1,624 research outputs found
Car-to-Cloud Communication Traffic Analysis Based on the Common Vehicle Information Model
Although connectivity services have been introduced already today in many of
the most recent car models, the potential of vehicles serving as highly mobile
sensor platform in the Internet of Things (IoT) has not been sufficiently
exploited yet. The European AutoMat project has therefore defined an open
Common Vehicle Information Model (CVIM) in combination with a cross-industry,
cloud-based big data marketplace. Thereby, vehicle sensor data can be leveraged
for the design of entirely new services even beyond traffic-related
applications (such as localized weather forecasts). This paper focuses on the
prediction of the achievable data rate making use of an analytical model based
on empirical measurements. For an in-depth analysis, the CVIM has been
integrated in a vehicle traffic simulator to produce CVIM-complaint data
streams as a result of the individual behavior of each vehicle (speed, brake
activity, steering activity, etc.). In a next step, a simulation of vehicle
traffic in a realistically modeled, large-area street network has been used in
combination with a cellular Long Term Evolution (LTE) network to determine the
cumulated amount of data produced within each network cell. As a result, a new
car-to-cloud communication traffic model has been derived, which quantifies the
data rate of aggregated car-to-cloud data producible by vehicles depending on
the current traffic situations (free flow and traffic jam). The results provide
a reference for network planning and resource scheduling for car-to-cloud type
services in the context of smart cities
A survey of machine learning techniques applied to self organizing cellular networks
In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future
Context-aware Cluster Based Device-to-Device Communication to Serve Machine Type Communications
Billions of Machine Type Communication (MTC) devices are foreseen to be
deployed in next ten years and therefore potentially open a new market for next
generation wireless network. However, MTC applications have different
characteristics and requirements compared with the services provided by legacy
cellular networks. For instance, an MTC device sporadically requires to
transmit a small data packet containing information generated by sensors. At
the same time, due to the massive deployment of MTC devices, it is inefficient
to charge their batteries manually and thus a long battery life is required for
MTC devices. In this sense, legacy networks designed to serve human-driven
traffics in real time can not support MTC efficiently. In order to improve the
availability and battery life of MTC devices, context-aware device-to-device
(D2D) communication is exploited in this paper. By applying D2D communication,
some MTC users can serve as relays for other MTC users who experience bad
channel conditions. Moreover, signaling schemes are also designed to enable the
collection of context information and support the proposed D2D communication
scheme. Last but not least, a system level simulator is implemented to evaluate
the system performance of the proposed technologies and a large performance
gain is shown by the numerical results
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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