303 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
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
View additional affiliations
View references (107)
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
Fair Quality of Experience (QoE) Measurements Related with Networking Technologies
[Invited Talk] Eighth International Conference on Wired/Wireless Internet Communications (June 1-3, Luleå, Sweden)Proceeding of: 8th International Conference, WWIC 2010, Lulea, Sweden, June 1-3, 2010This paper addresses the topic of Fair QoE measurements in networking. The research of new solutions in networking is oriented to improve the user experience. Any application or service can be im- proved and the deployment of new solutions is mandatory to get the user satisfaction. However, different solutions exist; thus, it is necessary to select the most suitable ones. Nevertheless, this selection is difficult to make since the QoE is subjective and the comparison among different technologies is not trivial. The aim of this paper is to give an overview on how to perform fair QoE measurements to facilitate the study and re- search of new networking solutions and paradigms. However, previously to address this problem, an overview about how networking affects to the QoE is provided.This work has been funded by the CONTENT NoE from the European Commission (FP6- 2005-IST-41) and by the Ministry of Science and Innovation under the CON- PARTE project (MEC, TEC2007-67966-C03-03/TCM) and T2C2 project grant (TIN2008-06739-C04-01).Publicad
Self-Sustaining Caching Stations: Towards Cost-Effective 5G-Enabled Vehicular Networks
In this article, we investigate the cost-effective 5G-enabled vehicular
networks to support emerging vehicular applications, such as autonomous
driving, in-car infotainment and location-based road services. To this end,
self-sustaining caching stations (SCSs) are introduced to liberate on-road base
stations from the constraints of power lines and wired backhauls. Specifically,
the cache-enabled SCSs are powered by renewable energy and connected to core
networks through wireless backhauls, which can realize "drop-and-play"
deployment, green operation, and low-latency services. With SCSs integrated, a
5G-enabled heterogeneous vehicular networking architecture is further proposed,
where SCSs are deployed along roadside for traffic offloading while
conventional macro base stations (MBSs) provide ubiquitous coverage to
vehicles. In addition, a hierarchical network management framework is designed
to deal with high dynamics in vehicular traffic and renewable energy, where
content caching, energy management and traffic steering are jointly
investigated to optimize the service capability of SCSs with balanced power
demand and supply in different time scales. Case studies are provided to
illustrate SCS deployment and operation designs, and some open research issues
are also discussed.Comment: IEEE Communications Magazine, to appea
A Survey of Deep Learning for Data Caching in Edge Network
The concept of edge caching provision in emerging 5G and beyond mobile
networks is a promising method to deal both with the traffic congestion problem
in the core network as well as reducing latency to access popular content. In
that respect end user demand for popular content can be satisfied by
proactively caching it at the network edge, i.e, at close proximity to the
users. In addition to model based caching schemes learning-based edge caching
optimizations has recently attracted significant attention and the aim
hereafter is to capture these recent advances for both model based and data
driven techniques in the area of proactive caching. This paper summarizes the
utilization of deep learning for data caching in edge network. We first outline
the typical research topics in content caching and formulate a taxonomy based
on network hierarchical structure. Then, a number of key types of deep learning
algorithms are presented, ranging from supervised learning to unsupervised
learning as well as reinforcement learning. Furthermore, a comparison of
state-of-the-art literature is provided from the aspects of caching topics and
deep learning methods. Finally, we discuss research challenges and future
directions of applying deep learning for cachin
- …