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 position 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 the 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 to past and current research efforts in this area, the technical approach depicted 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.
Original language | English |
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Title of host publication | 2016 International Conference on Computing, Networking and Communications, ICNC 2016 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Print) | 9781467385794 |
DOIs | |
Publication status | Published - Mar 23 2016 |
Event | International Conference on Computing, Networking and Communications, ICNC 2016 - Kauai, United States Duration: Feb 15 2016 → Feb 18 2016 |
Other
Other | International Conference on Computing, Networking and Communications, ICNC 2016 |
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Country/Territory | United States |
City | Kauai |
Period | 2/15/16 → 2/18/16 |
ASJC Scopus subject areas
- Computer Science Applications
- Computer Networks and Communications
- Social Sciences (miscellaneous)