Optical network physical layer parameter optimization for digital backpropagation using Gaussian processes

We present a novel methodology for optimizing fiber optic network performance by determining the ideal values for attenuation, nonlinearity, and dispersion parameters in terms of achieved signal-to-noise ratio (SNR) gain from digital backpropagation (DBP). Our approach uses Gaussian process regression, a probabilistic machine learning technique, to create a computationally efficient model for mapping these parameters to the resulting SNR after applying DBP. We then use simplicial homology global optimization to find the parameter values that yield maximum SNR for the Gaussian process model within a set of a priori bounds. This approach optimizes the parameters in terms of the DBP gain at the receiver. We demonstrate the effectiveness of our method through simulation and experimental testing, achieving optimal estimates of the dispersion, nonlinearity, and attenuation parameters. Our approach also highlights the limitations of traditional one-at-a-time grid search methods and emphasizes the interpretability of the technique. This methodology has broad applications in engineering and can be used to optimize performance in various systems beyond optical networks

Stochastic Digital Backpropagation

In this paper, we propose a novel detector for single-channel long-haul coherent optical communications, termed stochastic digital backpropagation (SDBP), which takes into account noise from the optical amplifiers in addition to handling deterministic linear and nonlinear impairments. We discuss the design approach behind this detector, which is based on the maximum a posteriori (MAP) principle. As closed-form expressions of the MAP detector are not tractable for coherent optical transmission, we employ the framework of Bayesian graphical models, which allows a numerical evaluation of the proposed detector. Through simulations, we observe that by accounting for nonlinear signal–noise interactions, we achieve a significant improvement in system reach with SDBP over digital backpropagation (DBP) for systems with periodic inline optical dispersion compensation. In uncompensated links with high symbol rates, the performance difference in terms of system reach for SDBP over DBP is small. In the absence of noise, the proposed detector is equivalent to the well-known DBP detector

A survey on fiber nonlinearity compensation for 400 Gbps and beyond optical communication systems

Optical communication systems represent the backbone of modern communication networks. Since their deployment, different fiber technologies have been used to deal with optical fiber impairments such as dispersion-shifted fibers and dispersion-compensation fibers. In recent years, thanks to the introduction of coherent detection based systems, fiber impairments can be mitigated using digital signal processing (DSP) algorithms. Coherent systems are used in the current 100 Gbps wavelength-division multiplexing (WDM) standard technology. They allow the increase of spectral efficiency by using multi-level modulation formats, and are combined with DSP techniques to combat the linear fiber distortions. In addition to linear impairments, the next generation 400 Gbps/1 Tbps WDM systems are also more affected by the fiber nonlinearity due to the Kerr effect. At high input power, the fiber nonlinear effects become more important and their compensation is required to improve the transmission performance. Several approaches have been proposed to deal with the fiber nonlinearity. In this paper, after a brief description of the Kerr-induced nonlinear effects, a survey on the fiber nonlinearity compensation (NLC) techniques is provided. We focus on the well-known NLC techniques and discuss their performance, as well as their implementation and complexity. An extension of the inter-subcarrier nonlinear interference canceler approach is also proposed. A performance evaluation of the well-known NLC techniques and the proposed approach is provided in the context of Nyquist and super-Nyquist superchannel systems.Comment: Accepted in the IEEE Communications Surveys and Tutorial

Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.Peer reviewe

Fault feature extraction method based on EWT-SMF and MF-DFA for valve fault of reciprocating compressor

According to the nonlinearity and nonstationarity characteristics of reciprocating compressor vibration signal, a fault feature extraction method of reciprocating compressor based on the empirical wavelet transform (EWT) and state-adaptive morphological filtering (SMF) is proposed. Firstly, an adaptive empirical wavelet transform was used to divide the Fourier spectrum by constructing a scale-space curve, and an appropriate orthogonal wavelet filter bank was constructed to extract the AM-FM component with a tightly-supported Fourier spectrum. Then according to the impact characteristic of the reciprocating compressor vibration signal, the morphological structural elements were constructed with the characteristics of the signal to perform state-adaptive morphological filtering on the partitioned modal functions. Finally, the MF-DFA method of the modal function was quantitatively analyzed and the fault identification was performed. By analyzing the experimental data, it can be shown that the method can effectively identify the fault type of reciprocating compressor valve

Weighted Chebyshev Distance Algorithms for Hyperspectral Target Detection and Classification Applications

In this study, an efficient spectral similarity method referred to as Weighted Chebyshev Distance (WCD) is introduced for supervised classification of hyperspectral imagery (HSI) and target detection applications. The WCD is based on a simple spectral similarity based decision rule using limited amount of reference data. The estimation of upper and lower spectral boundaries of spectral signatures for all classes across spectral bands is referred to as a vector tunnel (VT). To obtain the reference information, the training signatures are provided randomly from existing data for a known class. After determination of the parameters of the WCD algorithm with the training set, classification or detection procedures are accomplished at each pixel. The comparative performances of the algorithms are tested under various cases. The decision criterion for classification of an input vector is based on choosing its class corresponding to the narrowest VT that the input vector fits in to. This is also shown to be approximated by the WCD in which the weights are chosen as an inverse power of the generalized standard deviation per spectral band. In computer experiments, the WCD classifier is compared with the Euclidian Distance (ED) classifier and the Spectral Angle Map (SAM) classifier. The WCD algorithm is also used for HSI target detection purpose. Target detection problem is considered as a two-class classification problem. The WCD is characterized only by the target class spectral information. Then, this method is compared with ED, SAM, Spectral Matched Filter (SMF), Adaptive Cosine Estimator (ACE) and Support Vector Machine (SVM) algorithms. During these studies, threshold levels are evaluated based on the Receiver Operating Characteristic Curves (ROC)

Photonic reservoir computing enabled by stimulated Brillouin scattering

Artificial Intelligence (AI) drives the creation of future technologies that disrupt the way humans live and work, creating new solutions that change the way we approach tasks and activities, but it requires a lot of data processing, large amounts of data transfer, and computing speed. It has led to a growing interest of research in developing a new type of computing platform which is inspired by the architecture of the brain specifically those that exploit the benefits offered by photonic technologies, fast, low-power, and larger bandwidth. Here, a new computing platform based on the photonic reservoir computing architecture exploiting the non-linear wave-optical dynamics of the stimulated Brillouin scattering is reported. The kernel of the new photonic reservoir computing system is constructed of an entirely passive optical system. Moreover, it is readily suited for use in conjunction with high performance optical multiplexing techniques to enable real-time artificial intelligence. Here, a methodology to optimise the operational condition of the new photonic reservoir computing is described which is found to be strongly dependent on the dynamics of the stimulated Brillouin scattering system. The new architecture described here offers a new way of realising AI-hardware which highlight the application of photonics for AI.Comment: 8 pages, 6 figures, research articl