Neuro-Symbolic AI for Predictive Hardware Degradation in Aging SONET/SDH Architectures
Keywords:
Bit-Interleaved parity, Explainable AI, Neuro-Symbolic AI, Optical transceiver aging, Performance monitoring, Pointer justification, Predictive maintenance, SDH, SONETAbstract
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.
References
R. Ramaswami, K. N. Sivarajan, and G. H. Sasaki, Optical Networks: A Practical Perspective, 3rd ed. Burlington, MA, USA: Morgan Kaufmann, 2010.
W. J. Goralski, SONET/SDH, 3rd ed. New York, NY, USA: McGraw-Hill, 2002.
IEC, “Communication Networks and Systems for Power Utility Automation – Part 90-12: Wide Area Network Engineering Guidelines,” IEC, Geneva, Switzerland, Jul. 2015.
M. Fukuda, Reliability and Degradation of Semiconductor Lasers and LEDs. Norwood, MA, USA: Artech House, 1991.
GR-468-CORE, “Generic Reliability Assurance Requirements for Optoelectronic Devices Used in Telecommunications Equipment,” Telcordia Technologies, Ericsson.com, no. 2, Dec 1998.
S. Bregni, Synchronization of Digital Telecommunications Networks. Chichester, U.K.: Wiley, 2002.
GR-253-CORE, Synchronous Optical Network (SONET) Transport Systems: Common Generic Criteria, Telcordia Technologies, Ericsson.com, no. 5, Oct. 2009.
ITU, “Characteristics of Synchronous Digital Hierarchy (SDH) Equipment Functional Blocks,” Itu.int. 2020.
F. Musumeci et al., "An Overview of the Application of Machine Learning Techniques in Optical Networks," in IEEE Communications Surveys & Tutorials, vol. 21, no. 2, pp. 1383-1408, Nov. 2019.
J. Mata et al., “Artificial Intelligence (AI) Methods in Optical Networks: A Comprehensive Survey,” Optical Switching and Networking, vol. 28, pp. 43–57, Apr. 2018.
D. Rafique and L. Velasco, “Machine Learning for Network Automation: Overview, Architecture, and Applications,” Journal of Optical Communications and Networking, vol. 10, no. 10, p. D126, Aug. 2018.
A. P. Vela et al., "BER Degradation Detection and Failure Identification in Elastic Optical Networks," in Journal of Lightwave Technology, vol. 35, no. 21, pp. 4595-4604, Nov, 2017.
S. Shahkarami, F. Musumeci, F. Cugini and M. Tornatore, "Machine-Learning-Based Soft-Failure Detection and Identification in Optical Networks," 2018 Optical Fiber Communications Conference and Exposin (OFC), San Diego, CA, USA, Mar. 2018, pp. 1-3.
D. Rafique, T. Szyrkowiec, H. Grießer, A. Autenrieth, and J.-P. Elbers, “Cognitive assurance architecture for optical network fault management,” J. Lightw. Technol., vol. 36, no. 7, pp. 1443–1450, Apr. 2018.
K. Christodoulopoulos et al., "Toward efficient, reliable, and autonomous optical networks: the ORCHESTRA solution," in Journal of Optical Communications and Networking, vol. 11, no. 9, pp. C10-C24, Sep. 2019.
A. A. Garcez and L. C. Lamb, “Neurosymbolic AI: The 3Rd Wave,” Artificial Intelligence Review, vol. 56, no. 11, pp. 12387–12406, Mar. 2023.
G.707, Network Node Interface for the Synchronous Digital Hierarchy (SDH), ITU-T Recommendation G.707/Y.1322, Jan. 2007.
G.841, Types and Characteristics of SDH Network Protection Architectures, ITU-T Recommendation, Oct. 1998.
G.813, Timing Characteristics of SDH Equipment Slave Clocks (SEC), ITU-T Recommendation G.813, Mar. 2003.
G.826, End-to-End Error Performance Parameters and Objectives for International, Constant Bit-Rate Digital Paths and Connections, ITU-T Recommendation G.826, Dec. 2002.
G. P. Agrawal, Fiber-Optic Communication Systems, 4th ed. Hoboken, NJ, USA: Wiley, 2010.
Z. Wang et al., “Failure prediction using machine learning and time series in optical network,” Opt. Express, vol. 25, no. 16, pp. 18553–18565, Aug. 2017.
A. Vaswani et al., “Attention is all you need,” 31st Conference on Neural Information Processing Systems (NIPS 2017), Dec. 2017, pp. 6000 - 6010.
B. Lim, S. Ö. Arık, N. Loeff, and T. Pfister, “Temporal Fusion Transformers for Interpretable Multi-Horizon Time Series Forecasting,” International Journal of Forecasting, vol. 37, no. 4, pp. 1748–1764, Oct. 2021.
H. Zhou et al., “Informer: Beyond efficient transformer for long sequence time-series forecasting,” in Proc. AAAI Conf. Artif. Intell., vol. 35, no. 12, pp. 11106–11115, May. 2021.
Y. Nie, N. H. Nguyen, P. Sinthong, and J. Kalagnanam, “A time series is worth 64 words: Long-term forecasting with transformers,” In Proceedings of the International Conference on Learn. Represent. (ICLR), 2023.
Z. Hu, X. Ma, Z. Liu, E. Hovy, and E. Xing, “Harnessing deep neural networks with logic rules,” Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Aug. 2016, pp. 2410–2420.
S. Badreddine, A. d’Avila Garcez, L. Serafini, and M. Spranger, “Logic tensor networks,” Artificial Intelligence, vol. 303, Feb. 2022.
J. Mao, C. Gan, P. Kohli, J. B. Tenenbaum, and J. Wu, “The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision,” Proceedings of the International Conference on Learning Representations, Dec. 2019.
T. Saito and M. Rehmsmeier, “The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets,” PLoS ONE, vol. 10, no. 3, Mar. 2015.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning Representations by Back-Propagating Errors,” Nature, vol. 323, no. 6088, pp. 533–536, Oct. 1986.
D.P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” arXiv, Oct. 23, 2026.
