4,569 research outputs found
Final report on the evaluation of RRM/CRRM algorithms
Deliverable public del projecte EVERESTThis deliverable provides a definition and a complete evaluation of the RRM/CRRM algorithms selected in D11 and D15, and evolved and refined on an iterative process. The evaluation will be carried out by means of simulations using the simulators provided at D07, and D14.Preprin
Resource Allocation Frameworks for Network-coded Layered Multimedia Multicast Services
The explosive growth of content-on-the-move, such as video streaming to
mobile devices, has propelled research on multimedia broadcast and multicast
schemes. Multi-rate transmission strategies have been proposed as a means of
delivering layered services to users experiencing different downlink channel
conditions. In this paper, we consider Point-to-Multipoint layered service
delivery across a generic cellular system and improve it by applying different
random linear network coding approaches. We derive packet error probability
expressions and use them as performance metrics in the formulation of resource
allocation frameworks. The aim of these frameworks is both the optimization of
the transmission scheme and the minimization of the number of broadcast packets
on each downlink channel, while offering service guarantees to a predetermined
fraction of users. As a case of study, our proposed frameworks are then adapted
to the LTE-A standard and the eMBMS technology. We focus on the delivery of a
video service based on the H.264/SVC standard and demonstrate the advantages of
layered network coding over multi-rate transmission. Furthermore, we establish
that the choice of both the network coding technique and resource allocation
method play a critical role on the network footprint, and the quality of each
received video layer.Comment: IEEE Journal on Selected Areas in Communications - Special Issue on
Fundamental Approaches to Network Coding in Wireless Communication Systems.
To appea
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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