Using power measurements collected by a network of low-cost sensors, power spectral density (PSD) maps are con-

structed to capture the distribution of RF power across space and frequency. Linearly compressed and quantized power measure-

ments enable wideband sensing at affordable implementation complexity using a small number of bits. Strengths of data- and model-

driven approaches are combined to develop estimators capable of incorporating multiple forms of spectral and propagation prior

information while fitting the rapid variations of shadow fading across space. To this end, novel nonparametric and semiparametric

formulations are investigated. It is shown that the desired PSD maps can be obtained using support vector machine-type solvers.

In addition to batch approaches, an online algorithm attuned to real-time operation is developed. Numerical tests assess the performance of the novel algorithms.

%B IEEE Transactions on Signal Processing
%V 65
%P 2547-2560
%8 05/2017
%G eng
%N 10
%R 10.1109/TSP.2017.2666775
%0 Journal Article
%J IEEE Signal Processing Magazine
%D 2016
%T Compressive Covariance Sensing: Structure-Based Compressive Sensing Beyond Sparsity
%A Daniel Romero
%A Dyonisius D Ariananda
%A Zhi Tian
%A Geert Leus
%K compass
%K compressed sensing
%X Compressed sensing deals with the reconstruction of signals

from sub-Nyquist samples by exploiting the sparsity of their

projections onto known subspaces. In contrast, the present

article is concerned with the reconstruction of second-order

statistics, such as covariance and power spectrum, even in

the absence of sparsity priors. The framework described here

leverages the statistical structure of random processes to

enable signal compression and offers an alternative perspective

at sparsity-agnostic inference. Capitalizing on parsimonious

representations, we illustrate how compression and reconstruction

tasks can be addressed in popular applications such

as power spectrum estimation, incoherent imaging, direction

of arrival estimation, frequency estimation, and wideband

spectrum sensing.

%B IEEE Signal Processing Magazine
%V 33
%P 78--93
%8 01/2016
%G eng
%N 1
%0 Journal Article
%J IEEE Transactions on Signal Processing
%D 2016
%T Designing incoherent frames through convex techniques for optimized compressed sensing
%A Cristian Rusu
%A Nuria González-Prelcic
%K compass
%K compressed sensing
%B IEEE Transactions on Signal Processing
%V 64
%P 2334-2344
%8 May/2016
%G eng
%N 9
%& 2334
%0 Journal Article
%J IEEE Transactions on Signal Processing
%D 2016
%T Fast orthonormal sparsifying transforms based on Householder reflectors
%A C. Rusu
%A Nuria González-Prelcic
%A R.W. Heath Jr.
%K compass
%K compressed sensing
%B IEEE Transactions on Signal Processing
%V 64
%P 6589-6599
%8 12/2016
%G eng
%N 24
%R 10.1109/TSP.2016.2612168
%0 Conference Paper
%B IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
%D 2016
%T The use of unit norm tight measurement matrices for one-bit compressed sensing
%A C. Rusu
%A Nuria González-Prelcic
%A R. W. Heath Jr.
%K compass
%K compressed sensing
%X In this paper we analyze the mean squared error (MSE) for one-bit compressed sensing schemes based on measurement matrices that correspond to unit norm tight frames. We show that, as in the unquantized case, sensing with unit norm tight frames improves the MSE in the reconstruction of sparse vectors from one-bit measurements using l1 and thresholding algorithms. From our analytical and experimental results we conclude that when implementing one-bit compressed sensing schemes with fixed measurement matrices unit norm tight frames are the measurements of choice

%B IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) %8 2016 %G eng %0 Journal Article %J IEEE Trans. Information Theory %D 2015 %T Compression Limits for Random Vectors with Linearly Parameterized Second-Order Statistics %A Daniel Romero %A R. López-Valcarce %A Geert Leus %K compass %K compressed sensing %XThe class of complex random vectors whose covariance

matrix is linearly parameterized by a basis of Hermitian

Toeplitz (HT) matrices is considered, and the maximum

compression ratios that preserve all second-order information

are derived — the statistics of the uncompressed vector must

be recoverable from a set of linearly compressed observations.

This kind of vectors arises naturally when sampling widesense

stationary random processes and features a number of

applications in signal and array processing.

Explicit guidelines to design optimal and nearly optimal

schemes operating both in a periodic and non-periodic fashion

are provided by considering two of the most common linear

compression schemes, which we classify as dense or sparse. It

is seen that the maximum compression ratios depend on the

structure of the HT subspace containing the covariance matrix of

the uncompressed observations. Compression patterns attaining

these maximum ratios are found for the case without structure as

well as for the cases with circulant or banded structure. Universal

samplers are also proposed to compress unknown HT subspaces.

%B IEEE Trans. Information Theory
%V 61
%P 1410-1425
%8 03/2015
%G eng
%N 3
%& 1410
%R 10.1109/TIT.2015.2394784
%0 Conference Paper
%B IEEE Int. Conference on Acoustics, Speech and Signal Processing (ICASSP)
%D 2015
%T Spectrum Cartography using quantized observations
%A Daniel Romero
%A Seung-Jun Kim
%A R. López-Valcarce
%A Georgios Giannakis
%K cognitive radio
%K compass
%K compressed sensing
%K spectrum sensing
%X This work proposes a spectrum cartography algorithm used for learning the power spectrum distribution over a wide frequency band across a given geographic area. Motivated by low-complexity sensing hardware and stringent communication constraints, compressed and quantized measurements are considered. Setting out from a nonparametric regression framework, it is shown that a sensible approach leads to a support vector machine formulation. The simulated tests verify that accurate spectrum maps can be constructed using a simple sensing architecture with significant savings in the feedback.

%B IEEE Int. Conference on Acoustics, Speech and Signal Processing (ICASSP)
%I IEEE
%C Brisbane, Australia
%8 04/2015
%G eng
%0 Conference Paper
%B IEEE Global Communications Conference
%D 2014
%T Circular Sparse Rulers Based On Co-prime Sampling For Compressive Power Spectrum Estimation
%A Nuria González-Prelcic
%A M. E. Domíngez-Jiménez
%K cognitive radio
%K compass
%K compressed sensing
%B IEEE Global Communications Conference
%I IEEE
%C Austin, TX
%8 12/2014
%G eng
%R 10.1109/GLOCOM.2014.7037272
%0 Journal Article
%J IEEE Trans. Signal Process.
%D 2013
%T Wideband Spectrum Sensing From Compressed Measurements Using Spectral Prior Information
%A Daniel Romero
%A Geert Leus
%K cognitive radio
%K compressed sensing
%K dynacs
%K spectrum sensing
%B IEEE Trans. Signal Process.
%V 61
%P 6232-6246
%G eng
%R 10.1109/TSP.2013.2283473