Back to Search
Start Over
COBANETS: A new paradigm for cognitive communications systems
- Source :
- ICNC
- Publication Year :
- 2016
- Publisher :
- Institute of Electrical and Electronics Engineers Inc., 2016.
-
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.
- Subjects :
- Computer Networks and Communications
Social Sciences (miscellaneous)
Management science
business.industry
Computer science
Deep learning
Probabilistic logic
Control reconfiguration
Network virtualization
External Data Representation
Telecommunications network
Human–computer interaction
Scalability
Position paper
Artificial intelligence
business
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- ICNC
- Accession number :
- edsair.doi.dedup.....7e5ed850a39438a22b14cc5fb60774e9