Neuro-Symbolic AI for Predictive Hardware Degradation in Aging SONET/SDH Architectures

Matheswaran Marimuthu — Thu, 29 Oct 2026 00:00:00 +0000

Synchronous Optical Networking (SONET) and the Synchronous Digital Hierarchy (SDH) still carry the most safety-critical traffic of power utilities, railways, and defense networks, yet their network management systems remain reactive: an alarm is raised only after a bit-error-rate or loss-of-signal threshold has already been crossed. This paper presents a neuro-symbolic framework that predicts hardware degradation 72 hours ahead by using only telemetry that every SONET/SDH element already produces: the B1/B2/B3 bit-interleaved-parity counts, H1/H2 pointer justifications, and optical digital diagnostics. A lightweight neural predictor learns multi-scale temporal signatures of degradation, and a symbolic constraint layer encodes ITU-T and Telcordia semantics. Veto rules explain away transients caused by protection switching, synchronization re-locking, or upstream path defects; assert rules capture laser end-of-life physics and optical-margin exhaustion. The two parts are fused in logit space, so every alert carries the rules that shaped it. This study evaluates the framework on a physically grounded telemetry simulator of 480 OC-192/STM-64 transceivers with four degradation mechanisms and three classes of benign operational events. They report the mean of three independent seeds that were not used during development. The neuro-symbolic predictor reaches an F1 score of 0.888, compared with 0.094 for threshold-crossing alerts. It detects 70 of 70 test failures with a mean lead time of 58 h and cuts false alarms from 35.3 to 0.58 per transceiver-year. Under an unseen maintenance-campaign shift, it keeps false alarms 3.2× lower than a purely neural predictor. The simulator and code are provided for reproducibility.

High-Gain Switched-Capacitor Multilevel Inverters for Renewable Energy Conversion

Pawan Singh Kushwah, Shweta Raj — Wed, 30 Sep 2026 00:00:00 +0000

Switched-capacitor multilevel inverters (SCMLIs) are an important transformerless path towards high-gain, high-quality power conversion from low direct-current to multilevel alternating currents for scalable applications such as solar photovoltaic and distributed renewable energy sources, utilizing only capacitors and semiconductor switches. In this paper, recent research advances in the implementation of circuit- and control-level 17-level and closely related high-level SCMLI structures, compiled from 48 peer-reviewed papers from IEEE, Elsevier, Springer and MDPI, are reviewed. The topologies are divided into four structural families: CT-/T-type hybrids, cross-switched and modular hybrid topologies, common-ground configurations, and cascaded or neutral-point-clamped derivatives, and are compared on the basis of the following parameters: output-level count, voltage-boosting factor, total standing voltage, active-device count, and measured conversion efficiency. Inherent capacitor self-balancing occurs in a majority of the designs in the set and is demonstrated in most of the reviewed designs to eliminate dedicated voltage-sensing loops, and common-ground switching is reportedly the preferred method to suppress leakage currents in transformerless PV strings. Nearest-level control and selective/level-shifted pulse-width-modulation schemes are shown to jointly dictate how the harmonic quality and switching loss trade off, and several designs are identified that achieve seven- to eight-fold voltage gain from a single low-voltage source with a reduced number of semiconductors while maintaining high harmonic quality. Nevertheless, through the synthesis, it also reveals several shortcomings in the state of reporting the total standing voltage, high power, experimental verification, mitigation of inrush currents in capacitors, and the formal modelling of reliability within standard performance power-conversion interfaces, defining those as priority avenues for design optimization of the next generation of high-level power-conversion interfaces.

Research & Review: Electronics and Communication Engineering

Neuro-Symbolic AI for Predictive Hardware Degradation in Aging SONET/SDH Architectures

High-Gain Switched-Capacitor Multilevel Inverters for Renewable Energy Conversion