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

A novel single-polarization single-mode (SPSM) photonic crystal fiber (PCF) is proposed and analyzed through a full-vector finite element method (FEM) with anisotropic perfectly matched layer (PML) Numerical results show that the proposed fiber is a low-loss SPSM-PCF within wavelength range from 1370 to 1610nm, in which only the slow-axis mode is guided and the confinement loss is below 01dB/km

A novel single-polarization single-mode photonic crystal fiber

A degenerated MAC layer feedback-repeat reliable transmission scheme was proposed and experimentally demonstrated for LiFi networking with B5G/6G. The experimental results of end-to-end packet loss ratio precedes 1/10000 (equals to 99.999% reliability).

99.999% Transmission Reliability based on MAC layer Feedback-repeat Scheme for LiFi Networking with B5G/6G

In this paper, we propose a novel all-optical AND gate using frequency shift accompanied by cross-phase modulation in semiconductor optical amplifier. Theoretical model is built to analyze the performance of the proposed gate. Through numerical simulations, we find the bandwidth of the optical band filter can be optimized to improve the quality of the output signals. The AND gate with short pulse-width and high energy of the control signals is adaptive to high-speed optical packet-switched and label-switched network.

A novel all-optical AND gate using frequency shift accompanied by cross-phase modulation in semiconductor optical amplifier

With the development of large scale multi-layer and multi-domain optical networks, path computation becomes more complicated as complex policies and constraints are to be considered and dynamic routing becomes one of the key issues in GMPLS/ASON networks. A novel Dual Routing Engine Architecture in Multi-layer and multi-domain Scalable and Constraint-Aware Policy-Enabled optical networks (DREAMSCAPE), exploiting the advantages of both GMPLS control plane and Path Computation Element (PCE), is introduced to improve the performance of routing in multi-layer and multi-domain optical networks in this paper. In the architecture, the cooperation of Group Engine (GE) and Unit Engine (UE) makes it effective to optimize path computation especially under complex multi-constraints. The functional architectures of dual routing engines are described in the paper. Based on this novel architecture, a testbed with more than 1000 GMPLS-based control nodes has been built in our lab using virtual machine technology, and two path computation approaches have been implemented on the testbed. The latest results are experimentally demonstrated on the testbed, and some future research issues are presented in the conclusion.

Experimental demonstration of DREAMSCAPE with more than 1000 GMPLS-based control nodes

We propose a novel scheme for high-speed all-optical AND gate, utilizing the effect of cross-phase modulation in semiconductor optical amplifier (SOA) based Mach-Zehnder interferometer (MZI). Theory analysis and numerical simulations are performed to validate our proposal. The results are useful for design and optimization of SOA-MZI-based AND gate.

http://ieeexplore.ieee.org/iel5/8586/27215/01209148.pdf

Min Zhang

Papers

A novel single-polarization single-mode photonic crystal fiber

99.999% Transmission Reliability based on MAC layer Feedback-repeat Scheme for LiFi Networking with B5G/6G

A novel all-optical AND gate using frequency shift accompanied by cross-phase modulation in semiconductor optical amplifier

Experimental demonstration of DREAMSCAPE with more than 1000 GMPLS-based control nodes

Simple AND gate implementation for optical packet switching networks