2,058 research outputs found
Sensing-Throughput Tradeoff for Superior Selective Reporting-based Spectrum Sensing in Energy Harvesting HCRNs
In this paper, we investigate the performance of conventional cooperative
sensing (CCS) and superior selective reporting (SSR)-based cooperative sensing
in an energy harvesting-enabled heterogeneous cognitive radio network (HCRN).
In particular, we derive expressions for the achievable throughput of both
schemes and formulate nonlinear integer programming problems, in order to find
the throughput-optimal set of spectrum sensors scheduled to sense a particular
channel, given primary user (PU) interference and energy harvesting
constraints. Furthermore, we present novel solutions for the underlying
optimization problems based on the cross-entropy (CE) method, and compare the
performance with exhaustive search and greedy algorithms. Finally, we discuss
the tradeoff between the average achievable throughput of the SSR and CCS
schemes, and highlight the regime where the SSR scheme outperforms the CCS
scheme. Notably, we show that there is an inherent tradeoff between the channel
available time and the detection accuracy. Our numerical results show that, as
the number of spectrum sensors increases, the channel available time gains a
higher priority in an HCRN, as opposed to detection accuracy
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
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
RF-Powered Cognitive Radio Networks: Technical Challenges and Limitations
The increasing demand for spectral and energy efficient communication
networks has spurred a great interest in energy harvesting (EH) cognitive radio
networks (CRNs). Such a revolutionary technology represents a paradigm shift in
the development of wireless networks, as it can simultaneously enable the
efficient use of the available spectrum and the exploitation of radio frequency
(RF) energy in order to reduce the reliance on traditional energy sources. This
is mainly triggered by the recent advancements in microelectronics that puts
forward RF energy harvesting as a plausible technique in the near future. On
the other hand, it is suggested that the operation of a network relying on
harvested energy needs to be redesigned to allow the network to reliably
function in the long term. To this end, the aim of this survey paper is to
provide a comprehensive overview of the recent development and the challenges
regarding the operation of CRNs powered by RF energy. In addition, the
potential open issues that might be considered for the future research are also
discussed in this paper.Comment: 8 pages, 2 figures, 1 table, Accepted in IEEE Communications Magazin
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
Green Cellular Networks: A Survey, Some Research Issues and Challenges
Energy efficiency in cellular networks is a growing concern for cellular
operators to not only maintain profitability, but also to reduce the overall
environment effects. This emerging trend of achieving energy efficiency in
cellular networks is motivating the standardization authorities and network
operators to continuously explore future technologies in order to bring
improvements in the entire network infrastructure. In this article, we present
a brief survey of methods to improve the power efficiency of cellular networks,
explore some research issues and challenges and suggest some techniques to
enable an energy efficient or "green" cellular network. Since base stations
consume a maximum portion of the total energy used in a cellular system, we
will first provide a comprehensive survey on techniques to obtain energy
savings in base stations. Next, we discuss how heterogeneous network deployment
based on micro, pico and femto-cells can be used to achieve this goal. Since
cognitive radio and cooperative relaying are undisputed future technologies in
this regard, we propose a research vision to make these technologies more
energy efficient. Lastly, we explore some broader perspectives in realizing a
"green" cellular network technologyComment: 16 pages, 5 figures, 2 table
Throughput Analysis of Wireless Powered Cognitive Radio Networks with Compressive Sensing and Matrix Completion
In this paper, we consider a cognitive radio network in which energy
constrained secondary users (SUs) can harvest energy from the randomly deployed
power beacons (PBs). A new frame structure is proposed for the considered
network. A wireless power transfer (WPT) model and a compressive spectrum
sensing model are introduced. In the WPT model, a new WPT scheme is proposed,
and the closed-form expressions for the power outage probability are derived.
In compressive spectrum sensing model, two scenarios are considered: 1) Single
SU, and 2) Multiple SUs. In the single SU scenario, in order to reduce the
energy consumption at the SU, compressive sensing technique which enables
sub-Nyquist sampling is utilized. In the multiple SUs scenario, cooperative
spectrum sensing (CSS) is performed with adopting low-rank matrix completion
technique to obtain the complete matrix at the fusion center. Throughput
optimizations of the secondary network are formulated into two linear
constrained problems, which aim to maximize the throughput of single SU and the
CSS networks, respectively. Three methods are provided to obtain the maximal
throughput of secondary network by optimizing the time slots allocation and the
transmit power. Simulation results show that: 1) Multiple SUs scenario can
achieve lower power outage probability than single SU scenario; and 2) The
optimal throughput can be improved by implementing compressive spectrum sensing
in the proposed frame structure design.Comment: 11 pages, 8 figure
Probability Density Function Estimation in OFDM Transmitter and Receiver in Radio Cognitive Networks based on Recurrent Neural Network
The most important problem in telecommunication is bandwidth limitation due
to the uncontrolled growth of wireless technology. Deploying dynamic spectrum
access techniques is one of the procedures provided for efficient use of
bandwidth. In recent years, cognitive radio network introduced as a tool for
efficient use of spectrum. These radios are able to use radio resources by
recognizing surroundings via sensors and signal operations that means use these
resources only when authorized users do not use their spectrum. Secondary users
are unauthorized ones that must avoid from interferences with primary users
transmission. Secondary users must leave channel due to preventing damages to
primary users whenever these users discretion. In this article, spectrum
opportunities prediction based on Recurrent Neural Network for bandwidth
optimization and reducing the amount of energy by predicting spectrum holes
discovery for quality of services optimization proposed in OFDM-based cognitive
radio network based on probability density function. The result of the
simulation represent acceptable value of SNR and bandwidth optimization in
these networks that allows secondary users to taking spectrum and sending data
without collision and overlapping with primary users.Comment: OFDM, Cognitive Radio Networks, Recurrent Neural Network, Probability
Density Functio
Joint Cooperative Spectrum Sensing and MAC Protocol Design for Multi-channel Cognitive Radio Networks
In this paper, we propose a semi-distributed cooperative spectrum sen sing
(SDCSS) and channel access framework for multi-channel cognitive radio networks
(CRNs). In particular, we c onsider a SDCSS scheme where secondary users (SUs)
perform sensing and exchange sensing outcomes with ea ch other to locate
spectrum holes. In addition, we devise the p -persistent CSMA-based cognitive
MAC protocol integrating the SDCSS to enable efficient spectrum sharing among
SUs. We then perform throughput analysis and develop an algorithm to determine
the spectrum sensing and access parameters to maximize the throughput for a
given allocation of channel sensing sets. Moreover, we consider the spectrum
sensing set optimization problem for SUs to maxim ize the overall system
throughput. We present both exhaustive search and low-complexity greedy
algorithms to determine the sensing sets for SUs and analyze their complexity.
We also show how our design and analysis can be extended to consider reporting
errors. Finally, extensive numerical results are presented to demonstrate the
sig nificant performance gain of our optimized design framework with respect to
non-optimized designs as well as the imp acts of different protocol parameters
on the throughput performance.Comment: accepted for publication EURASIP Journal on Wireless Communications
and Networking, 201
A Comprehensive Survey of Potential Game Approaches to Wireless Networks
Potential games form a class of non-cooperative games where unilateral
improvement dynamics are guaranteed to converge in many practical cases. The
potential game approach has been applied to a wide range of wireless network
problems, particularly to a variety of channel assignment problems. In this
paper, the properties of potential games are introduced, and games in wireless
networks that have been proven to be potential games are comprehensively
discussed.Comment: 44 pages, 6 figures, to appear in IEICE Transactions on
Communications, vol. E98-B, no. 9, Sept. 201
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