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 the complex and variable marine environment, the navigation and localization of autonomous underwater vehicles (AUVs) are very important and challenging. When the conventional Kalman filter (KF) is applied to the cooperative localization of leader–follower AUVs, the outliers in the sensor observations will have a substantial adverse effect on the localization accuracy of the AUVs. Meanwhile, inaccurate noise covariance matrices may result in significant estimation errors. In this paper, we proposed an improved Sage–Husa adaptive extended Kalman filter (improved SHAEKF) for the cooperative localization of multi-AUVs. Firstly, the measurement anomalies were evaluated by calculating the Chi-square test statistics based on the innovation. The detection threshold was determined according to the confidence level of the Chi-square test, and the Chi-square test statistics exceeding the threshold were regarded as measurement abnormalities. When measurement anomalies occurred, the Sage–Husa adaptive extended Kalman filter algorithm was improved by suboptimal maximum a posterior estimation using weighted exponential fading memory, and the measurement noise covariance matrix was adjusted online. The numerical simulation of leader–follower multi-AUV cooperative localization verified the effectiveness of the improved SHAEKF and demonstrated that the average root mean square and the average standard deviation of the localization errors based on the improved SHAEKF were significantly reduced in the case of the presence of measurement abnormalities.

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An Adaptive Filtering Method for Cooperative Localization in Leader–Follower AUVs

A containerization resource management scheme based on deep learning was introduced into the vehicular fog computing. Experimental results demonstrate the effectiveness of the scheme by reducing the resource occupancy and decreasing the response delay significantly.

Deep Learning-Based Containerization Resource Management in Vehicular Fog Computing

We propose a scheme of optical duobinary-modulated upstream transmission system for reflective semiconductor optical amplifier-based colorless optical network units in 10-Gbps wavelength-division multiplexed passive optical network (WDM-PON), where a fiber Bragg grating (FBG) is adopted as an optical equalizer for better performance. The demodulation module is extremely simple, only needing a binary intensity modulation direct detection receiver. A better received sensitivity of −16.98  dBm at bit rate error ( BER)=1.0×10 −4 can be achieved at 120 km without FBG, and the BER at the sensitivity of −18.49  dBm can be up to 2.1×10 −5 at the transmission distance of 160 km with FBG, which demonstrates the feasibility of our proposed scheme. Moreover, it could be a high cost-effectiveness scheme for WDM-PON in the future.

10-Gbps optical duobinary signal generated by bandwidth-limited reflective semiconductor optical amplifier in colorless optical network units and compensated by fiber Bragg grating-based equalizer in optical line terminal

Proposed and demonstrated in this letter is a scheme of fiber Bragg grating (FBG) interrogation system based on thermistance and matching-FBG of temperature sensitivity enhancement. According to the relationship between the period of the matching-FBG and the resistance value of the thermistance, the scheme interrogates the wavelength-shift by adjusting the matching-FBG's temperature and detecting the resistance value of the thermistance. In measured experiments, the results show that the precision of the proposed system is 0.048nm in a range of 5.9nm.

FBG-based wavelength-shift detection system with enhanced temperature sensitivity

We quantify the fragmentation in elastic optical networks and propose a defragmentation algorithm which takes network's existing condition of spectrum allocation into consideration for the first time, we simulate the blocking probability (BP) together with spectrum continuous degree of networks after using three different algorithms. The results show our defragmentation algorithm has better performance compared to the existing strategies.

Min Zhang

Papers

An Adaptive Filtering Method for Cooperative Localization in Leader–Follower AUVs

Deep Learning-Based Containerization Resource Management in Vehicular Fog Computing

10-Gbps optical duobinary signal generated by bandwidth-limited reflective semiconductor optical amplifier in colorless optical network units and compensated by fiber Bragg grating-based equalizer in optical line terminal

FBG-based wavelength-shift detection system with enhanced temperature sensitivity

An optimal independent sets based greedy spectral defragmentation algorithm in elastic optical network