78,601 research outputs found
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
Communication Theoretic Data Analytics
Widespread use of the Internet and social networks invokes the generation of
big data, which is proving to be useful in a number of applications. To deal
with explosively growing amounts of data, data analytics has emerged as a
critical technology related to computing, signal processing, and information
networking. In this paper, a formalism is considered in which data is modeled
as a generalized social network and communication theory and information theory
are thereby extended to data analytics. First, the creation of an equalizer to
optimize information transfer between two data variables is considered, and
financial data is used to demonstrate the advantages. Then, an information
coupling approach based on information geometry is applied for dimensionality
reduction, with a pattern recognition example to illustrate the effectiveness.
These initial trials suggest the potential of communication theoretic data
analytics for a wide range of applications.Comment: Published in IEEE Journal on Selected Areas in Communications, Jan.
201
Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks
The fifth generation of cellular networks (5G) will rely on edge cloud
deployments to satisfy the ultra-low latency demand of future applications. In
this paper, we argue that such deployments can also be used to enable advanced
data-driven and Machine Learning (ML) applications in mobile networks. We
propose an edge-controller-based architecture for cellular networks and
evaluate its performance with real data from hundreds of base stations of a
major U.S. operator. In this regard, we will provide insights on how to
dynamically cluster and associate base stations and controllers, according to
the global mobility patterns of the users. Then, we will describe how the
controllers can be used to run ML algorithms to predict the number of users in
each base station, and a use case in which these predictions are exploited by a
higher-layer application to route vehicular traffic according to network Key
Performance Indicators (KPIs). We show that the prediction accuracy improves
when based on machine learning algorithms that rely on the controllers' view
and, consequently, on the spatial correlation introduced by the user mobility,
with respect to when the prediction is based only on the local data of each
single base station.Comment: 15 pages, 10 figures, 5 tables. IEEE Transactions on Mobile Computin
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