3,332 research outputs found
Collaborative spectrum sensing in cognitive radio networks
The radio frequency (RF) spectrum is a scarce natural resource, currently regulated by government
agencies. With the explosive emergence of wireless applications, the demands for the
RF spectrum are constantly increasing. On the other hand, it has been reported that localised
temporal and geographic spectrum utilisation efficiency is extremely low. Cognitive radio is an
innovative technology designed to improve spectrum utilisation by exploiting those spectrum
opportunities. This ability is dependent upon spectrum sensing, which is one of most critical
components in a cognitive radio system. A significant challenge is to sense the whole RF
spectrum at a particular physical location in a short observation time. Otherwise, performance
degrades with longer observation times since the lagging response to spectrum holes implies
low spectrum utilisation efficiency. Hence, developing an efficient wideband spectrum sensing
technique is prime important.
In this thesis, a multirate asynchronous sub-Nyquist sampling (MASS) system that employs
multiple low-rate analog-to-digital converters (ADCs) is developed that implements wideband
spectrum sensing. The key features of the MASS system are, 1) low implementation complexity,
2) energy-efficiency for sharing spectrum sensing data, and 3) robustness against the lack
of time synchronisation. The conditions under which recovery of the full spectrum is unique
are presented using compressive sensing (CS) analysis. The MASS system is applied to both
centralised and distributed cognitive radio networks. When the spectra of the cognitive radio
nodes have a common spectral support, using one low-rate ADC in each cognitive radio node
can successfully recover the full spectrum. This is obtained by applying a hybrid matching
pursuit (HMP) algorithm - a synthesis of distributed compressive sensing simultaneous orthogonal
matching pursuit (DCS-SOMP) and compressive sampling matching pursuit (CoSaMP).
Moreover, a multirate spectrum detection (MSD) system is introduced to detect the primary
users from a small number of measurements without ever reconstructing the full spectrum.
To achieve a better detection performance, a data fusion strategy is developed for combining
sensing data from all cognitive radio nodes. Theoretical bounds on detection performance
are derived for distributed cognitive radio nodes suffering from additive white Gaussian noise
(AWGN), Rayleigh fading, and log-normal fading channels.
In conclusion, MASS and MSD both have a low implementation complexity, high energy efficiency,
good data compression capability, and are applicable to distributed cognitive radio
networks
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
Spatial-Spectral Joint Detection for Wideband Spectrum Sensing in Cognitive Radio Networks
Spectrum sensing is an essential functionality that enables cognitive radios
to detect spectral holes and opportunistically use under-utilized frequency
bands without causing harmful interference to primary networks. Since
individual cognitive radios might not be able to reliably detect weak primary
signals due to channel fading/shadowing, this paper proposes a cooperative
wideband spectrum sensing scheme, referred to as spatial-spectral joint
detection, which is based on a linear combination of the local statistics from
spatially distributed multiple cognitive radios. The cooperative sensing
problem is formulated into an optimization problem, for which suboptimal but
efficient solutions can be obtained through mathematical transformation under
practical conditions.Comment: To appear in the Proceedings of the 2008 IEEE International
Conference on Acoustics, Speech and Signal Processing, Las Vegas, NV, March
30-April 4, 200
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
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
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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