18,888 research outputs found
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
Sub-Nyquist Sampling: Bridging Theory and Practice
Sampling theory encompasses all aspects related to the conversion of
continuous-time signals to discrete streams of numbers. The famous
Shannon-Nyquist theorem has become a landmark in the development of digital
signal processing. In modern applications, an increasingly number of functions
is being pushed forward to sophisticated software algorithms, leaving only
those delicate finely-tuned tasks for the circuit level.
In this paper, we review sampling strategies which target reduction of the
ADC rate below Nyquist. Our survey covers classic works from the early 50's of
the previous century through recent publications from the past several years.
The prime focus is bridging theory and practice, that is to pinpoint the
potential of sub-Nyquist strategies to emerge from the math to the hardware. In
that spirit, we integrate contemporary theoretical viewpoints, which study
signal modeling in a union of subspaces, together with a taste of practical
aspects, namely how the avant-garde modalities boil down to concrete signal
processing systems. Our hope is that this presentation style will attract the
interest of both researchers and engineers in the hope of promoting the
sub-Nyquist premise into practical applications, and encouraging further
research into this exciting new frontier.Comment: 48 pages, 18 figures, to appear in IEEE Signal Processing Magazin
Compressive and Noncompressive Power Spectral Density Estimation from Periodic Nonuniform Samples
This paper presents a novel power spectral density estimation technique for
band-limited, wide-sense stationary signals from sub-Nyquist sampled data. The
technique employs multi-coset sampling and incorporates the advantages of
compressed sensing (CS) when the power spectrum is sparse, but applies to
sparse and nonsparse power spectra alike. The estimates are consistent
piecewise constant approximations whose resolutions (width of the piecewise
constant segments) are controlled by the periodicity of the multi-coset
sampling. We show that compressive estimates exhibit better tradeoffs among the
estimator's resolution, system complexity, and average sampling rate compared
to their noncompressive counterparts. For suitable sampling patterns,
noncompressive estimates are obtained as least squares solutions. Because of
the non-negativity of power spectra, compressive estimates can be computed by
seeking non-negative least squares solutions (provided appropriate sampling
patterns exist) instead of using standard CS recovery algorithms. This
flexibility suggests a reduction in computational overhead for systems
estimating both sparse and nonsparse power spectra because one algorithm can be
used to compute both compressive and noncompressive estimates.Comment: 26 pages, single spaced, 9 figure
Regularized sampling of multiband signals
This paper presents a regularized sampling method for multiband signals, that
makes it possible to approach the Landau limit, while keeping the sensitivity
to noise at a low level. The method is based on band-limited windowing,
followed by trigonometric approximation in consecutive time intervals. The key
point is that the trigonometric approximation "inherits" the multiband
property, that is, its coefficients are formed by bursts of non-zero elements
corresponding to the multiband components. It is shown that this method can be
well combined with the recently proposed synchronous multi-rate sampling (SMRS)
scheme, given that the resulting linear system is sparse and formed by ones and
zeroes. The proposed method allows one to trade sampling efficiency for noise
sensitivity, and is specially well suited for bounded signals with unbounded
energy like those in communications, navigation, audio systems, etc. Besides,
it is also applicable to finite energy signals and periodic band-limited
signals (trigonometric polynomials). The paper includes a subspace method for
blindly estimating the support of the multiband signal as well as its
components, and the results are validated through several numerical examples.Comment: The title and introduction have changed. Submitted to the IEEE
Transactions on Signal Processin
A review of RFI mitigation techniques in microwave radiometry
Radio frequency interference (RFI) is a well-known problem in microwave radiometry (MWR). Any undesired signal overlapping the MWR protected frequency bands introduces a bias in the measurements, which can corrupt the retrieved geophysical parameters. This paper presents a literature review of RFI detection and mitigation techniques for microwave radiometry from space. The reviewed techniques are divided between real aperture and aperture synthesis. A discussion and assessment of the application of RFI mitigation techniques is presented for each type of radiometer.Peer ReviewedPostprint (published version
Extracting cosmic microwave background polarisation from satellite astrophysical maps
We present the application of the Fast Independent Component Analysis
({\ica}) technique for blind component separation to polarized astrophysical
emission. We study how the Cosmic Microwave Background (CMB) polarized signal,
consisting of and modes, can be extracted from maps affected by
substantial contamination from diffuse Galactic foreground emission and
instrumental noise. {We implement Monte Carlo chains varying the CMB and noise
realizations in order to asses the average capabilities of the algorithm and
their variance.} We perform the analysis of all sky maps simulated according to
the {\sc Planck} satellite capabilities, modelling the sky signal as a
superposition of the CMB and of the existing simulated polarization templates
of Galactic synchrotron. Our results indicate that the angular power spectrum
of CMB -mode can be recovered on all scales up to ,
corresponding to the fourth acoustic oscillation, while the -mode power
spectrum can be detected, up to its turnover at , if the ratio
of tensor to scalar contributions to the temperature quadrupole exceeds 30%.
The power spectrum of the cross correlation between total intensity and
polarization, , can be recovered up to , corresponding to
the seventh acoustic oscillation.Comment: 20 pages, MNRAS in pres
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