Machine learning (ML) has disrupted a wide range of science and engineering disciplines in recent years. ML applications in optical communications and networking are also gaining more attention, particularly in the areas of nonlinear transmission systems, optical performance monitoring, and cross-layer network optimizations for software-defined networks. However, the extent to which ML techniques can benefit optical communications and networking is not clear and this is partly due to an insufficient understanding of the nature of ML concepts. This paper aims to describe the mathematical foundations of basic ML techniques from communication theory and signal processing perspectives, which in turn will shed light on the types of problems in optical communications and networking that naturally warrant ML use. This will be followed by an overview of ongoing ML research in optical communications and networking with a focus on physical layer issues.

We experimentally demonstrate the use of deep neural networks (DNNs) in combination with signals’ amplitude histograms (AHs) for simultaneous optical signal-to-noise ratio (OSNR) monitoring and modulation format identification (MFI) in digital coherent receivers. The proposed technique automatically extracts OSNR and modulation format dependent features of AHs, obtained after constant modulus algorithm (CMA) equalization, and exploits them for the joint estimation of these parameters. Experimental results for 112 Gbps polarization-multiplexed (PM) quadrature phase-shift keying (QPSK), 112 Gbps PM 16 quadrature amplitude modulation (16-QAM), and 240 Gbps PM 64-QAM signals demonstrate OSNR monitoring with mean estimation errors of 1.2 dB, 0.4 dB, and 1 dB, respectively. Similarly, the results for MFI show 100% identification accuracy for all three modulation formats. The proposed technique applies deep machine learning algorithms inside standard digital coherent receiver and does not require any additional hardware. Therefore, it is attractive for cost-effective multi-parameter estimation in next-generation elastic optical networks (EONs).

Digital twin (DT) has revolutionized optical communication networks by enabling their full life-cycle management, including planning, prediction, optimization, upgrade, and troubleshooting. The rapid development of DT applications for optical networks, from concepts and architecture to simulation and experimental validation, has been exceptional. As a real-time virtual replica of physical optical networks, the DT of optical networks can be divided into three parts: real-time monitoring, mirror modeling, and automatic control. Recent advancements in each part have individually propelled the network towards intelligence and automation. In this paper, we make a comprehensive survey on this timely topic and illustrate the principal evolutions within these three parts. This will be followed by an overview of ongoing DT research in optical communications and networking, as well as an outlook on potential future directions for further investigation.

A novel ultra-long distributed vibration sensor using forward transmission, coherent detection, and a frequency-shifted optical delay line is proposed and experimentally demonstrated. In the proposed scheme, a pair of multi-span optical fibers is deployed for sensing. A frequency-shifted optical delay line consisting of an acoustic-optic modulator and a time delay fiber is used at the far end of these two fibers. Coherent detection is used to retrieve the vibration induced phase fluctuations of the base-band signals as well as the intermediate frequency signals generated by the frequency-shifted optical delay line. Two differential phase signals can be calculated from the obtained phase fluctuations, which can then be used to localize the vibration events by correlation operations. Localization of a few hundred Hz, around 1 kHz and tens of kHz vibrations has been experimentally demonstrated over a total length of 1230 km sensing fiber. Less than 125 m spatial resolution can be obtained over the 615 km sensing range for vibrations with larger than 1 kHz frequency by using averaging of 30 times tests due to the nature of asynchronous operation. Frequencies from infrasound to ultrasound can be detected. The proposed scheme has advantages of ultrabroad frequency response, ultra-long sensing range and simple sensing structure due to the nature of forward transmission and coherent detection. The sensing length can be further extended to even trans-oceanic distances using more fiber spans and erbium-doped fiber amplifiers, making it a promising candidate for vibration event detection and localization in long-haul and ultra-long-haul fiber links.

Silicon photonics is a low-cost and versatile platform for various applications. For design of silicon photonic devices, the light-material interaction within its complex subwavelength geometry is difficult to investigate analytically and therefore numerical simulations are majorly adopted. To make the design process more time-efficient and to improve the device performance to its physical limits, various methods have been proposed over the past few years to manipulate the geometries of silicon platform for specific applications. In this review paper, we summarize the design methodologies for silicon photonics including iterative optimization algorithms and deep neural networks. In case of iterative optimization methods, we discuss them in different scenarios in the sequence of increased degrees of freedom: empirical structure, QR-code like structure and irregular structure. We also review inverse design approaches assisted by deep neural networks, which generate multiple devices with similar structure much faster than iterative optimization methods and are thus suitable in situations where piles of optical components are needed. Finally, the applications of inverse design methodology in optical neural networks are also discussed. This review intends to provide the readers with the suggestion for the most suitable design methodology for a specific scenario.

We propose and experimentally demonstrate the use of principal component analysis (PCA) based pattern recognition to extract temperature distribution from the measured Brillouin gain spectra (BGSs) along the fiber under test (FUT) obtained by Brillouin optical time domain analysis (BOTDA) system. The proposed scheme employs a reference database consisting of relevant ideal BGSs with known temperature attributes. PCA is then applied to the BGSs in the reference database as well as to the measured BGSs so as to reduce their size by extracting their most significant features. Now, for each feature vector of the measured BGS, we determine its best match in the reference database comprised of numerous reduced-size feature vectors of the ideal BGSs. The known temperature attribute corresponding to the best-matched BGS in the reference database is then taken as the extracted temperature of the measured BGS. We analyzed the performance of PCA-based pattern recognition algorithm in detail and compared it with that of curve fitting method. The experimental results validate that the proposed technique can provide better accuracy, faster processing speed and larger noise tolerance for the measured BGSs. Therefore, the proposed PCA-based pattern recognition algorithm can be considered as an attractive method for extracting temperature distributions along the fiber in BOTDA sensors.