3,204 research outputs found
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
Reconfigurable Wireless Networks
Driven by the advent of sophisticated and ubiquitous applications, and the
ever-growing need for information, wireless networks are without a doubt
steadily evolving into profoundly more complex and dynamic systems. The user
demands are progressively rampant, while application requirements continue to
expand in both range and diversity. Future wireless networks, therefore, must
be equipped with the ability to handle numerous, albeit challenging
requirements. Network reconfiguration, considered as a prominent network
paradigm, is envisioned to play a key role in leveraging future network
performance and considerably advancing current user experiences. This paper
presents a comprehensive overview of reconfigurable wireless networks and an
in-depth analysis of reconfiguration at all layers of the protocol stack. Such
networks characteristically possess the ability to reconfigure and adapt their
hardware and software components and architectures, thus enabling flexible
delivery of broad services, as well as sustaining robust operation under highly
dynamic conditions. The paper offers a unifying framework for research in
reconfigurable wireless networks. This should provide the reader with a
holistic view of concepts, methods, and strategies in reconfigurable wireless
networks. Focus is given to reconfigurable systems in relatively new and
emerging research areas such as cognitive radio networks, cross-layer
reconfiguration and software-defined networks. In addition, modern networks
have to be intelligent and capable of self-organization. Thus, this paper
discusses the concept of network intelligence as a means to enable
reconfiguration in highly complex and dynamic networks. Finally, the paper is
supported with several examples and case studies showing the tremendous impact
of reconfiguration on wireless networks.Comment: 28 pages, 26 figures; Submitted to the Proceedings of the IEEE (a
special issue on Reconfigurable Systems
A study of research trends and issues in wireless ad hoc networks
Ad hoc network enables network creation on the fly without support of any
predefined infrastructure. The spontaneous erection of networks in anytime and
anywhere fashion enables development of various novel applications based on ad
hoc networks. However, at the same ad hoc network presents several new
challenges. Different research proposals have came forward to resolve these
challenges. This chapter provides a survey of current issues, solutions and
research trends in wireless ad hoc network. Even though various surveys are
already available on the topic, rapid developments in recent years call for an
updated account on this topic. The chapter has been organized as follows. In
the first part of the chapter, various ad hoc network's issues arising at
different layers of TCP/IP protocol stack are presented. An overview of
research proposals to address each of these issues is also provided. The second
part of the chapter investigates various emerging models of ad hoc networks,
discusses their distinctive properties and highlights various research issues
arising due to these properties. We specifically provide discussion on ad hoc
grids, ad hoc clouds, wireless mesh networks and cognitive radio ad hoc
networks. The chapter ends with presenting summary of the current research on
ad hoc network, ignored research areas and directions for further research
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
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
Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues
As a promising paradigm to reduce both capital and operating expenditures,
the cloud radio access network (C-RAN) has been shown to provide high spectral
efficiency and energy efficiency. Motivated by its significant theoretical
performance gains and potential advantages, C-RANs have been advocated by both
the industry and research community. This paper comprehensively surveys the
recent advances of C-RANs, including system architectures, key techniques, and
open issues. The system architectures with different functional splits and the
corresponding characteristics are comprehensively summarized and discussed. The
state-of-the-art key techniques in C-RANs are classified as: the fronthaul
compression, large-scale collaborative processing, and channel estimation in
the physical layer; and the radio resource allocation and optimization in the
upper layer. Additionally, given the extensiveness of the research area, open
issues and challenges are presented to spur future investigations, in which the
involvement of edge cache, big data mining, social-aware device-to-device,
cognitive radio, software defined network, and physical layer security for
C-RANs are discussed, and the progress of testbed development and trial test
are introduced as well.Comment: 27 pages, 11 figure
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
Effective Capacity in Wireless Networks: A Comprehensive Survey
Low latency applications, such as multimedia communications, autonomous
vehicles, and Tactile Internet are the emerging applications for
next-generation wireless networks, such as 5th generation (5G) mobile networks.
Existing physical-layer channel models, however, do not explicitly consider
quality-of-service (QoS) aware related parameters under specific delay
constraints. To investigate the performance of low-latency applications in
future networks, a new mathematical framework is needed. Effective capacity
(EC), which is a link-layer channel model with QoS-awareness, can be used to
investigate the performance of wireless networks under certain statistical
delay constraints. In this paper, we provide a comprehensive survey on existing
works, that use the EC model in various wireless networks. We summarize the
work related to EC for different networks such as cognitive radio networks
(CRNs), cellular networks, relay networks, adhoc networks, and mesh networks.
We explore five case studies encompassing EC operation with different design
and architectural requirements. We survey various delay-sensitive applications
such as voice and video with their EC analysis under certain delay constraints.
We finally present the future research directions with open issues covering EC
maximization
Base Station ON-OFF Switching in 5G Wireless Networks: Approaches and Challenges
To achieve the expected 1000x data rates under the exponential growth of
traffic demand, a large number of base stations (BS) or access points (AP) will
be deployed in the fifth generation (5G) wireless systems, to support high data
rate services and to provide seamless coverage. Although such BSs are expected
to be small-scale with lower power, the aggregated energy consumption of all
BSs would be remarkable, resulting in increased environmental and economic
concerns. In existing cellular networks, turning off the under-utilized BSs is
an efficient approach to conserve energy while preserving the quality of
service (QoS) of mobile users. However, in 5G systems with new physical layer
techniques and the highly heterogeneous network architecture, new challenges
arise in the design of BS ON-OFF switching strategies. In this article, we
begin with a discussion on the inherent technical challenges of BS ON-OFF
switching. We then provide a comprehensive review of recent advances on
switching mechanisms in different application scenarios. Finally, we present
open research problems and conclude the paper.Comment: Appear to IEEE Wireless Communications, 201
A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions
The fifth generation (5G) wireless network technology is to be standardized
by 2020, where main goals are to improve capacity, reliability, and energy
efficiency, while reducing latency and massively increasing connection density.
An integral part of 5G is the capability to transmit touch perception type
real-time communication empowered by applicable robotics and haptics equipment
at the network edge. In this regard, we need drastic changes in network
architecture including core and radio access network (RAN) for achieving
end-to-end latency on the order of 1 ms. In this paper, we present a detailed
survey on the emerging technologies to achieve low latency communications
considering three different solution domains: RAN, core network, and caching.
We also present a general overview of 5G cellular networks composed of software
defined network (SDN), network function virtualization (NFV), caching, and
mobile edge computing (MEC) capable of meeting latency and other 5G
requirements.Comment: Accepted in IEEE Communications Surveys and Tutorial
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