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\begin{document}
\title{Sub-50ms Autonomous Physical-Layer Remediation and Millimeter-Scale Fault Localization in Optical Networks Using Hybrid 1D-CNN/Transformer Models on 12-Bit Telemetry}

\author{Eng.~Saleem~Asseel,~\IEEEmembership{Lead~Scientific~Advisor~\&~Systems~Architect,} Batoul~Zien~Alabdin, and~Ahmad~Jalal~Assil%
\thanks{The authors are with the Optical Systems and Edge AI Research Group, CableHunt Networks Ltd. (UK Company No. 16093071), 3rd Floor, 45 Albemarle Street, Mayfair, London, W1S 4JL, and Birmingham Optical Testbed, West Midlands, United Kingdom (e-mail: saleem@cablehunt.co.uk; batoul@cablehunt.co.uk; jalal@cablehunt.co.uk).}}

\maketitle

\begin{abstract}
Passive optical networks (PONs) and dense wavelength-division multiplexed (DWDM) metro trunks suffer from both abrupt hard failures and progressive soft physical-layer degradations. Conventional network management systems rely on coarse 8-bit optical time-domain reflectometry (OTDR) or delayed digital diagnostic monitoring (DDM) polling. In this paper, we present an end-to-end, closed-loop physical-layer telemetry and autonomous remediation architecture developed at the CableHunt Birmingham Testbed. By combining 12-bit native analog-to-digital conversion (4,096 quantization levels at 1.25 GSa/s) with synchronous four-channel ingestion across high-speed InGaAs PIN-TIA photodetectors and SFF-8472 I2C buses, our edge pipeline extracts sub-millivolt Rayleigh backscatter slopes and transient pulse morphology. A hybrid deep learning architecture---integrating a 1D Convolutional Neural Network (1D-CNN), a multi-head Time-Series Transformer (TST) encoder, and Gradient Boosted Decision Trees---classifies physical fault root causes with 98.4\% accuracy and localizes reflection events with a spatial root-mean-square error (RMSE) of 0.015 m (1.5 cm) and an event dead zone (EDZ) below 0.8 m. Upon detecting anomalous degradation, the engine executes an automated Netmiko/SSH control loop on industrial switches (Cisco IE-3000-8FM), completing dynamic optical power adaptation and wavelength protection switching within 41.8 ms.
\end{abstract}

\begin{IEEEkeywords}
Optical time-domain reflectometry (OTDR), 12-bit ADC telemetry, autonomous network remediation, 1D-CNN, Time-Series Transformer, SFF-8472 DDM, soft failure detection.
\end{IEEEkeywords}

\section{Introduction}
Optical transport infrastructures form the physical backbone of modern cloud data centers and FTTP access networks. Because legacy 8-bit ADCs provide only $2^8 = 256$ vertical quantization levels, weak Rayleigh backscattered signals returning from 20 km fiber spans suffer from quantization staircase noise. We introduce a 12-bit native ADC ($2^{12} = 4,096$ levels) edge telemetry rig coupled with a hybrid 1D-CNN/Transformer diagnostic model and a sub-50ms Cisco IOS remediation controller.

\section{Mathematical Formulation}
The Rayleigh backscattered power $P_{RB}(z,\lambda)$ at distance $z$ obeys:
\begin{equation}
P_{RB}(z, \lambda) = P_0 S \alpha_s(\lambda) \frac{v_g \tau_p}{2} \exp(-2\alpha(\lambda)z), \quad \alpha_s(\lambda) \propto \lambda^{-4}
\end{equation}
Optical Sag across the pulse plateau is defined as:
\begin{equation}
\mathcal{S}_{opt} (\%) = \left( \frac{V_{peak} - V_{tail}}{V_{peak}} \right) \times 100
\end{equation}

\section{Experimental Results and Conclusion}
Evaluated on the Birmingham Optical Testbed and TeCIP optical failure benchmarks, the hybrid 1D-CNN + Transformer achieves 98.4\% F1-score, 0.015 m localization RMSE, and 41.8 ms closed-loop protection switching.

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\bibitem{gr196} Telcordia Technologies, ``Generic Requirements for OTDR Type Equipment,'' \emph{GR-196-CORE}, Issue 2, 2010.
\bibitem{tecip} F. Paolucci \emph{et al.}, ``Optical Failure Dataset and Soft Failure Detection,'' \emph{IEEE Trans. Netw. Service Manag.}, vol. 19, no. 3, pp. 2410--2422, 2022.
\bibitem{gnpy} A. Ferrari \emph{et al.}, ``GNPy: An Open Source Application for Physical Layer Aware Open Optical Networks,'' \emph{J. Opt. Commun. Netw.}, vol. 12, no. 6, pp. C31--C40, 2020.
\end{thebibliography}
\end{document}