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

We propose NOMA-OFDM for PON, which offers a high throughput, high resistance to fiber dispersion and a higher system capacity. Bidirectional NOMA-OFDM-PON transmission is experimentally demonstrated.

Experimental Demonstration of an NOMA Scheme for Passive Optical Network

A scheme of long-reach RSOA based WDM-PON is proposed and simulated. By using 10Gbit/s high power efficient coded downstream signals such as RZ, IRZ and PPM, the coverage of WDM-PON is extended to 100km.

http://ieeexplore.ieee.org/iel5/6412853/6413749/06414012.pdf

100-km Long-Reach WDM-PON Using High Power Efficient On-Off Keying Codes in Downstream

Abstract Light fidelity (LiFi) would be a potential optical wireless communications technology for future mixed-spectrum 5G/6G indoor attocell network, which uses light-emitting diodes based as an optical antenna and offers Gbits capacity. For this scenario, a dynamic handover scheme based on bidirectional visible light communications (VLC) channel is proposed, which uses the uplink light sources to characterize terminal’s movement status for the mobility and make the handover decision in contrast to the unidirectional links. The uplink reference signal received power and the movement state are examined, and the created algorithm is executed in the bidirectional VLC test bed. The experimental results show that the proposed method will reduce the terminal’s handover rate by up to 45% and increase the optical network throughput by up to 26% at the experimental data rate compared with the skipping handover scheme for indoor LiFi network. 

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A dynamic handover scheme based on bidirectional VLC channel in multi-user attocell networks

We experimentally demonstrate one-to-five quadrature phase-shift keying (QPSK) wavelength multicasting based on four-wave mixing in bulk semiconductor optical amplifier. The input 25 Gb/s nonreturn-to-zero QPSK signal is successfully multicast to five new wavelengths with all information preserved. All the multicast channels are with a power penalty less than 1.1 dB at a bit error rate (BER) of 10-3. A characterization of the conversion efficiency in terms of pump and signal powers using the BER as figure of merit is also presented, the results indicate that the pump and signal powers should be optimized to eliminate the introduced deleterious nonlinear components.

QPSK wavelength multicasting based on four-wave mixing in semiconductor optical amplifier

A deep learning-based modeling technique is proposed for dual-polarization optical fiber channel in PM-QPSK system. All the values of R-square and similarity are $\gt0.98$ and $\gt0.99$.

Min Zhang

Papers

Experimental Demonstration of an NOMA Scheme for Passive Optical Network

100-km Long-Reach WDM-PON Using High Power Efficient On-Off Keying Codes in Downstream

A dynamic handover scheme based on bidirectional VLC channel in multi-user attocell networks

QPSK wavelength multicasting based on four-wave mixing in semiconductor optical amplifier

Optical Fiber Channel Modeling Method Using Multi-BiLSTM for PM-QPSK Systems