DocumentCode :
740669
Title :
Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence
Author :
Zorzi, Michele ; Zanella, Andrea ; Testolin, Alberto ; De Filippo De Grazia, Michele ; Zorzi, Marco
Author_Institution :
Department of Information Engineering, University of Padua, Padua, Italy
Volume :
3
fYear :
2015
fDate :
7/7/1905 12:00:00 AM
Firstpage :
1512
Lastpage :
1530
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 networks.
Keywords :
Cognitive networks; Communication networks; Deep learning; Hierarchical networks; Optimization; Cognitive networks; deep learning; hierarchical generative models; optimization;
fLanguage :
English
Journal_Title :
Access, IEEE
Publisher :
ieee
ISSN :
2169-3536
Type :
jour
DOI :
10.1109/ACCESS.2015.2471178
Filename :
7217798
Link To Document :
بازگشت