Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.07169 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911428692148224 |
|---|---|
| author | Haigh, Paul Anthony |
| author_facet | Haigh, Paul Anthony |
| contents | This paper proposes a neural-network-assisted deformable matched filtering framework for carrier-less amplitude and phase (CAP) modulation operating under bandwidth-limited channel conditions. Instead of replacing the analytically derived CAP matched filter, the proposed receiver learns a residual deformation of the nominal matched filter based on a compact set of physically motivated signal features extracted from the received waveform. A total of 16 time-domain, frequency-domain, and memory-related features are used to provide a low-dimensional representation of bandwidth-induced pulse distortion. These features are mapped by a fully connected neural network to complex-valued matched filter coefficients, enabling adaptive pulse-shape compensation prior to symbol-rate sampling. The network is trained end-to-end using a differentiable loss function based on error vector magnitude (EVM). Experimental results obtained using a hardware-in-the-loop CAP transmission system demonstrate that the proposed deformable matched filter significantly outperforms conventional fixed matched filtering under severe bandwidth constraints, without requiring decision feedback or increasing receiver latency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_07169 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ML-Enabled Deformable Matched Filters for Bandlimitation Compensation in Free-Space Optics Haigh, Paul Anthony Signal Processing This paper proposes a neural-network-assisted deformable matched filtering framework for carrier-less amplitude and phase (CAP) modulation operating under bandwidth-limited channel conditions. Instead of replacing the analytically derived CAP matched filter, the proposed receiver learns a residual deformation of the nominal matched filter based on a compact set of physically motivated signal features extracted from the received waveform. A total of 16 time-domain, frequency-domain, and memory-related features are used to provide a low-dimensional representation of bandwidth-induced pulse distortion. These features are mapped by a fully connected neural network to complex-valued matched filter coefficients, enabling adaptive pulse-shape compensation prior to symbol-rate sampling. The network is trained end-to-end using a differentiable loss function based on error vector magnitude (EVM). Experimental results obtained using a hardware-in-the-loop CAP transmission system demonstrate that the proposed deformable matched filter significantly outperforms conventional fixed matched filtering under severe bandwidth constraints, without requiring decision feedback or increasing receiver latency. |
| title | ML-Enabled Deformable Matched Filters for Bandlimitation Compensation in Free-Space Optics |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2602.07169 |