3,570 research outputs found
Compressive Identification of Active OFDM Subcarriers in Presence of Timing Offset
In this paper we study the problem of identifying active subcarriers in an
OFDM signal from compressive measurements sampled at sub-Nyquist rate. The
problem is of importance in Cognitive Radio systems when secondary users (SUs)
are looking for available spectrum opportunities to communicate over them while
sensing at Nyquist rate sampling can be costly or even impractical in case of
very wide bandwidth. We first study the effect of timing offset and derive the
necessary and sufficient conditions for signal recovery in the oracle-assisted
case when the true active sub-carriers are assumed known. Then we propose an
Orthogonal Matching Pursuit (OMP)-based joint sparse recovery method for
identifying active subcarriers when the timing offset is known. Finally we
extend the problem to the case of unknown timing offset and develop a joint
dictionary learning and sparse approximation algorithm, where in the dictionary
learning phase the timing offset is estimated and in the sparse approximation
phase active subcarriers are identified. The obtained results demonstrate that
active subcarrier identification can be carried out reliably, by using the
developed framework.Comment: To appear in the proceedings of the IEEE Global Communications
Conference (GLOBECOM) 201
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
A NLLS based sub-Nyquist rate Spectrum Sensing for Wideband Cognitive Radio
For systems and devices, such as cognitive radio and networks, that need to be aware of available frequency bands, spectrum sensing has an important role. A major challenge in this area is the requirement of a high sampling rate in the sensing of a wideband signal. In this paper a wideband spectrum sensing method is presented that utilizes a sub-Nyquist sampling scheme to bring substantial savings in terms of the sampling rate. The correlation matrix of a finite number of noisy samples is computed and used by a NLLS estimator to detect the occupied and vacant channels of the spectrum. We provide an expression for the detection threshold as a function of sampling parameters and noise power. Also, a sequential forward selection algorithm is presented to find the occupied channels in a low complexity. The method can be applied to both correlated and uncorrelated wideband multichannel signals. A comparison with conventional energy detection using Nyquist-rate sampling shows that the proposed scheme can yield similar performance for SNR above 4 dB with a factor of 3 smaller sampling rate
Non-uniform sampling and reconstruction of multi-band signals and its application in wideband spectrum sensing of cognitive radio
Sampling theories lie at the heart of signal processing devices and
communication systems. To accommodate high operating rates while retaining low
computational cost, efficient analog-to digital (ADC) converters must be
developed. Many of limitations encountered in current converters are due to a
traditional assumption that the sampling state needs to acquire the data at the
Nyquist rate, corresponding to twice the signal bandwidth. In this thesis a
method of sampling far below the Nyquist rate for sparse spectrum multiband
signals is investigated. The method is called periodic non-uniform sampling,
and it is useful in a variety of applications such as data converters, sensor
array imaging and image compression. Firstly, a model for the sampling system
in the frequency domain is prepared. It relates the Fourier transform of
observed compressed samples with the unknown spectrum of the signal. Next, the
reconstruction process based on the topic of compressed sensing is provided. We
show that the sampling parameters play an important role on the average sample
ratio and the quality of the reconstructed signal. The concept of condition
number and its effect on the reconstructed signal in the presence of noise is
introduced, and a feasible approach for choosing a sample pattern with a low
condition number is given. We distinguish between the cases of known spectrum
and unknown spectrum signals respectively. One of the model parameters is
determined by the signal band locations that in case of unknown spectrum
signals should be estimated from sampled data. Therefore, we applied both
subspace methods and non-linear least square methods for estimation of this
parameter. We also used the information theoretic criteria (Akaike and MDL) and
the exponential fitting test techniques for model order selection in this case
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
Accelerated High-Resolution Photoacoustic Tomography via Compressed Sensing
Current 3D photoacoustic tomography (PAT) systems offer either high image
quality or high frame rates but are not able to deliver high spatial and
temporal resolution simultaneously, which limits their ability to image dynamic
processes in living tissue. A particular example is the planar Fabry-Perot (FP)
scanner, which yields high-resolution images but takes several minutes to
sequentially map the photoacoustic field on the sensor plane, point-by-point.
However, as the spatio-temporal complexity of many absorbing tissue structures
is rather low, the data recorded in such a conventional, regularly sampled
fashion is often highly redundant. We demonstrate that combining variational
image reconstruction methods using spatial sparsity constraints with the
development of novel PAT acquisition systems capable of sub-sampling the
acoustic wave field can dramatically increase the acquisition speed while
maintaining a good spatial resolution: First, we describe and model two general
spatial sub-sampling schemes. Then, we discuss how to implement them using the
FP scanner and demonstrate the potential of these novel compressed sensing PAT
devices through simulated data from a realistic numerical phantom and through
measured data from a dynamic experimental phantom as well as from in-vivo
experiments. Our results show that images with good spatial resolution and
contrast can be obtained from highly sub-sampled PAT data if variational image
reconstruction methods that describe the tissues structures with suitable
sparsity-constraints are used. In particular, we examine the use of total
variation regularization enhanced by Bregman iterations. These novel
reconstruction strategies offer new opportunities to dramatically increase the
acquisition speed of PAT scanners that employ point-by-point sequential
scanning as well as reducing the channel count of parallelized schemes that use
detector arrays.Comment: submitted to "Physics in Medicine and Biology
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