11,583 research outputs found
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
Crowdsourced Live Streaming over the Cloud
Empowered by today's rich tools for media generation and distribution, and
the convenient Internet access, crowdsourced streaming generalizes the
single-source streaming paradigm by including massive contributors for a video
channel. It calls a joint optimization along the path from crowdsourcers,
through streaming servers, to the end-users to minimize the overall latency.
The dynamics of the video sources, together with the globalized request demands
and the high computation demand from each sourcer, make crowdsourced live
streaming challenging even with powerful support from modern cloud computing.
In this paper, we present a generic framework that facilitates a cost-effective
cloud service for crowdsourced live streaming. Through adaptively leasing, the
cloud servers can be provisioned in a fine granularity to accommodate
geo-distributed video crowdsourcers. We present an optimal solution to deal
with service migration among cloud instances of diverse lease prices. It also
addresses the location impact to the streaming quality. To understand the
performance of the proposed strategies in the realworld, we have built a
prototype system running over the planetlab and the Amazon/Microsoft Cloud. Our
extensive experiments demonstrate that the effectiveness of our solution in
terms of deployment cost and streaming quality
Millimeter-wave Wireless LAN and its Extension toward 5G Heterogeneous Networks
Millimeter-wave (mmw) frequency bands, especially 60 GHz unlicensed band, are
considered as a promising solution for gigabit short range wireless
communication systems. IEEE standard 802.11ad, also known as WiGig, is
standardized for the usage of the 60 GHz unlicensed band for wireless local
area networks (WLANs). By using this mmw WLAN, multi-Gbps rate can be achieved
to support bandwidth-intensive multimedia applications. Exhaustive search along
with beamforming (BF) is usually used to overcome 60 GHz channel propagation
loss and accomplish data transmissions in such mmw WLANs. Because of its short
range transmission with a high susceptibility to path blocking, multiple number
of mmw access points (APs) should be used to fully cover a typical target
environment for future high capacity multi-Gbps WLANs. Therefore, coordination
among mmw APs is highly needed to overcome packet collisions resulting from
un-coordinated exhaustive search BF and to increase the total capacity of mmw
WLANs. In this paper, we firstly give the current status of mmw WLANs with our
developed WiGig AP prototype. Then, we highlight the great need for coordinated
transmissions among mmw APs as a key enabler for future high capacity mmw
WLANs. Two different types of coordinated mmw WLAN architecture are introduced.
One is the distributed antenna type architecture to realize centralized
coordination, while the other is an autonomous coordination with the assistance
of legacy Wi-Fi signaling. Moreover, two heterogeneous network (HetNet)
architectures are also introduced to efficiently extend the coordinated mmw
WLANs to be used for future 5th Generation (5G) cellular networks.Comment: 18 pages, 24 figures, accepted, invited paper
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Reinforcement Learning for Hybrid and Plug-In Hybrid Electric Vehicle Energy Management: Recent Advances and Prospects
A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments
In recent years, due to the unnecessary wastage of electrical energy in
residential buildings, the requirement of energy optimization and user comfort
has gained vital importance. In the literature, various techniques have been
proposed addressing the energy optimization problem. The goal of each technique
was to maintain a balance between user comfort and energy requirements such
that the user can achieve the desired comfort level with the minimum amount of
energy consumption. Researchers have addressed the issue with the help of
different optimization algorithms and variations in the parameters to reduce
energy consumption. To the best of our knowledge, this problem is not solved
yet due to its challenging nature. The gap in the literature is due to the
advancements in the technology and drawbacks of the optimization algorithms and
the introduction of different new optimization algorithms. Further, many newly
proposed optimization algorithms which have produced better accuracy on the
benchmark instances but have not been applied yet for the optimization of
energy consumption in smart homes. In this paper, we have carried out a
detailed literature review of the techniques used for the optimization of
energy consumption and scheduling in smart homes. The detailed discussion has
been carried out on different factors contributing towards thermal comfort,
visual comfort, and air quality comfort. We have also reviewed the fog and edge
computing techniques used in smart homes
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