1,004 research outputs found
Wideband Spectrum Sensing in Cognitive Radio Networks
Spectrum sensing is an essential enabling functionality for cognitive radio
networks to detect spectrum holes and opportunistically use the under-utilized
frequency bands without causing harmful interference to legacy networks. This
paper introduces a novel wideband spectrum sensing technique, called multiband
joint detection, which jointly detects the signal energy levels over multiple
frequency bands rather than consider one band at a time. The proposed strategy
is efficient in improving the dynamic spectrum utilization and reducing
interference to the primary users. The spectrum sensing problem is formulated
as a class of optimization problems in interference limited cognitive radio
networks. By exploiting the hidden convexity in the seemingly non-convex
problem formulations, optimal solutions for multiband joint detection are
obtained under practical conditions. Simulation results show that the proposed
spectrum sensing schemes can considerably improve the system performance. This
paper establishes important principles for the design of wideband spectrum
sensing algorithms in cognitive radio networks
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
Eigenvalue-based Cyclostationary Spectrum Sensing Using Multiple Antennas
In this paper, we propose a signal-selective spectrum sensing method for
cognitive radio networks and specifically targeted for receivers with
multiple-antenna capability. This method is used for detecting the presence or
absence of primary users based on the eigenvalues of the cyclic covariance
matrix of received signals. In particular, the cyclic correlation significance
test is used to detect a specific signal-of-interest by exploiting knowledge of
its cyclic frequencies. The analytical threshold for achieving constant false
alarm rate using this detection method is presented, verified through
simulations, and shown to be independent of both the number of samples used and
the noise variance, effectively eliminating the dependence on accurate noise
estimation. The proposed method is also shown, through numerical simulations,
to outperform existing multiple-antenna cyclostationary-based spectrum sensing
algorithms under a quasi-static Rayleigh fading channel, in both spatially
correlated and uncorrelated noise environments. The algorithm also has
significantly lower computational complexity than these other approaches.Comment: 6 pages, 6 figures, accepted to IEEE GLOBECOM 201
Spectrum Sensing Techniqes in Cognitive Radio: Cyclostationary Method
Cognitive Radios promise to be a major shift in wireless communications based on developing a novel approach which attempt to reduce spectrum scarcity that growing up in the past and waited to increase in the future. Since formulating stages for increasing interest in wireless application proves to be
extremely challenging, it is growing rapidly. Initially this growth leads to huge demand for the radio spectrum. The novelty of this approach needs to optimize the spectrum utilization and find the efficient way for sharing the radio frequencies through spectrum sensing process. Spectrum sensing is one of the most significant
tasks that allow cognitive radio functionality to implement and one of the most challenging tasks. A main challenge in sensing process arises from the fact that, detecting signals with a very low SNR in back ground of noise or severely masked by interference in specific time based on high reliability. This thesis describes the fundamental cognitive radio system aspect based on design and implementation by connecting between the theoretical and practical issue. Efficient method for
sensing and detecting are studied and discussed through two fast methods of computing the spectral correlation density function, the FFT Accumulation Method and the Strip Spectral Correlation Algorithm. Several simulations have been performed to show the ability and performance of studied algorithms.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format
Cooperative Wideband Spectrum Sensing Based on Joint Sparsity
COOPERATIVE WIDEBAND SPECTRUM SENSING BASED ON JOINT SPARSITY
By Ghazaleh Jowkar, Master of Science
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University
Virginia Commonwealth University 2017
Major Director: Dr. Ruixin Niu, Associate Professor of Department of Electrical and Computer Engineering
In this thesis, the problem of wideband spectrum sensing in cognitive radio (CR) networks using sub-Nyquist sampling and sparse signal processing techniques is investigated. To mitigate multi-path fading, it is assumed that a group of spatially dispersed SUs collaborate for wideband spectrum sensing, to determine whether or not a channel is occupied by a primary user (PU). Due to the underutilization of the spectrum by the PUs, the spectrum matrix has only a small number of non-zero rows. In existing state-of-the-art approaches, the spectrum sensing problem was solved using the low-rank matrix completion technique involving matrix nuclear-norm minimization. Motivated by the fact that the spectrum matrix is not only low-rank, but also sparse, a spectrum sensing approach is proposed based on minimizing a mixed-norm of the spectrum matrix instead of low-rank matrix completion to promote the joint sparsity among the column vectors of the spectrum matrix. Simulation results are obtained, which demonstrate that the proposed mixed-norm minimization approach outperforms the low-rank matrix completion based approach, in terms of the PU detection performance. Further we used mixed-norm minimization model in multi time frame detection. Simulation results shows that increasing the number of time frames will increase the detection performance, however, by increasing the number of time frames after a number of times the performance decrease dramatically
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