1,820 research outputs found
Opportunistic Spectrum Sharing in Dynamic Access Networks: Deployment Challenges, Optimizations, Solutions, and Open Issues
In this paper, we investigate the issue of spectrum assignment in CRNs and
examine various opportunistic spectrum access approaches proposed in the
literature. We provide insight into the efficiency of such approaches and their
ability to attain their design objectives. We discuss the factors that impact
the selection of the appropriate operating channel(s), including the important
interaction between the cognitive linkquality conditions and the time-varying
nature of PRNs. Protocols that consider such interaction are described. We
argue that using best quality channels does not achieve the maximum possible
throughput in CRNs (does not provide the best spectrum utilization). The impact
of guard bands on the design of opportunistic spectrum access protocols is also
investigated. Various complementary techniques and optimization methods are
underlined and discussed, including the utilization of variablewidth spectrum
assignment, resource virtualization, full-duplex capability, cross-layer
design, beamforming and MIMO technology, cooperative communication, network
coding, discontinuousOFDM technology, and software defined radios. Finally, we
highlight several directions for future research in this field
Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning
The ability to intelligently utilize resources to meet the need of growing
diversity in services and user behavior marks the future of wireless
communication systems. Intelligent wireless communications aims at enabling the
system to perceive and assess the available resources, to autonomously learn to
adapt to the perceived wireless environment, and to reconfigure its operating
mode to maximize the utility of the available resources. The perception
capability and reconfigurability are the essential features of cognitive radio
while modern machine learning techniques project great potential in system
adaptation. In this paper, we discuss the development of the cognitive radio
technology and machine learning techniques and emphasize their roles in
improving spectrum and energy utility of wireless communication systems. We
describe the state-of-the-art of relevant techniques, covering spectrum sensing
and access approaches and powerful machine learning algorithms that enable
spectrum- and energy-efficient communications in dynamic wireless environments.
We also present practical applications of these techniques and identify further
research challenges in cognitive radio and machine learning as applied to the
existing and future wireless communication systems
QoS Provisioning for Multimedia Transmission in Cognitive Radio Networks
In cognitive radio (CR) networks, the perceived reduction of application
layer quality of service (QoS), such as multimedia distortion, by secondary
users may impede the success of CR technologies. Most previous work in CR
networks ignores application layer QoS. In this paper we take an integrated
design approach to jointly optimize multimedia intra refreshing rate, an
application layer parameter, together with access strategy, and spectrum
sensing for multimedia transmission in a CR system with time varying wireless
channels. Primary network usage and channel gain are modeled as a finite state
Markov process. With channel sensing and channel state information errors, the
system state cannot be directly observed. We formulate the QoS optimization
problem as a partially observable Markov decision process (POMDP). A low
complexity dynamic programming framework is presented to obtain the optimal
policy. Simulation results show the effectiveness of the proposed scheme
Transport Protocols in Cognitive Radio Networks: A Survey
Cognitive radio networks (CRNs) have emerged as a promising solution to
enhance spectrum utilization by using unused or less used spectrum in radio
environments. The basic idea of CRNs is to allow secondary users (SUs) access
to licensed spectrum, under the condition that the interference perceived by
the primary users (PUs) is minimal. In CRNs, the channel availability is
uncertainty due to the existence of PUs, resulting in intermittent
communication. Transmission control protocol (TCP) performance may
significantly degrade in such conditions. To address the challenges, some
transport protocols have been proposed for reliable transmission in CRNs. In
this paper we survey the state-of-the-art transport protocols for CRNs. We
firstly highlight the unique aspects of CRNs, and describe the challenges of
transport protocols in terms of PU behavior, spectrum sensing, spectrum
changing and TCP mechanism itself over CRNs. Then, we provide a summary and
comparison of existing transport protocols for CRNs. Finally, we discuss
several open issues and research challenges. To the best of our knowledge, our
work is the first survey on transport protocols for CRNs.Comment: to appear in KSII Transactions on Internet and Information System
Security and Privacy Challenges in Cognitive Wireless Sensor Networks
Wireless sensor networks (WSNs) have attracted a lot of interest in the
research community due to their potential applicability in a wide range of
real-world practical applications. However, due to the distributed nature and
their deployments in critical applications without human interventions and
sensitivity and criticality of data communicated, these networks are vulnerable
to numerous security and privacy threats that can adversely affect their
performance. These issues become even more critical in cognitive wireless
sensor networks (CWSNs) in which the sensor nodes have the capabilities of
changing their transmission and reception parameters according to the radio
environment under which they operate in order to achieve reliable and efficient
communication and optimum utilization of the network resources. This chapter
presents a comprehensive discussion on the security and privacy issues in CWSNs
by identifying various security threats in these networks and various defense
mechanisms to counter these vulnerabilities. Various types of attacks on CWSNs
are categorized under different classes based on their natures and targets, and
corresponding to each attack class, appropriate security mechanisms are also
discussed. Some critical research issues on security and privacy in CWSNs are
also identified.Comment: 36 pages, 4 figures, 2 tables. The book chapter is accepted for
publication in 201
Cross-layer Design in Cognitive Radio Standards
The growing demand for wireless applications and services on the one hand,
and limited available radio spectrum on the other hand have made cognitive
radio (CR) a promising solution for future mobile networks. It has attracted
considerable attention by academia and industry since its introduction in 1999
and several relevant standards have been developed within the last decade.
Cognitive radio is based on four main functions, spanning across more than one
layer of OSI model. Therefore, solutions based on cognitive radio technology
require cross layer (CL) designs for optimum performance. This article briefly
reviews the basics of cognitive radio technology as an introduction and
highlights the need for cross layer design in systems deploying CR technology.
Then some of the published standards with CL characteristics are outlined in a
later section, and in the final section some research examples of cross layer
design ideas based on the existing CR standards conclude this article
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
On Scalable Video Streaming over Cognitive Radio Cellular and Ad Hoc Networks
Video content delivery over wireless networks is expected to grow drastically
in the coming years. In this paper, we investigate the challenging problem of
video over cognitive radio (CR) networks. Although having high potential, this
problem brings about a new level of technical challenges. After reviewing
related work, we first address the problem of video over infrastructure-based
CR networks, and then extend the problem to video over non-infrastructure-based
ad hoc CR networks. We present formulations of cross-layer optimization
problems as well as effective algorithms to solving the problems. The proposed
algorithms are analyzed with respect to their optimality and validate with
simulations
Capacity Analysis in Multi-Radio Multi-Channel Cognitive Radio Networks: A Small World Perspective
Cognitive radio (CR) has emerged as a promising technology to improve
spectrum utilization. Capacity analysis is very useful in investigating the
ultimate performance limits for wireless networks. Meanwhile, with increasing
potential future applications for the CR systems, it is necessary to explore
the limitations on their capacity in a dynamic spectrum access environment.
However, due to spectrum sharing in cognitive radio networks (CRNs), the
capacity of the secondary network (SRN) is much more difficult to analyze than
that of traditional wireless networks. To overcome this difficulty, in this
paper we introduce a novel solution based on small world model to analyze the
capacity of SRN. First, we propose a new method of shortcut creation for CRNs,
which is based on connectivity ratio. Also, a new channel assignment algorithm
is proposed, which jointly considers the available time and transmission time
of the channels. And then, we derive the capacity of SRN based on the small
world model over multi-radio multi-channel (MRMC) environment. The simulation
results show that our proposed scheme can obtain a higher capacity and smaller
latency compared with traditional schemes in MRMC CRNs.Comment: Wireless Pers Commun(2014)79:2209-222
Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey
The Internet of Things (IoT) is expected to require more effective and
efficient wireless communications than ever before. For this reason, techniques
such as spectrum sharing, dynamic spectrum access, extraction of signal
intelligence and optimized routing will soon become essential components of the
IoT wireless communication paradigm. Given that the majority of the IoT will be
composed of tiny, mobile, and energy-constrained devices, traditional
techniques based on a priori network optimization may not be suitable, since
(i) an accurate model of the environment may not be readily available in
practical scenarios; (ii) the computational requirements of traditional
optimization techniques may prove unbearable for IoT devices. To address the
above challenges, much research has been devoted to exploring the use of
machine learning to address problems in the IoT wireless communications domain.
This work provides a comprehensive survey of the state of the art in the
application of machine learning techniques to address key problems in IoT
wireless communications with an emphasis on its ad hoc networking aspect.
First, we present extensive background notions of machine learning techniques.
Then, by adopting a bottom-up approach, we examine existing work on machine
learning for the IoT at the physical, data-link and network layer of the
protocol stack. Thereafter, we discuss directions taken by the community
towards hardware implementation to ensure the feasibility of these techniques.
Additionally, before concluding, we also provide a brief discussion of the
application of machine learning in IoT beyond wireless communication. Finally,
each of these discussions is accompanied by a detailed analysis of the related
open problems and challenges.Comment: Ad Hoc Networks Journa
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