20,693 research outputs found
AACT: Application-Aware Cooperative Time Allocation for Internet of Things
As the number of Internet of Things (IoT) devices keeps increasing, data is
required to be communicated and processed by these devices at unprecedented
rates. Cooperation among wireless devices by exploiting Device-to-Device (D2D)
connections is promising, where aggregated resources in a cooperative setup can
be utilized by all devices, which would increase the total utility of the
setup. In this paper, we focus on the resource allocation problem for
cooperating IoT devices with multiple heterogeneous applications. In
particular, we develop Application-Aware Cooperative Time allocation (AACT)
framework, which optimizes the time that each application utilizes the
aggregated system resources by taking into account heterogeneous device
constraints and application requirements. AACT is grounded on the concept of
Rolling Horizon Control (RHC) where decisions are made by iteratively solving a
convex optimization problem over a moving control window of estimated system
parameters. The simulation results demonstrate significant performance gains
A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications
As the explosive growth of smart devices and the advent of many new
applications, traffic volume has been growing exponentially. The traditional
centralized network architecture cannot accommodate such user demands due to
heavy burden on the backhaul links and long latency. Therefore, new
architectures which bring network functions and contents to the network edge
are proposed, i.e., mobile edge computing and caching. Mobile edge networks
provide cloud computing and caching capabilities at the edge of cellular
networks. In this survey, we make an exhaustive review on the state-of-the-art
research efforts on mobile edge networks. We first give an overview of mobile
edge networks including definition, architecture and advantages. Next, a
comprehensive survey of issues on computing, caching and communication
techniques at the network edge is presented respectively. The applications and
use cases of mobile edge networks are discussed. Subsequently, the key enablers
of mobile edge networks such as cloud technology, SDN/NFV and smart devices are
discussed. Finally, open research challenges and future directions are
presented as well
A Survey on Device-to-Device Communication in Cellular Networks
Device-to-Device (D2D) communication was initially proposed in cellular
networks as a new paradigm to enhance network performance. The emergence of new
applications such as content distribution and location-aware advertisement
introduced new use-cases for D2D communications in cellular networks. The
initial studies showed that D2D communication has advantages such as increased
spectral efficiency and reduced communication delay. However, this
communication mode introduces complications in terms of interference control
overhead and protocols that are still open research problems. The feasibility
of D2D communications in LTE-A is being studied by academia, industry, and the
standardization bodies. To date, there are more than 100 papers available on
D2D communications in cellular networks and, there is no survey on this field.
In this article, we provide a taxonomy based on the D2D communicating spectrum
and review the available literature extensively under the proposed taxonomy.
Moreover, we provide new insights to the over-explored and under-explored areas
which lead us to identify open research problems of D2D communication in
cellular networks.Comment: 18 pages; 8 figures; Accepted for publication in IEEE Communications
Surveys and Tutorial
Energy-Performance Trade-offs in Mobile Data Transfers
By year 2020, the number of smartphone users globally will reach 3 Billion
and the mobile data traffic (cellular + WiFi) will exceed PC internet traffic
the first time. As the number of smartphone users and the amount of data
transferred per smartphone grow exponentially, limited battery power is
becoming an increasingly critical problem for mobile devices which increasingly
depend on network I/O. Despite the growing body of research in power management
techniques for the mobile devices at the hardware layer as well as the lower
layers of the networking stack, there has been little work focusing on saving
energy at the application layer for the mobile systems during network I/O. In
this paper, to the best of our knowledge, we are first to provide an in depth
analysis of the effects of application layer data transfer protocol parameters
on the energy consumption of mobile phones. We show that significant energy
savings can be achieved with application layer solutions at the mobile systems
during data transfer with no or minimal performance penalty. In many cases,
performance increase and energy savings can be achieved simultaneously
Exploiting Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing
In this article we propose a novel Device-to-Device (D2D) Crowd framework for
5G mobile edge computing, where a massive crowd of devices at the network edge
leverage the network-assisted D2D collaboration for computation and
communication resource sharing among each other. A key objective of this
framework is to achieve energy-efficient collaborative task executions at
network-edge for mobile users. Specifically, we first introduce the D2D Crowd
system model in details, and then formulate the energy-efficient D2D Crowd task
assignment problem by taking into account the necessary constraints. We next
propose a graph matching based optimal task assignment policy, and further
evaluate its performance through extensive numerical study, which shows a
superior performance of more than 50% energy consumption reduction over the
case of local task executions. Finally, we also discuss the directions of
extending the D2D Crowd framework by taking into variety of application
factors.Comment: Xu Chen, Lingjun Pu, Lin Gao, Weigang Wu, and Di Wu, "Exploiting
Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing,"
accepted by IEEE Wireless Communications, 201
On Green Energy Powered Cognitive Radio Networks
Green energy powered cognitive radio (CR) network is capable of liberating
the wireless access networks from spectral and energy constraints. The
limitation of the spectrum is alleviated by exploiting cognitive networking in
which wireless nodes sense and utilize the spare spectrum for data
communications, while dependence on the traditional unsustainable energy is
assuaged by adopting energy harvesting (EH) through which green energy can be
harnessed to power wireless networks. Green energy powered CR increases the
network availability and thus extends emerging network applications. Designing
green CR networks is challenging. It requires not only the optimization of
dynamic spectrum access but also the optimal utilization of green energy. This
paper surveys the energy efficient cognitive radio techniques and the
optimization of green energy powered wireless networks. Existing works on
energy aware spectrum sensing, management, and sharing are investigated in
detail. The state of the art of the energy efficient CR based wireless access
network is discussed in various aspects such as relay and cooperative radio and
small cells. Envisioning green energy as an important energy resource in the
future, network performance highly depends on the dynamics of the available
spectrum and green energy. As compared with the traditional energy source, the
arrival rate of green energy, which highly depends on the environment of the
energy harvesters, is rather random and intermittent. To optimize and adapt the
usage of green energy according to the opportunistic spectrum availability, we
discuss research challenges in designing cognitive radio networks which are
powered by energy harvesters
A Survey on QoE-oriented Wireless Resources Scheduling
Future wireless systems are expected to provide a wide range of services to
more and more users. Advanced scheduling strategies thus arise not only to
perform efficient radio resource management, but also to provide fairness among
the users. On the other hand, the users' perceived quality, i.e., Quality of
Experience (QoE), is becoming one of the main drivers within the schedulers
design. In this context, this paper starts by providing a comprehension of what
is QoE and an overview of the evolution of wireless scheduling techniques.
Afterwards, a survey on the most recent QoE-based scheduling strategies for
wireless systems is presented, highlighting the application/service of the
different approaches reported in the literature, as well as the parameters that
were taken into account for QoE optimization. Therefore, this paper aims at
helping readers interested in learning the basic concepts of QoE-oriented
wireless resources scheduling, as well as getting in touch with its current
research frontier.Comment: Revised version: updated according to the most recent related
literature; added references; corrected typo
Recent Advances in Fog Radio Access Networks: Performance Analysis and Radio Resource Allocation
As a promising paradigm for the fifth generation wireless communication (5G)
system, the fog radio access network (F-RAN) has been proposed as an advanced
socially-aware mobile networking architecture to provide high spectral
efficiency (SE) while maintaining high energy efficiency (EE) and low latency.
Recent advents are advocated to the performance analysis and radio resource
allocation, both of which are fundamental issues to make F-RANs successfully
rollout. This article comprehensively summarizes the recent advances of the
performance analysis and radio resource allocation in F-RANs. Particularly, the
advanced edge cache and adaptive model selection schemes are presented to
improve SE and EE under maintaining a low latency level. The radio resource
allocation strategies to optimize SE and EE in F-RANs are respectively
proposed. A few open issues in terms of the F-RAN based 5G architecture and the
social-awareness technique are identified as well
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
This paper presents a comprehensive literature review on applications of deep
reinforcement learning in communications and networking. Modern networks, e.g.,
Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, become
more decentralized and autonomous. In such networks, network entities need to
make decisions locally to maximize the network performance under uncertainty of
network environment. Reinforcement learning has been efficiently used to enable
the network entities to obtain the optimal policy including, e.g., decisions or
actions, given their states when the state and action spaces are small.
However, in complex and large-scale networks, the state and action spaces are
usually large, and the reinforcement learning may not be able to find the
optimal policy in reasonable time. Therefore, deep reinforcement learning, a
combination of reinforcement learning with deep learning, has been developed to
overcome the shortcomings. In this survey, we first give a tutorial of deep
reinforcement learning from fundamental concepts to advanced models. Then, we
review deep reinforcement learning approaches proposed to address emerging
issues in communications and networking. The issues include dynamic network
access, data rate control, wireless caching, data offloading, network security,
and connectivity preservation which are all important to next generation
networks such as 5G and beyond. Furthermore, we present applications of deep
reinforcement learning for traffic routing, resource sharing, and data
collection. Finally, we highlight important challenges, open issues, and future
research directions of applying deep reinforcement learning.Comment: 37 pages, 13 figures, 6 tables, 174 reference paper
Energy-Efficient Mobile Network I/O Optimization at the Application Layer
Mobile data traffic (cellular + WiFi) will exceed PC Internet traffic by
2020. As the number of smartphone users and the amount of data transferred per
smartphone grow exponentially, limited battery power is becoming an
increasingly critical problem for mobile devices which depend on the network
I/O. Despite the growing body of research in power management techniques for
the mobile devices at the hardware layer as well as the lower layers of the
networking stack, there has been little work focusing on saving energy at the
application layer for the mobile systems during network I/O. In this paper, to
the best of our knowledge, we are first to provide an in-depth analysis of the
effects of application-layer data transfer protocol parameters on the energy
consumption of mobile phones. We propose a novel model, called FastHLA, that
can achieve significant energy savings at the application layer during mobile
network I/O without sacrificing the performance. In many cases, our model
achieves performance increase and energy saving simultaneously.Comment: arXiv admin note: text overlap with arXiv:1805.03970 and substantial
text overlap with arXiv:1707.0682
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