455 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
Wideband cyclostationary spectrum sensing and characterization for cognitive radios
Motivated by the spectrum scarcity problem, Cognitive Radios (CRs) have been proposed as a solution to opportunistically communicate over unused spectrum licensed to Primary users (PUs). In this context, the unlicensed Secondary users (SUs) sense the spectrum to detect the presence or absence of PUs, and use the unoccupied bands without causing interference to PUs. CRs are equipped with capabilities such as, learning, adaptability, and recongurability, and are spectrum aware. Spectrum awareness comes from spectrum sensing, and it can be performed using different techniques
Sub-Nyquist Wideband Spectrum Sensing and Sharing
PhDThe rising popularity of wireless services resulting in spectrum shortage has motivated
dynamic spectrum sharing to facilitate e cient usage of the underutilized spectrum.
Wideband spectrum sensing is a critical functionality to enable dynamic spectrum access
by enhancing the opportunities of exploring spectral holes, but entails a major implemen-
tation challenge in compact commodity radios that have limited energy and computation
capabilities. The sampling rates speci ed by the Shannon-Nyquist theorem impose great
challenges both on the acquisition hardware and the subsequent storage and digital sig-
nal processors. Sub-Nyquist sampling was thus motivated to sample wideband signals
at rates far lower than the Nyquist rate, while still retaining the essential information in
the underlying signals.
This thesis proposes several algorithms for invoking sub-Nyquist sampling in wideband
spectrum sensing. Speci cally, a sub-Nyquist wideband spectrum sensing algorithm is
proposed that achieves wideband sensing independent of signal sparsity without sampling
at full bandwidth by using the low-speed analog-to-digital converters based on sparse
Fast Fourier Transform. To lower signal spectrum sparsity while maintaining the channel
state information, the received signal is pre-processed through a proposed permutation
and ltering algorithm. Additionally, a low-complexity sub-Nyquist wideband spectrum
sensing scheme is proposed that locates occupied channels blindly by recovering the sig-
nal support, based on the jointly sparse nature of multiband signals. Exploiting the
common signal support shared among multiple secondary users, an e cient coopera-
tive spectrum sensing scheme is developed, in which the energy consumption on signal
acquisition, processing, and transmission is reduced with the detection performance guar-
antee. To further reduce the computation complexity of wideband spectrum sensing, a
hybrid framework of sub-Nyquist wideband spectrum sensing with geolocation database
is explored. Prior channel information from geolocation database is utilized in the sens-
ing process to reduce the processing requirements on the sensor nodes. The models of
the proposed algorithms are derived and veri ed by numerical analyses and tested on
both real-world and simulated TV white space signals
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