3,696 research outputs found
Compressed Sensing based Dynamic PSD Map Construction in Cognitive Radio Networks
In the context of spectrum sensing in cognitive radio networks, collaborative spectrum sensing has been proposed as a way to overcome multipath and shadowing, and hence increasing the reliability of the sensing. Due to the high amount of information to be transmitted, a dynamic compressive sensing approach is proposed to map the PSD estimate to a sparse domain which is then transmitted to the fusion center. In this regard, CRs send a compressed version of their estimated PSD to the fusion center, whose job is to reconstruct the PSD estimates of the CRs, fuse them, and make a global decision on the availability of the spectrum in space and frequency domains at a given time. The proposed compressive sensing based method considers the dynamic nature of the PSD map, and uses this dynamicity in order to decrease the amount of data needed to be transmitted between CR sensors’ and the fusion center. By using the proposed method, an acceptable PSD map for cognitive radio purposes can be achieved by only 20 % of full data transmission between sensors and master node. Also, simulation results show the robustness of the proposed method against the channel variations, diverse compression ratios and processing times in comparison with static methods
Energy efficient scheme based on simultaneous transmission of the local decisions in cooperative spectrum sensing
A common concern regarding cooperative spectrum sensing (CSS) schemes is the occupied bandwidth and the energy consumption during the transmissions of sensing information to the fusion center over the reporting control channels. This concern is intensified if the number of cooperating secondary users in the network is large. This article presents a new fusion strategy for a CSS scheme, aiming at increasing the energy efficiency of a recently proposed bandwidth-efficient fusion scheme. Analytical results and computational simulations unveil a high increase in energy efficiency when compared with the original approach, yet achieving better performances in some situations, and lower implementation complexity
Collaborative Spectrum Sensing from Sparse Observations in Cognitive Radio Networks
Spectrum sensing, which aims at detecting spectrum holes, is the precondition
for the implementation of cognitive radio (CR). Collaborative spectrum sensing
among the cognitive radio nodes is expected to improve the ability of checking
complete spectrum usage. Due to hardware limitations, each cognitive radio node
can only sense a relatively narrow band of radio spectrum. Consequently, the
available channel sensing information is far from being sufficient for
precisely recognizing the wide range of unoccupied channels. Aiming at breaking
this bottleneck, we propose to apply matrix completion and joint sparsity
recovery to reduce sensing and transmitting requirements and improve sensing
results. Specifically, equipped with a frequency selective filter, each
cognitive radio node senses linear combinations of multiple channel information
and reports them to the fusion center, where occupied channels are then decoded
from the reports by using novel matrix completion and joint sparsity recovery
algorithms. As a result, the number of reports sent from the CRs to the fusion
center is significantly reduced. We propose two decoding approaches, one based
on matrix completion and the other based on joint sparsity recovery, both of
which allow exact recovery from incomplete reports. The numerical results
validate the effectiveness and robustness of our approaches. In particular, in
small-scale networks, the matrix completion approach achieves exact channel
detection with a number of samples no more than 50% of the number of channels
in the network, while joint sparsity recovery achieves similar performance in
large-scale networks.Comment: 12 pages, 11 figure
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
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
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