3,263 research outputs found
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
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
Next Generation M2M Cellular Networks: Challenges and Practical Considerations
In this article, we present the major challenges of future machine-to-machine
(M2M) cellular networks such as spectrum scarcity problem, support for
low-power, low-cost, and numerous number of devices. As being an integral part
of the future Internet-of-Things (IoT), the true vision of M2M communications
cannot be reached with conventional solutions that are typically cost
inefficient. Cognitive radio concept has emerged to significantly tackle the
spectrum under-utilization or scarcity problem. Heterogeneous network model is
another alternative to relax the number of covered users. To this extent, we
present a complete fundamental understanding and engineering knowledge of
cognitive radios, heterogeneous network model, and power and cost challenges in
the context of future M2M cellular networks
Full-Duplex Cognitive Radio: A New Design Paradigm for Enhancing Spectrum Usage
With the rapid growth of demand for ever-increasing data rate, spectrum
resources have become more and more scarce. As a promising technique to
increase the efficiency of the spectrum utilization, cognitive radio (CR)
technique has the great potential to meet such a requirement by allowing
un-licensed users to coexist in licensed bands. In conventional CR systems, the
spectrum sensing is performed at the beginning of each time slot before the
data transmission. This unfortunately results in two major problems: 1)
transmission time reduction due to sensing, and 2) sensing accuracy impairment
due to data transmission. To tackle these problems, in this paper we present a
new design paradigm for future CR by exploring the full-duplex (FD) techniques
to achieve the simultaneous spectrum sensing and data transmission. With FD
radios equipped at the secondary users (SUs), SUs can simultaneously sense and
access the vacant spectrum, and thus, significantly improve sensing
performances and meanwhile increase data transmission efficiency. The aim of
this article is to transform the promising conceptual framework into the
practical wireless network design by addressing a diverse set of challenges
such as protocol design and theoretical analysis. Several application scenarios
with FD enabled CR are elaborated, and key open research directions and novel
algorithms in these systems are discussed
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