Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network

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