Price Effects of Regulation: Telecommunications, Air Passenger Transport and Electricity Supply

Price Effects of Regulation draws on research undertaken at the OECD to quantify the effects of domestic regulatory regimes on prices in up to 50 economies for 3 sectors — international air passenger transport, telecommunications and electricity supply. The study finds wide variations in regulatory regimes across economies for each sector. The results suggest a positive relationship between the stringency of regulatory regimes and higher prices in each sector. For example, the bilateral system of restrictions on the number of air passenger flights between countries and the conditions under which they operate are estimated to collectively increase airfares by between 3 and 22 per cent.regulation - price effects - telecommunications - air transport - airlines - electricity - trade restrictions

Comparison of noise indicators in an urban context

Inter-Noise 2016, 45th International Congress and Exposition of Noise Control Engineering, HAMBOURG, ALLEMAGNE, 21-/08/2016 - 24/08/2016Noise is a major environmental issue, which gave birth in the last decades to the development of many engineering methods dedicated to both its estimation and mitigation. The specificity of the noise pollution problem lies in the complexity of human hearing and subjective assessment, and in the high spatiotemporal variation and rich spectral content of the noise generated by a wide variety of sources in urban context. Indicators that encompass all these dimensions are required for the description of sound environments and for the evaluation of noise mitigation strategies. This paper compares usual and more specific indicators, dedicated to environmental noise analyses, by means of a literature review. The comparison is based on the three following criteria: i) the ability of indicators to describe and physically categorize the urban sound environments, ii) the relevance of indicators for describing the perceptive appreciations of urban sound environments, iii) the ability of indicators to be estimated through classical or more advanced traffic noise estimation models. A discussion compares the pro and cons of the selected indicators in an operational scop

An Overview on Application of Machine Learning Techniques in Optical Networks

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 the paper proposing new possible research directions

Aeronautical Engineering: A special bibliography with indexes, supplement 55

This bibliography lists 260 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1975

A Very Brief Introduction to Machine Learning With Applications to Communication Systems

Given the unprecedented availability of data and computing resources, there is widespread renewed interest in applying data-driven machine learning methods to problems for which the development of conventional engineering solutions is challenged by modelling or algorithmic deficiencies. This tutorial-style paper starts by addressing the questions of why and when such techniques can be useful. It then provides a high-level introduction to the basics of supervised and unsupervised learning. For both supervised and unsupervised learning, exemplifying applications to communication networks are discussed by distinguishing tasks carried out at the edge and at the cloud segments of the network at different layers of the protocol stack

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing

Network slicing is a key technology in 5G communications system. Its purpose is to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure. Therein, demand-aware resource allocation is of significant importance to network slicing. In this paper, we consider a scenario that contains several slices in a radio access network with base stations that share the same physical resources (e.g., bandwidth or slots). We leverage deep reinforcement learning (DRL) to solve this problem by considering the varying service demands as the environment state and the allocated resources as the environment action. In order to reduce the effects of the annoying randomness and noise embedded in the received service level agreement (SLA) satisfaction ratio (SSR) and spectrum efficiency (SE), we primarily propose generative adversarial network-powered deep distributional Q network (GAN-DDQN) to learn the action-value distribution driven by minimizing the discrepancy between the estimated action-value distribution and the target action-value distribution. We put forward a reward-clipping mechanism to stabilize GAN-DDQN training against the effects of widely-spanning utility values. Moreover, we further develop Dueling GAN-DDQN, which uses a specially designed dueling generator, to learn the action-value distribution by estimating the state-value distribution and the action advantage function. Finally, we verify the performance of the proposed GAN-DDQN and Dueling GAN-DDQN algorithms through extensive simulations