Machine Learning Applications for Short Reach Optical Communication

With the rapid development of optical communication systems, more advanced techniques conventionally used in long-haul transmissions have gradually entered systems covering shorter distances below 100 km, where higher-speed connections are required in various applications, such as the optical access networks, inter- and intra-data center interconnects, mobile fronthaul, and in-building and indoor communications. One of the techniques that has attracted intensive interests in short-reach optical communications is machine learning (ML). Due to its robust problem-solving, decision-making, and pattern recognition capabilities, ML techniques have become an essential solution for many challenging aspects. In particular, taking advantage of their high accuracy, adaptability, and implementation efficiency, ML has been widely studied in short-reach optical communications for optical performance monitoring (OPM), modulation format identification (MFI), signal processing and in-building/indoor optical wireless communications. Compared with long-reach communications, the ML techniques used in short-reach communications have more stringent complexity and cost requirements, and also need to be more sensitive. In this paper, a comprehensive review of various ML methods and their applications in short-reach optical communications are presented and discussed, focusing on existing and potential advantages, limitations and prospective trends.

Machine Learning-Aided Optical Performance Monitoring Techniques: A Review

Future communication systems are faced with increased demand for high capacity, dynamic bandwidth, reliability and heterogeneous traffic. To meet these requirements, networks have become more complex and thus require new design methods and monitoring techniques, as they evolve towards becoming autonomous. Machine learning has come to the forefront in recent years as a promising technology to aid in this evolution. Optical fiber communications can already provide the high capacity required for most applications, however, there is a need for increased scalability and adaptability to changing user demands and link conditions. Accurate performance monitoring is an integral part of this transformation. In this paper, we review optical performance monitoring techniques where machine learning algorithms have been applied. Moreover, since many performance monitoring approaches in the optical domain depend on knowledge of the signal type, we also review work for modulation format recognition and bitrate identification. We additionally briefly introduce a neuromorphic approach as an emerging technique that has only recently been applied to this domain.

Simultaneous monitoring of the values of CD, Crosstalk and OSNR phenomena in the physical layer of the optical network using CNN

The aim of the research was to explore the possibilities of using the Asynchronous Delay Tap Sampling (ADTS) and Convolutional Neural Network (CNN) methods to monitor the simultaneously occurring phenomena in the physical layer of the optical network. The ADTS method was used to create a data sets showing the combination of Chromatic Dispersion (CD), Crosstalk and Optical to Signal Noise Ratio (OSNR) as optical disturbances in graphic form. Data were generated for 10 GB/s, Non-return-to-zero On–off keying (NRZ-OOK) and Differential Phase Shift Keying (DPSK) modulation and bit delays: 1 bit, 0.5 bit and 0.25 bit. A total of 6 data sets of 62,000 images each were obtained. The learning process was carried out for the number of epochs 50 and 1000. From the obtained learning results of the network, models with the best $$R^{2}$$ R 2 matching factor were selected. The learned models were further used to study the recognition of three phenomena simultaneously. The tests were carried out on sets of 2500 images in a combination of interference in the following ranges: 400–1600 ps/nm for CD and 10–30 dB for Crosstalk and OSNR. Very good results were obtained for recognizing simultaneously occurring phenomena using models learned up to 1000 epoch. Accuracy of over 99% was obtained for CD and Crosstalk for both modulations. In the case of the OSNR phenomenon, slightly weaker results were obtained above 96% in most cases. For models taught up to 50 epoch, very good results were obtained for the CD phenomenon (over 99%). For Crosstalk weaker results for OOK modulation were obtained. Poor results were obtained for the OSNR phenomenon, where recognition accuracy ranged from 50 to 80%, depending on the type of modulation and bit delay. Based on the conducted research, it was established that the use of ADTS and CNN methods enables monitoring of simultaneously occurring CD, Crosstalk and OSNR interference in the physical layer of the optical network, while maintaining the requirements for Optical Performance Monitoring systems. These requirements are met for network models learned up to 1000 epoch.

Going Deeper into OSNR Estimation with CNN

As optical performance monitoring (OPM) requires accurate and robust solutions to tackle the increasing dynamic and complicated optical network architectures, we experimentally demonstrate an end-to-end optical signal-to-noise (OSNR) estimation method based on the convolutional neural network (CNN), named OptInception. The design principles of the proposed scheme are specified. The idea behind the combination of the Inception module and finite impulse response (FIR) filter is elaborated as well. We experimentally evaluate the mean absolute error (MAE) and root-mean-squared error (RMSE) of the OSNR monitored in PDM-QPSK and PDM-16QAM signals under various symbol rates. The results suggest that the MAE reaches as low as 0.125 dB and RMSE is 0.246 dB in general. OptInception is also proved to be insensitive to the symbol rate, modulation format, and chromatic dispersion. The investigation of kernels in CNN indicates that the proposed scheme helps convolutional layers learn much more than a lowpass filter or bandpass filter. Finally, a comparison in performance and complexity presents the advantages of OptInception.

Artificial intelligence based optical performance monitoring

In this paper, a technique for optical performance monitoring (OPM) using deep learning-based artificial neural network (ANN) is implemented. ANN is trained with parameters derived from eye-diagram for the identification of optical signal to noise ratio (OSNR), chromatic dispersion (CD) and polarisation mode dispersion (PMD) simultaneously and independently in a 10 Gb/s system with non-return-to-zero (NRZ) on-off keying (OOK) data signal. ANN-based OPM confirms that the proposed approach can provide reliable estimated results. The mean squared errors for OSNR, CD and differential group delay (DGD) are found to be 4.6071 dB, 0.0417 ps/nm/km and 0.0016 ps/km, respectively. The proposed technique may be utilized in analyzing the signals of future heterogeneous optical communication networks intelligently.

Joint Estimation of Symbol Rate and Chromatic Dispersion Using Delayed Multiplier for Optical Performance Monitoring

Symbol rate and chromatic dispersion (CD) are very important for optical performance monitoring. The CD, however, hinders the symbol rate detection. In this paper, we proposed a joint estimation of symbol rate and chromatic dispersion. We show that, when the signal conjugates and multiplies with the delayed replica, the spectral line can be restored. The proposed method provides a fast and simple solution for joint estimation as traditional tentative CD scanning is time consuming. The simulation shows that the root-mean-squared error (RMSE) for CD was 39.5 ps/nm and the symbol rate was 2.4 MHz. For the squared-root-raised-cosine (SRRC) pulse shape with a roll-off factor of 0.1, the experimental results show that 25,000 input samples were needed for an error-free estimation. The RMSE is 105.6 ps/nm and 63.5 kHz for CD and symbol rate, respectively.