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Stephan Pachnicke

Publications and source records attributed to Stephan Pachnicke.

At least 19 recordsLinked to original sources
arXiv2025

Cooperative-Memory Photonic Reservoir using Modulation Nonlinearity: Circumventing the Speed Constraints of Nonlinear Silicon Microring Resonators

Complex dynamics of silicon microring resonators loaded by delayed feedback elements enable high-speed photonic reservoir computing. Implementing feedback is especially challenging when the required delay should match the time scales of silicon's nonlinearities. To increase the computation speed and preclude any need for very long delay lines, we avoid relying on silicon's nonlinearity and merely employ either amplitude or phase modulation along with direct detection. By supplementing its memory with that of the electronic output layer, the proposed photonic reservoir composed of a Mach-Zehnder interferometer and a microring resonator is predicted to perform computations one order of magnitude faster than those based on silicon's nonlinearity with its speed only limited by the modulation/detection bandwidth. This reservoir performs accurately in NARMA-10, Mackey-Glass, and Santa-Fe prediction tasks, and enables signal equalization in optical communication systems.

physics.optics↗
arXiv2025

Soft-Demapping for Short Reach Optical Communication: A Comparison of Deep Neural Networks and Volterra Series

In optical fiber communication, optical and electrical components introduce nonlinearities, which require effective compensation to attain highest data rates. In particular, in short reach communication, components are the dominant source of nonlinearities. Volterra series are a popular countermeasure for receiver-side equalization of nonlinear component impairments and their memory effects. However, Volterra equalizer architectures are generally very complex. This article investigates soft deep neural network (DNN) architectures as an alternative for nonlinear equalization and soft-decision demapping. On coherent 92 GBd dual polarization 64QAM back-to-back measurements performance and complexity is experimentally evaluated. The proposed bit-wise soft DNN equalizer (SDNNE) is compared to a 5th order Volterra equalizer at a 15 % overhead forward error correction (FEC) limit. At equal performance, the computational complexity is reduced by 65 %. At equal complexity, the performance is improved by 0.35 dB gain in optical signal-to-noise-ratio (OSNR).

eess.SP↗
arXiv2023

Experimental Investigation of Machine Learning based Soft-Failure Management using the Optical Spectrum

The demand for high-speed data is exponentially growing. To conquer this, optical networks underwent significant changes getting more complex and versatile. The increasing complexity necessitates the fault management to be more adaptive to enhance network assurance. In this paper, we experimentally compare the performance of soft-failure management of different machine learning algorithms. We further introduce a machine-learning based soft-failure management framework. It utilizes a variational autoencoder based generative adversarial network (VAE-GAN) running on optical spectral data obtained by optical spectrum analyzers. The framework is able to reliably run on a fraction of available training data as well as identifying unknown failure types. The investigations show, that the VAE-GAN outperforms the other machine learning algorithms when up to 10\% of the total training data is available in identification tasks. Furthermore, the advanced training mechanism for the GAN shows a high F1-score for unknown spectrum identification. The failure localization comparison shows the advantage of a low complexity neural network in combination with a VAE over established machine learning algorithms.

cs.NI↗
arXiv2023

Fault Monitoring in Passive Optical Networks using Machine Learning Techniques

Passive optical network (PON) systems are vulnerable to a variety of failures, including fiber cuts and optical network unit (ONU) transmitter/receiver failures. Any service interruption caused by a fiber cut can result in huge financial losses for service providers or operators. Identifying the faulty ONU becomes difficult in the case of nearly equidistant branch terminations because the reflections from the branches overlap, making it difficult to distinguish the faulty branch given the global backscattering signal. With increasing network size, the complexity of fault monitoring in PON systems increases, resulting in less reliable monitoring. To address these challenges, we propose in this paper various machine learning (ML) approaches for fault monitoring in PON systems, and we validate them using experimental optical time domain reflectometry (OTDR) data.

cs.LG↗
arXiv2023

Faulty Branch Identification in Passive Optical Networks using Machine Learning

Passive optical networks (PONs) have become a promising broadband access network solution. To ensure a reliable transmission, and to meet service level agreements, PON systems have to be monitored constantly in order to quickly identify and localize networks faults. Typically, a service disruption in a PON system is mainly due to fiber cuts and optical network unit (ONU) transmitter/receiver failures. When the ONUs are located at different distances from the optical line terminal (OLT), the faulty ONU or branch can be identified by analyzing the recorded optical time domain reflectometry (OTDR) traces. However, faulty branch isolation becomes very challenging when the reflections originating from two or more branches with similar length overlap, which makes it very hard to discriminate the faulty branches given the global backscattered signal. Recently, machine learning (ML) based approaches have shown great potential for managing optical faults in PON systems. Such techniques perform well when trained and tested with data derived from the same PON system. But their performance may severely degrade, if the PON system (adopted for the generation of the training data) has changed, e.g. by adding more branches or varying the length difference between two neighboring branches. etc. A re-training of the ML models has to be conducted for each network change, which can be time consuming. In this paper, to overcome the aforementioned issues, we propose a generic ML approach trained independently of the network architecture for identifying the faulty branch in PON systems given OTDR signals for the cases of branches with close lengths. Such an approach can be applied to an arbitrary PON system without requiring to be re-trained for each change of the network. The proposed approach is validated using experimental data derived from PON system.

cs.LG↗
arXiv2023

Probabilistic Shaping for High-Speed Unamplified IM/DD Systems with an O-Band EML

Probabilistic constellation shaping has been used in long-haul optically amplified coherent systems for its capability to approach the Shannon limit and realize fine rate granularity. The availability of high-bandwidth optical-electronic components and the previously mentioned advantages have invigorated researchers to explore probabilistic shaping (PS) in intensity-modulation and direct-detection (IM/DD) systems. This article presents an extensive comparison of uniform 8-ary pulse amplitude modulation (PAM) with PS PAM-8 using cap and cup Maxwell-Boltzmann (MB) distributions as well as MB distributions of different Gaussian orders. We report that in the presence of linear equalization, PS-PAM-8 outperforms uniform PAM-8 in terms of bit error ratio, achievable information rate and operational net bit rate indicating that cap-shaped PS-PAM-8 shows high tolerance against nonlinearities. In this paper, we have focused our investigations on O-band electro-absorption modulated laser unamplified IM/DD systems, which are operated close to the zero dispersion wavelength.

eess.SP↗
arXiv2022

A Machine Learning-based Framework for Predictive Maintenance of Semiconductor Laser for Optical Communication

Semiconductor lasers, one of the key components for optical communication systems, have been rapidly evolving to meet the requirements of next generation optical networks with respect to high speed, low power consumption, small form factor etc. However, these demands have brought severe challenges to the semiconductor laser reliability. Therefore, a great deal of attention has been devoted to improving it and thereby ensuring reliable transmission. In this paper, a predictive maintenance framework using machine learning techniques is proposed for real-time heath monitoring and prognosis of semiconductor laser and thus enhancing its reliability. The proposed approach is composed of three stages: i) real-time performance degradation prediction, ii) degradation detection, and iii) remaining useful life (RUL) prediction. First of all, an attention based gated recurrent unit (GRU) model is adopted for real-time prediction of performance degradation. Then, a convolutional autoencoder is used to detect the degradation or abnormal behavior of a laser, given the predicted degradation performance values. Once an abnormal state is detected, a RUL prediction model based on attention-based deep learning is utilized. Afterwards, the estimated RUL is input for decision making and maintenance planning. The proposed framework is validated using experimental data derived from accelerated aging tests conducted for semiconductor tunable lasers. The proposed approach achieves a very good degradation performance prediction capability with a small root mean square error (RMSE) of 0.01, a good anomaly detection accuracy of 94.24% and a better RUL estimation capability compared to the existing ML-based laser RUL prediction models.

cs.LG↗
arXiv2022

Degradation Prediction of Semiconductor Lasers using Conditional Variational Autoencoder

Semiconductor lasers have been rapidly evolving to meet the demands of next-generation optical networks. This imposes much more stringent requirements on the laser reliability, which are dominated by degradation mechanisms (e.g., sudden degradation) limiting the semiconductor laser lifetime. Physics-based approaches are often used to characterize the degradation behavior analytically, yet explicit domain knowledge and accurate mathematical models are required. Building such models can be very challenging due to a lack of a full understanding of the complex physical processes inducing the degradation under various operating conditions. To overcome the aforementioned limitations, we propose a new data-driven approach, extracting useful insights from the operational monitored data to predict the degradation trend without requiring any specific knowledge or using any physical model. The proposed approach is based on an unsupervised technique, a conditional variational autoencoder, and validated using vertical-cavity surface-emitting laser (VCSEL) and tunable edge emitting laser reliability data. The experimental results confirm that our model (i) achieves a good degradation prediction and generalization performance by yielding an F1 score of 95.3%, (ii) outperforms several baseline ML based anomaly detection techniques, and (iii) helps to shorten the aging tests by early predicting the failed devices before the end of the test and thereby saving costs

cs.LG↗
arXiv2022

Convolutional Neural Networks for Reflective Event Detection and Characterization in Fiber Optical Links Given Noisy OTDR Signals

Fast and accurate fault detection and localization in fiber optic cables is extremely important to ensure the optical network survivability and reliability. Hence there exists a crucial need to develop an automatic and reliable algorithm for real time optical fiber fault detection and diagnosis leveraging the telemetry data obtained by an optical time domain reflectometry (OTDR) instrument. In this paper, we propose a novel data driven approach based on convolutional neural networks (CNNs) to detect and characterize the fiber reflective faults given noisy simulated OTDR data, whose SNR (signal-to-noise ratio) values vary from 0 dB to 30 dB, incorporating reflective event patterns. In our simulations, we achieved a higher detection capability with low false alarm rate and greater localization accuracy even for low SNR values compared to conventionally employed techniques.

eess.SP↗
arXiv2022

Machine Learning based Laser Failure Mode Detection

Laser degradation analysis is a crucial process for the enhancement of laser reliability. Here, we propose a data-driven fault detection approach based on Long Short-Term Memory (LSTM) recurrent neural networks to detect the different laser degradation modes based on synthetic historical failure data. In comparison to typical threshold-based systems, attaining 24.41% classification accuracy, the LSTM-based model achieves 95.52% accuracy, and also outperforms classical machine learning (ML) models namely Random Forest (RF), K-Nearest Neighbours (KNN) and Logistic Regression (LR).

eess.SP↗
arXiv2022

Optical Fiber Fault Detection and Localization in a Noisy OTDR Trace Based on Denoising Convolutional Autoencoder and Bidirectional Long Short-Term Memory

Optical time-domain reflectometry (OTDR) has been widely used for characterizing fiber optical links and for detecting and locating fiber faults. OTDR traces are prone to be distorted by different kinds of noise, causing blurring of the backscattered signals, and thereby leading to a misleading interpretation and a more cumbersome event detection task. To address this problem, a novel method combining a denoising convolutional autoencoder (DCAE) and a bidirectional long short-term memory (BiLSTM) is proposed, whereby the former is used for noise removal of OTDR signals and the latter for fault detection, localization, and diagnosis with the denoised signal as input. The proposed approach is applied to noisy OTDR signals of different levels of input SNR ranging from -5 dB to 15 dB. The experimental results demonstrate that: (i) the DCAE is efficient in denoising the OTDR traces and it outperforms other deep learning techniques and the conventional denoising methods; and (ii) the BiLSTM achieves a high detection and diagnostic accuracy of 96.7% with an improvement of 13.74% compared to the performance of the same model trained with noisy OTDR signals.

eess.SP↗
arXiv2022

Reflective Fiber Faults Detection and Characterization Using Long-Short-Term Memory

To reduce operation-and-maintenance expenses (OPEX) and to ensure optical network survivability, optical network operators need to detect and diagnose faults in a timely manner and with high accuracy. With the rapid advancement of telemetry technology and data analysis techniques, data-driven approaches leveraging telemetry data to tackle the fault diagnosis problem have been gaining popularity due to their quick implementation and deployment. In this paper, we propose a novel multi-task learning model based on long short-term memory (LSTM) to detect, locate, and estimate the reflectance of fiber reflective faults (events) including the connectors and the mechanical splices by extracting insights from monitored data obtained by the optical time domain reflectometry (OTDR) principle commonly used for troubleshooting of fiber optic cables or links. The experimental results prove that the proposed method: (i) achieves a good detection capability and high localization accuracy within short measurement time even for low SNR values; and (ii) outperforms conventionally employed techniques.

cs.NI↗