analyze this mismatch and propose a practical architecture separating long-term basis feedback from short-term coefficient quantization. Using a random vector quantization, we derive a closed-form end-to-end mean square error expression. This allows us to characterize the optimal rate split and identify a phase transition threshold for basis updates. Simulations on correlated Gaussian and COST2100 channels demonstrate near-optimal performance, robustness to update overhead, and significant complexity reduction compared to deep-learning-based autoencoders. |
| title | CSI Feedback Under Basis Mismatch: Rate-Splitting Transform Coding for FDD Massive MIMO |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2604.20380 |