Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Light-Emitting Diodes. (Monographs in Electrical and Electronic Engineering.) By A. A. Bergh and P. J. Dean. Pp. viii+591. (Clarendon: Oxford; Oxford University: London, 1976.) £22.

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Light-emitting diodes

Machine Learning (ML) has been enjoying an unprecedented surge in applications that solve problems and enable automation in diverse domains. Primarily, this is due to the explosion in the availability of data, significant improvements in ML techniques, and advancement in computing capabilities. Undoubtedly, ML has been applied to various mundane and complex problems arising in network operation and management. There are various surveys on ML for specific areas in networking or for specific network technologies. This survey is original, since it jointly presents the application of diverse ML techniques in various key areas of networking across different network technologies. In this way, readers will benefit from a comprehensive discussion on the different learning paradigms and ML techniques applied to fundamental problems in networking, including traffic prediction, routing and classification, congestion control, resource and fault management, QoS and QoE management, and network security. Furthermore, this survey delineates the limitations, give insights, research challenges and future opportunities to advance ML in networking. Therefore, this is a timely contribution of the implications of ML for networking, that is pushing the barriers of autonomic network operation and management.

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A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

Radio access technologies for cellular mobile communications are typically characterized by multiple access schemes, e.g., frequency division multiple access (FDMA), time division multiple access (TDMA), code division multiple access (CDMA), and OFDMA. In the 4th generation (4G) mobile communication systems such as Long-Term Evolution (LTE) (Au et al., Uplink contention based SCMA for 5G radio access. Globecom Workshops (GC Wkshps), 2014. doi:10.1109/GLOCOMW.2014.7063547) and LTE-Advanced (Baracca et al., IEEE Trans. Commun., 2011. doi:10.1109/TCOMM.2011.121410.090252; Barry et al., Digital Communication, Kluwer, Dordrecht, 2004), standardized by the 3rd Generation Partnership Project (3GPP), orthogonal multiple access based on OFDMA or single carrier (SC)-FDMA is adopted. Orthogonal multiple access was a reasonable choice for achieving good system-level throughput performance with simple single-user detection. However, considering the trend in 5G, achieving significant gains in capacity and system throughput performance is a high priority requirement in view of the recent exponential increase in the volume of mobile traffic. In addition the proposed system should be able to support enhanced delay-sensitive high-volume services such as video streaming and cloud computing. Another high-level target of 5G is reduced cost, higher energy efficiency and robustness against emergencies.

Non-Orthogonal Multiple Access (NOMA) for cellular future radio access

Today’s telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users’ behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, machine learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing, and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude this paper proposing new possible research directions.

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An Overview on Application of Machine Learning Techniques in Optical Networks

In this paper, the all-optical logic exclusive OR using integrated SOA-based Mach-Zehnder interferometer with differential modulation scheme is proved to exceed the operation speed limit of the logic XOR with original modulation effectively through numerical analysis for the first time. The SOA Parameters, the signal parameters and the time delay are discussed and designed to optimize the switching performance of the proposed logic XOR. The operation of the proposed logic XOR with 80Gb/s RZ pseudorandom bit sequences is simulated with fairly high performance.

Ultrafast all-optical logic XOR using SOA-MZI with differential modulation scheme

A Bayesian neural network-based uncertainty analysis technique is proposed for failure prediction in optical transport network, which can not only achieve F1-score up to 99.5%, but also give an uncertainty quantification for the prediction results.

Uncertainty Analysis for Failure Prediction in Optical Transport Network Using Bayesian Neural Network

A scheme of format conversion between OOK signal and optical quadrature phase shift keying (QPSK)/16-ary quadrature amplitude modulation (16QAM) signal based on cross phase modulation (XPM) effect in semiconductor optical amplifiers (SOA) is proposed. Theoretical analysis and simulations of the format conversion scheme are conducted to validate the feasibility of the proposal. In this proposal, the QPSK signal is generated after passed an SOA, and then the 16QAM signal is generated based on XPM in a single SOA-MZI after two QPSK signal coupled. The performance and the optimal design of the 10Gbit/s format conversion system under various key parameters of SOAs are evaluated and discussed. The receiver sensitivity of the converted QPSK signal and 16QAM signal after detection is -32dBm and -28.5dBm with BtB at BER of 10 -9 , respectively. Simulation results present useful to enable interconnection between backbone network and access network.

A scheme format conversion from NRZ-OOK to QPSK/16QAM signal based on XPM in SOA-MZI

Based on the cross-validation and grid-search, a parameter optimization method for SVM-based decision processor is proposed. Compared with random selection scheme, the performance is improved significantly, and the BER reaches lower than 3.0×10-4.

Min Zhang

Papers

Ultrafast all-optical logic XOR using SOA-MZI with differential modulation scheme

Uncertainty Analysis for Failure Prediction in Optical Transport Network Using Bayesian Neural Network

A scheme format conversion from NRZ-OOK to QPSK/16QAM signal based on XPM in SOA-MZI

Optimized SVM-based Decision Processor for 16QAM Coherent Optical Systems to Mitigate NLPN

A low-cost indoor visible light communication link enabling 13 Mbit/s OOK communication with common white LED