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Paul Anthony Haigh

Publications and source records attributed to Paul Anthony Haigh.

8 recordsLinked to original sources
arXiv2026

Towards a Connected Heterogeneous All-Medium Integrated Network (CHAIN) for Converged Connectivity Across Land, Sea, Air, and Space

Next-generation connectivity depends on data traversing multiple physical media within a single end-to-end path, yet research in optical fibre, free-space optical and radio wireless, non-terrestrial networks, and underwater communications has advanced largely in isolation. This fragmentation has a wider adverse impact on communication performance, deployment and adaption as the most demanding open problems in next-generation connectivity, cross-medium channel characterisation, transport-layer protocol design across heterogeneous latency regimes, and cross-domain orchestration, sit at the boundaries between domains and can only be addressed by bringing these challenges together in cross-domain systems. Space-air-ground integrated network research has begun to treat three domains analytically, and its recent extension to the sea surface represents the most ambitious multi-domain framework proposed to date, yet neither has produced experimental results, a deployed system, or a standards engagement. The subsurface and maritime domain remains absent from both. This paper proposes the connected heterogeneous all-medium integrated network (CHAIN), a framework that treats the undersea, maritime, terrestrial, aerial and space domains as a single design space. We identify an all-photonic network backbone as the unifying physical infrastructure, Artificial intelligence (AI)-driven orchestration as the cross-domain control layer, and the joints between domains as a unification challenge. We survey the state of the art across all domains and the existing cross-domain literature, develop the CHAIN framework and its technical pillars, characterise the five core open problems the field must resolve, and set out a research roadmap from near-term measurement campaigns and testbed validation through cross-medium field trials to global-scale deployment.

cs.NI↗
arXiv2026

Explainable deformable matched filtering reveals measurable departures from classical receiver theory in optical wireless communications

Matched filtering is a central result of communication theory, providing the optimal linear receiver when the received waveform satisfies specific assumptions. Practical communication systems rarely satisfy these assumptions, yet learned receivers that outperform the classical matched filter provide little insight into what those improvements reveal about the limitations of the underlying theory. Here we introduce an explainable deformable matched-filter framework in which machine learning is constrained to learn a low-dimensional deformation of the classical matched filter rather than replacing it. Because every learned correction is defined relative to the theoretical matched-filter solution, the deformation becomes a measurable representation of receiver mismatch rather than an unconstrained optimisation. The communication waveform remains processed entirely by the matched filter, while a Kolmogorov-Arnold Network predicts only the deformation from physically interpretable receiver-state descriptors. Using an optical wireless communication testbed spanning ten signalling formats, four impairment classes and 1,600 conditions, we show that learned deformations improve receiver performance, yielding a median relative error-vector-magnitude reduction of 18.1%, while revealing departures from classical matched-filter optimality. Different signalling families occupy distinct deformation regimes, spectral analysis identifies the physical mechanisms underlying receiver mismatch, and latent receiver-state organisation demonstrates that these departures are structured rather than arbitrary.

eess.SP↗
arXiv2026

Learning to deform the matched filter

Analytical signal-processing blocks are interpretable and reliable, but their optimality depends on assumptions that practical hardware and channels violate. Learned replacements can adapt, but often discard the structure that makes the original solution understandable. Here we introduce deformable matched filtering, a platform in which learning steers a bounded deformation of an explicit matched filter instead of replacing the waveform-processing path. In a hardware-in-the-loop optical wireless link, blind state descriptors drive causal, pilotless updates while payload samples, transmitted references, and condition labels remain outside the controller. Receiver deformation generalises across held-out signalling and channel conditions and remains ahead of a span-matched fractionally spaced equaliser after stationary convergence in most tested regimes. A transferable transmitter deformation reduces error-vector magnitude in all 144 held-out evaluations, whereas its additional value after receiver adaptation emerges principally under severe combined distortion. KAN, MLP, and linear controllers provide different condition-dependent advantages within the same filter structure. During 24 hours of uninterrupted changing-condition operation, bounded deformable receivers recover their original operating regime after severe intervening distortion while the persistent conventional equaliser accumulates destructive state. These results establish deformable analytical filters as a reusable middle ground between fixed theory and end-to-end learned signal processing.

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arXiv2026

ML-Enabled Deformable Matched Filters for Band-Limit Compensation in Free-Space Optics

This paper proposes a neural-network-assisted deformable matched filtering (DMF) 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 DMF significantly outperforms conventional fixed matched filtering under severe bandwidth constraints, without requiring decision feedback or increasing receiver latency.

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arXiv2019

Experimental Demonstration of Staggered CAP Modulation for Low Bandwidth Red-Emitting Polymer-LED based Visible Light Communications

In this paper we experimentally demonstrate, for the first time, staggered carrier-less amplitude and phase (sCAP) modulation for visible light communication systems based on polymer light-emitting diodes emitting at ~639 nm. The key advantage offered by sCAP in comparison to conventional multiband CAP is its full use of the available spectrum. In this work, we compare sCAP, which utilises four orthogonal filters to generate the signal, with a conventional 4-band multi-CAP system and on-off keying (OOK). We transmit each modulation format with equal energy and present a record un-coded transmission speed of ~6 Mb/s. This represents gains of 25% and 65% over the achievable rate using 4-CAP and OOK, respectively.

cs.NI↗
arXiv2018

Non-Orthogonal Multi-band CAP for Highly Spectrally Efficient VLC Systems

In this work we propose and experimentally demonstrate a novel non-orthogonal multi-band carrier-less amplitude and phase (NM-CAP) scheme for bandlimited visible light communication systems in order to increase the spectral efficiency. We show that a bandwidth saving up to 30% can be achieved thus resulting in 44% improvement in the measured spectral efficiency with no further bit error rate performance degradation compared to the traditional m-CAP scheme. We also show that higher order systems can provide higher bandwidth compression than low order systems. Furthermore, with no additional functional blocks at the transmitter or the receiver the proposed scheme introduces no extra computational complexity.

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