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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2606.00263 |
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| _version_ | 1866916068517216256 |
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| author | Zhang, Zhibin Potekhin, Robert Wan, Ziwei Lyashev, Vladimir Gao, Zhen |
| author_facet | Zhang, Zhibin Potekhin, Robert Wan, Ziwei Lyashev, Vladimir Gao, Zhen |
| contents | This letter studies CSI denoising for MIMO--OFDM with variable NR resource block (RB) allocations. ReFLEX is a length-generalizable Transformer whose frequency attention uses a relative-frequency position bias (RFPB) generated from subcarrier offsets. A single checkpoint handles unseen RB lengths and can be applied to sparse DM-RS observations in the tested RB5/RB10 PUSCH setup without retraining. In a 3GPP~TR~38.901 UMa NLOS channel, ReFLEX achieves about $-9.6$~dB NMSE on unseen RB lengths. In NR PUSCH/UL-SCH simulations, ReFLEX denoising followed by time-frequency interpolation reduces the 10\% BLER threshold by about 2--3~dB. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_00263 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ReFLEX: Length-Generalizable CSI Denoising for MIMO-OFDM via Relative-Frequency Bias Zhang, Zhibin Potekhin, Robert Wan, Ziwei Lyashev, Vladimir Gao, Zhen Signal Processing Machine Learning 15A90 G.1.3 This letter studies CSI denoising for MIMO--OFDM with variable NR resource block (RB) allocations. ReFLEX is a length-generalizable Transformer whose frequency attention uses a relative-frequency position bias (RFPB) generated from subcarrier offsets. A single checkpoint handles unseen RB lengths and can be applied to sparse DM-RS observations in the tested RB5/RB10 PUSCH setup without retraining. In a 3GPP~TR~38.901 UMa NLOS channel, ReFLEX achieves about $-9.6$~dB NMSE on unseen RB lengths. In NR PUSCH/UL-SCH simulations, ReFLEX denoising followed by time-frequency interpolation reduces the 10\% BLER threshold by about 2--3~dB. |
| title | ReFLEX: Length-Generalizable CSI Denoising for MIMO-OFDM via Relative-Frequency Bias |
| topic | Signal Processing Machine Learning 15A90 G.1.3 |
| url | https://arxiv.org/abs/2606.00263 |