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.

/pdf/light-emitting-diodes-ubzde6g9qc.pdf

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.

/pdf/a-comprehensive-survey-on-machine-learning-for-networking-3t5h3kbpe8.pdf

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.

/pdf/an-overview-on-application-of-machine-learning-techniques-in-3eel2cbu3o.pdf

An Overview on Application of Machine Learning Techniques in Optical Networks

Based on analytical methods, we show that when Internet protocol (IP) traffic is loaded directly over differential phase-shift keying (DPSK) wavelength division multiplexing (WDM) systems with perfect dispersion compensation, the burstiness of IP packets has significant effects on the cross-phase modulation (XPM)-induced nonlinear phase noise, and therefore exerts an influence on the packet-error probability performance of the systems. It is demonstrated that the packet-error probability is dependent on the traffic source models. Moreover, we can shorten the packet and diminish the burstiness of the IP traffic to get a small packet-error probability. The results given are helpful for the design of DPSK WDM systems.

Impact of Internet protocol traffic on differential phase-shift keying wavelength division multiplexing systems

We experimentally demonstrate a 10-Gb/s operation of bandwidth-limited RSOA, where a fiber Bragg grating (FBG) is used as an optical equalizer to improve the RSOA modulation bandwidth from 1.2 GHz to 8 GHz and suppress the pattern dependence.

Upstream Transmission of 10-Gb/s NRZ Signal Generated by Bandwidth-Limited RSOA Utilizing Optical Equalizer

We propose a novel OpenFlow based recovery mechanism for Flexi-Grid optical networks, called partial protection full bandwidth restoration (PP-FBR), based on the implement of common protection and restoration mechanisms. Our experimental results demonstrate feasibility of our recovery mechanisms and the PP-FBR’s high spectrum efficiency.

Partial Protection with Full Bandwidth Restoration by OpenFlow-based Control Plane in Flexi-Grid Optical Networks

A big-data-driven dynamic clustering and load balancing scheme of virtual base stations for 5G fronthaul network is proposed. The comparison of load balancing factor shows that the optimization effect of the scheme is obvious.

Big-Data-Driven Dynamic Clustering and Load Balancing of Virtual Base Stations for 5G Fronthaul Network

The error probability of differential quadrature phase-shift keying (DQPSK) systems is analyzed considering both self- and cross-phase modulation (SPM and XPM)-induced nonlinear phase noise. XPM-induced nonlinear phase noise is treated here as Gaussian distributed phase noise. In DQPSK systems where group velocity dispersion (GVD) is perfectly compensated span after span, the influence of XPM-induced nonlinear phase noise on the error bribability performance can be dominant compared with that of the SPM-induced nonlinear phase noise. The 10-Gb/s systems, whose walkoff length is larger than that of the 40 Gb/s systems, are more sensitive to XPM-induced nonlinear phase noise.

Min Zhang

Papers

Impact of Internet protocol traffic on differential phase-shift keying wavelength division multiplexing systems

Upstream Transmission of 10-Gb/s NRZ Signal Generated by Bandwidth-Limited RSOA Utilizing Optical Equalizer

Partial Protection with Full Bandwidth Restoration by OpenFlow-based Control Plane in Flexi-Grid Optical Networks

Big-Data-Driven Dynamic Clustering and Load Balancing of Virtual Base Stations for 5G Fronthaul Network

Impact of nonlinear phase noise on direct detection DQPSK systems