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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
Big Data Caching for Networking: Moving from Cloud to Edge
In order to cope with the relentless data tsunami in wireless networks,
current approaches such as acquiring new spectrum, deploying more base stations
(BSs) and increasing nodes in mobile packet core networks are becoming
ineffective in terms of scalability, cost and flexibility. In this regard,
context-aware G networks with edge/cloud computing and exploitation of
\emph{big data} analytics can yield significant gains to mobile operators. In
this article, proactive content caching in G wireless networks is
investigated in which a big data-enabled architecture is proposed. In this
practical architecture, vast amount of data is harnessed for content popularity
estimation and strategic contents are cached at the BSs to achieve higher
users' satisfaction and backhaul offloading. To validate the proposed solution,
we consider a real-world case study where several hours of mobile data traffic
is collected from a major telecom operator in Turkey and a big data-enabled
analysis is carried out leveraging tools from machine learning. Based on the
available information and storage capacity, numerical studies show that several
gains are achieved both in terms of users' satisfaction and backhaul
offloading. For example, in the case of BSs with of content ratings
and Gbyte of storage size ( of total library size), proactive
caching yields of users' satisfaction and offloads of the
backhaul.Comment: accepted for publication in IEEE Communications Magazine, Special
Issue on Communications, Caching, and Computing for Content-Centric Mobile
Network
Big Data Meets Telcos: A Proactive Caching Perspective
Mobile cellular networks are becoming increasingly complex to manage while
classical deployment/optimization techniques and current solutions (i.e., cell
densification, acquiring more spectrum, etc.) are cost-ineffective and thus
seen as stopgaps. This calls for development of novel approaches that leverage
recent advances in storage/memory, context-awareness, edge/cloud computing, and
falls into framework of big data. However, the big data by itself is yet
another complex phenomena to handle and comes with its notorious 4V: velocity,
voracity, volume and variety. In this work, we address these issues in
optimization of 5G wireless networks via the notion of proactive caching at the
base stations. In particular, we investigate the gains of proactive caching in
terms of backhaul offloadings and request satisfactions, while tackling the
large-amount of available data for content popularity estimation. In order to
estimate the content popularity, we first collect users' mobile traffic data
from a Turkish telecom operator from several base stations in hours of time
interval. Then, an analysis is carried out locally on a big data platform and
the gains of proactive caching at the base stations are investigated via
numerical simulations. It turns out that several gains are possible depending
on the level of available information and storage size. For instance, with 10%
of content ratings and 15.4 Gbyte of storage size (87% of total catalog size),
proactive caching achieves 100% of request satisfaction and offloads 98% of the
backhaul when considering 16 base stations.Comment: 8 pages, 5 figure
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
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