25,357 research outputs found
Big Data Meets Telcos: A Proactive Caching Perspective
Mobile cellular networks are becoming increasingly complex to manage while
classical deployment/optimization techniques and current solutions (i.e., cell
densification, acquiring more spectrum, etc.) are cost-ineffective and thus
seen as stopgaps. This calls for development of novel approaches that leverage
recent advances in storage/memory, context-awareness, edge/cloud computing, and
falls into framework of big data. However, the big data by itself is yet
another complex phenomena to handle and comes with its notorious 4V: velocity,
voracity, volume and variety. In this work, we address these issues in
optimization of 5G wireless networks via the notion of proactive caching at the
base stations. In particular, we investigate the gains of proactive caching in
terms of backhaul offloadings and request satisfactions, while tackling the
large-amount of available data for content popularity estimation. In order to
estimate the content popularity, we first collect users' mobile traffic data
from a Turkish telecom operator from several base stations in hours of time
interval. Then, an analysis is carried out locally on a big data platform and
the gains of proactive caching at the base stations are investigated via
numerical simulations. It turns out that several gains are possible depending
on the level of available information and storage size. For instance, with 10%
of content ratings and 15.4 Gbyte of storage size (87% of total catalog size),
proactive caching achieves 100% of request satisfaction and offloads 98% of the
backhaul when considering 16 base stations.Comment: 8 pages, 5 figure
Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors
The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone
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
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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
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