Optical Receiver SOP Reconstruction for Early Anomaly Detection
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Solution Overview
Problem
Existing optical fiber networks lack effective proactive fault detection mechanisms, relying largely on reactive fault management, which can lead to delayed identification of potential anomalies.
Innovation Solution
An optical receiver apparatus and method utilizing a lossy sequence reconstruction algorithm, implemented in part by a neural network, to detect anomalies by reconstructing state-of-polarization sequences, signaling anomalies when dissimilarity exceeds a threshold, enabling early detection of potential faults.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional reactive fault management is used, then device complexity is reduced, but fault detection timing is delayed
Solution Approach 1:
The system performs preliminary actions by training a neural network model in advance using historical SOP sequences labeled with fault types. The model learns to recognize patterns associated with different fault conditions before actual fault detection is needed, enabling early and accurate anomaly detection without adding complex real-time analysis infrastructure.
Solution Approach 2:
A neural network model is introduced as an intermediary between the raw SOP sequence data and fault detection. The model acts as a mediator that processes the optical signal characteristics and translates them into meaningful fault predictions, simplifying the overall system architecture while improving detection capabilities.
2Measurement precision
If lossy sequence reconstruction algorithm is applied, then anomaly detection accuracy is improved, but processing time is increased
Solution Approach 1:
The system applies partial action by using a lossy reconstruction algorithm that intentionally introduces controlled degradation. Rather than perfectly reconstructing the SOP sequence, the algorithm applies sufficient transformation to highlight anomalies while maintaining processing efficiency, achieving adequate detection accuracy without excessive processing time.
3Measurement precision
If neural network is trained on normal SOP sequences only, then detection precision for normal operations is improved, but ability to detect novel anomalies is reduced
Solution Approach 1:
The system implements feedback by using labeled historical fault data to train the neural network model. The model learns from past fault patterns and continuously improves its detection capabilities. This feedback mechanism enables the system to adapt to different fault types while maintaining high precision for normal operation detection.
Solution Approach 2:
The neural network model is designed with universal applicability to detect multiple types of faults across different fault scenarios. By training on diverse labeled data representing various fault conditions, the model becomes versatile in detecting different anomaly types while maintaining a unified detection framework.
Data Source
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AI summary
An apparatus for an optical receiver, the apparatus comprising processing circuitry configured to: receive an original state-of-polarization, SOP, sequence, the original SOP sequence being a sequence of SOP samples of an optical signal received at the optical receiver; generate a reconstructed SOP sequence by applying a lossy sequence reconstruction algorithm to the original SOP sequence; determine a level of dissimilarity between the original SOP sequence and the reconstructed SOP sequence; and signal an anomaly in response to the level of dissimilarity exceeding a set threshold.