49,820 research outputs found
Characterisation of radio access network slicing scenarios with 5G QoS provisioning
5G systems are envisaged to support a wide range of application scenarios with variate requirements. To handle this heterogeneity, 5G architecture includes network slicing capabilities that facilitate the
partitioning of a single network infrastructure into multiple logical networks on top of it, each tailored to a
given use case and provided with appropriate isolation and Quality of Service (QoS) characteristics. Network
slicing also enables the use of multi-tenancy networks, in which the same infrastructure can be shared by
multiple tenants by associating one slice to each tenant, easing the cost-effective deployment and operation
of future 5G networks. Concerning the Radio Access Network (RAN), slicing is particularly challenging as it
implies the configuration of multiple RAN behaviors over a common pool of radio resources. In this context,
this work presents a Markov model for RAN slicing capable of characterizing diverse Radio Resource
Management (RRM) strategies in multi-tenant and multi-service 5G scenarios including both guaranteed and
non-guaranteed bit rate services. The proposed model captures the fact that different radio links from diverse
users can experience distinct spectral efficiencies, which enables an accurate modeling of the randomness
associated with the actual resource requirements. The model is evaluated in a multi-tenant scenario in urban
micro cell and rural macro cell environments to illustrate the impact of the considered RRM polices in the
QoS provisioning.Peer ReviewedPostprint (published version
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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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