Multi-Snapshot Deep Denoising for Channel Estimation in OTFS Modulated Systems
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911727448227840 |
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| author | Gehlot, Surbhi Shinde, Siddhi Srivastava, Suraj Yadav, Sandeep Kumar |
| author_facet | Gehlot, Surbhi Shinde, Siddhi Srivastava, Suraj Yadav, Sandeep Kumar |
| contents | A deep denoising based channel estimation framework is proposed for orthogonal time frequency space (OTFS) modulated systems, wherein channel state information (CSI) recovery is formulated as an image restoration problem. A salient attribute of the approach is the exploitation of structural invariance in the delay Doppler (DD) domain channel over a geometric coherence time, allowing multiple OTFS frames captured during this period to serve as noisy snapshots of the approximately identical channel. These snapshots jointly enhance the effectiveness of the proposed lightweight denoiser based on nonlinear activation free network (NAFNet). The method exhibits low computational complexity, operates reliably even at low pilot signal-to-noise ratio (PSNR), and can accommodate both fractional delay and fractional Doppler effects. Simulation results demonstrate significant performance gains over the existing methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_29777 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Multi-Snapshot Deep Denoising for Channel Estimation in OTFS Modulated Systems Gehlot, Surbhi Shinde, Siddhi Srivastava, Suraj Yadav, Sandeep Kumar Signal Processing A deep denoising based channel estimation framework is proposed for orthogonal time frequency space (OTFS) modulated systems, wherein channel state information (CSI) recovery is formulated as an image restoration problem. A salient attribute of the approach is the exploitation of structural invariance in the delay Doppler (DD) domain channel over a geometric coherence time, allowing multiple OTFS frames captured during this period to serve as noisy snapshots of the approximately identical channel. These snapshots jointly enhance the effectiveness of the proposed lightweight denoiser based on nonlinear activation free network (NAFNet). The method exhibits low computational complexity, operates reliably even at low pilot signal-to-noise ratio (PSNR), and can accommodate both fractional delay and fractional Doppler effects. Simulation results demonstrate significant performance gains over the existing methods. |
| title | Multi-Snapshot Deep Denoising for Channel Estimation in OTFS Modulated Systems |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2605.29777 |