Machine Learning Techniques to Mitigate Nonlinear Phase Noise in Moderate Baud Rate Optical Communication Systems

Nonlinear phase noise (NLPN) is the most common impairment that degrades the performance of radio-over-fiber networks. The effect of NLPN in the constellation diagram consists of a shape distortion of symbols that increases the symbol error rate due to symbol overlapping when using a conventional demodulation grid. Symbol shape characterization was obtained experimentally at a moderate baud rate (250 MBd) for constellations impaired by phase noise due to a mismatch between the optical carrier and the transmitted radio frequency signal. Machine learning algorithms have become a powerful tool to perform monitoring and to identify and mitigate distortions introduced in both the electrical and optical domains. Clustering-based demodulation assisted with Voronoi contours enables the definition of non-Gaussian boundaries to provide flexible demodulation of 16-QAM and 4+12 PSK modulation formats. Phase-offset and in-phase and quadrature imbalance may be detected on the received constellation and compensated by applying thresholding boundaries obtained from impairment characterization through statistical analysis. Experimental results show increased tolerance to the optical signal-to-noise ratio (OSNR) obtained from clustering methods based on k-means and fuzzy c-means Gustafson-Kessel algorithms. Improvements of 3.2 dB for 16-QAM, and 1.4 dB for 4+12 PSK in the OSNR scale as a function of the bit error rate are obtained without requiring additional compensation algorithms

Machine Learning Techniques to Mitigate Nonlinear Phase Noise in Moderate Baud Rate Optical Communication Systems

Nonlinear phase noise (NLPN) is the most common impairment that degrades the performance of radio-over-fiber networks. The effect of NLPN in the constellation diagram consists of a shape distortion of symbols that increases the symbol error rate due to symbol overlapping when using a conventional demodulation grid. Symbol shape characterization was obtained experimentally at a moderate baud rate (250 MBd) for constellations impaired by phase noise due to a mismatch between the optical carrier and the transmitted radio frequency signal. Machine learning algorithms have become a powerful tool to perform monitoring and to identify and mitigate distortions introduced in both the electrical and optical domains. Clustering-based demodulation assisted with Voronoi contours enables the definition of non-Gaussian boundaries to provide flexible demodulation of 16-QAM and 4+12 PSK modulation formats. Phase-offset and in-phase and quadrature imbalance may be detected on the received constellation and compensated by applying thresholding boundaries obtained from impairment characterization through statistical analysis. Experimental results show increased tolerance to the optical signal-to-noise ratio (OSNR) obtained from clustering methods based on k-means and fuzzy c-means Gustafson-Kessel algorithms. Improvements of 3.2 dB for 16-QAM, and 1.4 dB for 4+12 PSK in the OSNR scale as a function of the bit error rate are obtained without requiring additional compensation algorithms

Chapter Machine Learning Techniques to Mitigate Nonlinear Phase Noise in Moderate Baud Rate Optical Communication Systems

Nonlinear phase noise (NLPN) is the most common impairment that degrades the performance of radio-over-fiber networks. The effect of NLPN in the constellation diagram consists of a shape distortion of symbols that increases the symbol error rate due to symbol overlapping when using a conventional demodulation grid. Symbol shape characterization was obtained experimentally at a moderate baud rate (250 MBd) for constellations impaired by phase noise due to a mismatch between the optical carrier and the transmitted radio frequency signal. Machine learning algorithms have become a powerful tool to perform monitoring and to identify and mitigate distortions introduced in both the electrical and optical domains. Clustering-based demodulation assisted with Voronoi contours enables the definition of non-Gaussian boundaries to provide flexible demodulation of 16-QAM and 4+12 PSK modulation formats. Phase-offset and in-phase and quadrature imbalance may be detected on the received constellation and compensated by applying thresholding boundaries obtained from impairment characterization through statistical analysis. Experimental results show increased tolerance to the optical signal-to-noise ratio (OSNR) obtained from clustering methods based on k-means and fuzzy c-means Gustafson-Kessel algorithms. Improvements of 3.2 dB for 16-QAM, and 1.4 dB for 4+12 PSK in the OSNR scale as a function of the bit error rate are obtained without requiring additional compensation algorithms

Unsupervised and supervised machine learning for performance improvement of NFT optical transmission

We apply both the unsupervised and supervised machine learning (ML) methods, in particular, the k-means clustering and support vector machine (SVM) to improve the performance of the optical communication system based on the nonlinear Fourier transform (NFT). The NFT system employs the continuous NFT spectrum part to carry data up to 1000 km using the 16-QAM OFDM modulation. We classify the performance of the system in terms of BER versus signal power dependence. We show that the NFT system performance can be improved considerably by means of the ML techniques and that the more advanced SVM method typically outperforms the k-means clustering

Developing coherent optical wavelength conversion systems for reconfigurable photonic networks

In future optical networks that employ wavelength division multiplexing (WDM), the use of optical switching technologies on a burst or packet level, combined with advanced modulation formats would achieve greater spectral efficiency and utilize the existing bandwidth more efficiently. All-optical wavelength converters are expected to be one of the key components in these broadband networks. They can be used at the network nodes to avoid contention and to dynamically allocate wavelengths to ensure optimum use of fiber bandwidth. In this work, a reconfigurable wavelength converter comprising of a Semiconductor Optical Amplifier (SOA) as the nonlinear element and a fast-switching sampled grating distributed Bragg reflector (SG-DBR) tunable laser as one of the pumps is developed. The wavelength conversion of 12.5-Gbaud quadrature phase shift keying (QPSK) and Pol-Mul QPSK signals with switching time of tens of nanoseconds is experimentally achieved. Although the tunable DBR lasers can achieve ns tuning time, they present relatively large phase noise. The phase noise transfer from the pump to the converted signal can have a deleterious effect on signal quality and cause a performance penalty with phase modulated signals. To overcome the phase noise transfer issue, a wavelength converter using tunable dual-correlated pumps provided by the combination of a single-section quantum dash passively mode-locked laser (QD-PMLL) and a programmable tunable optical filter is designed and the wavelength conversion of QPSK and 16-quadrature amplitude modulation (16-QAM) signals at 12.5 GBaud is experimentally investigated. Nonlinear distortion of the wavelength converted signal caused by gain saturation effects in the SOA can significantly degrade the signal quality and cause difficulties for the practical wavelength conversion of sig nal data with advanced modulation formats. In this work, the machine learning clustering based nonlinearity compensation method is proposed to improve the tolerance to nonlinear distortion in an SOA based wavelength conversion system with 16 QAM and 64 QAM signals

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Dynamic Optical Networks for Data Centres and Media Production

This thesis explores all-optical networks for data centres, with a particular focus on network designs for live media production. A design for an all-optical data centre network is presented, with experimental verification of the feasibility of the network data plane. The design uses fast tunable (< 200 ns) lasers and coherent receivers across a passive optical star coupler core, forming a network capable of reaching over 1000 nodes. Experimental transmission of 25 Gb/s data across the network core, with combined wavelength switching and time division multiplexing (WS-TDM), is demonstrated. Enhancements to laser tuning time via current pre-emphasis are discussed, including experimental demonstration of fast wavelength switching (< 35 ns) of a single laser between all combinations of 96 wavelengths spaced at 50 GHz over a range wider than the optical C-band. Methods of increasing the overall network throughput by using a higher complexity modulation format are also described, along with designs for line codes to enable pulse amplitude modulation across the WS-TDM network core. The construction of an optical star coupler network core is investigated, by evaluating methods of constructing large star couplers from smaller optical coupler components. By using optical circuit switches to rearrange star coupler connectivity, the network can be partitioned, creating independent reserves of bandwidth and resulting in increased overall network throughput. Several topologies for constructing a star from optical couplers are compared, and algorithms for optimum construction methods are presented. All of the designs target strict criteria for the flexible and dynamic creation of multicast groups, which will enable future live media production workflows in data centres. The data throughput performance of the network designs is simulated under synthetic and practical media production traffic scenarios, showing improved throughput when reconfigurable star couplers are used compared to a single large star. An energy consumption evaluation shows reduced network power consumption compared to incumbent and other proposed data centre network technologies