3 papers
eess.SP2026
Learning to deform the matched filter
Paul Anthony Haigh
Analytical signal-processing blocks are interpretable and reliable, but their optimality depends on assumptions that practical hardware and channels violate. Learned replacements c…
eess.SP2026
Explainable deformable matched filtering reveals measurable departures from classical receiver theory in optical wireless communications
Paul Anthony Haigh
Matched filtering is a central result of communication theory, providing the optimal linear receiver when the received waveform satisfies specific assumptions. Practical communicat…
eess.SP2026
ML-Enabled Deformable Matched Filters for Band-Limit Compensation in Free-Space Optics
Paul Anthony Haigh
This paper proposes a neural-network-assisted deformable matched filtering (DMF) framework for carrier-less amplitude and phase (CAP) modulation operating under bandwidth-limited c…