24,183 research outputs found
Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks
Cognitive radio has been widely considered as one of the prominent solutions
to tackle the spectrum scarcity. While the majority of existing research has
focused on single-band cognitive radio, multiband cognitive radio represents
great promises towards implementing efficient cognitive networks compared to
single-based networks. Multiband cognitive radio networks (MB-CRNs) are
expected to significantly enhance the network's throughput and provide better
channel maintenance by reducing handoff frequency. Nevertheless, the wideband
front-end and the multiband spectrum access impose a number of challenges yet
to overcome. This paper provides an in-depth analysis on the recent
advancements in multiband spectrum sensing techniques, their limitations, and
possible future directions to improve them. We study cooperative communications
for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also
investigate several limits and tradeoffs of various design parameters for
MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that
differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE
Journal, Special Issue on Future Radio Spectrum Access, March 201
Hard Decision Cooperative Spectrum Sensing Based on Estimating the Noise Uncertainty Factor
Spectrum Sensing (SS) is one of the most challenging issues in Cognitive
Radio (CR) systems. Cooperative Spectrum Sensing (CSS) is proposed to enhance
the detection reliability of a Primary User (PU) in fading environments. In
this paper, we propose a hard decision based CSS algorithm using energy
detection with taking into account the noise uncertainty effect. In the
proposed algorithm, two dynamic thresholds are toggled based on predicting the
current PU activity, which can be successfully expected using a simple
successive averaging process with time. Also, their values are evaluated using
an estimated value of the noise uncertainty factor. These dynamic thresholds
are used to compensate the noise uncertainty effect and increase (decrease) the
probability of detection (false alarm), respectively. Theoretical analysis is
performed on the proposed algorithm to deduce its enhanced false alarm and
detection probabilities compared to the conventional hard decision CSS.
Moreover, simulation analysis is used to confirm the theoretical claims and
prove the high performance of the proposed scheme compared to the conventional
CSS using different fusion rules.Comment: 5 pages, 4 figures, IEEE International Conference on Computer
Engineering and Systems (ICCES 2015). arXiv admin note: text overlap with
arXiv:1505.0558
Quantum sensing
"Quantum sensing" describes the use of a quantum system, quantum properties
or quantum phenomena to perform a measurement of a physical quantity.
Historical examples of quantum sensors include magnetometers based on
superconducting quantum interference devices and atomic vapors, or atomic
clocks. More recently, quantum sensing has become a distinct and rapidly
growing branch of research within the area of quantum science and technology,
with the most common platforms being spin qubits, trapped ions and flux qubits.
The field is expected to provide new opportunities - especially with regard to
high sensitivity and precision - in applied physics and other areas of science.
In this review, we provide an introduction to the basic principles, methods and
concepts of quantum sensing from the viewpoint of the interested
experimentalist.Comment: 45 pages, 13 figures. Submitted to Rev. Mod. Phy
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