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.

VL - 65
IS - 10
ER -
TY - JOUR
T1 - Compressive Covariance Sensing: Structure-Based Compressive Sensing Beyond Sparsity
JF - IEEE Signal Processing Magazine
Y1 - 2016
A1 - Daniel Romero
A1 - Dyonisius D Ariananda
A1 - Zhi Tian
A1 - Geert Leus
KW - compass
KW - compressed sensing
AB - 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.

VL - 33
IS - 1
ER -
TY - JOUR
T1 - Designing incoherent frames through convex techniques for optimized compressed sensing
JF - IEEE Transactions on Signal Processing
Y1 - 2016
A1 - Cristian Rusu
A1 - Nuria González-Prelcic
KW - compass
KW - compressed sensing
VL - 64
IS - 9
ER -
TY - JOUR
T1 - Fast orthonormal sparsifying transforms based on Householder reflectors
JF - IEEE Transactions on Signal Processing
Y1 - 2016
A1 - C. Rusu
A1 - Nuria González-Prelcic
A1 - R.W. Heath Jr.
KW - compass
KW - compressed sensing
VL - 64
IS - 24
ER -
TY - CONF
T1 - The use of unit norm tight measurement matrices for one-bit compressed sensing
T2 - IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Y1 - 2016
A1 - C. Rusu
A1 - Nuria González-Prelcic
A1 - R. W. Heath Jr.
KW - compass
KW - compressed sensing
AB - 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

JF - IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) ER - TY - JOUR T1 - Compression Limits for Random Vectors with Linearly Parameterized Second-Order Statistics JF - IEEE Trans. Information Theory Y1 - 2015 A1 - Daniel Romero A1 - R. López-Valcarce A1 - Geert Leus KW - compass KW - compressed sensing AB -The 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.

VL - 61
IS - 3
ER -
TY - CONF
T1 - Spectrum Cartography using quantized observations
T2 - IEEE Int. Conference on Acoustics, Speech and Signal Processing (ICASSP)
Y1 - 2015
A1 - Daniel Romero
A1 - Seung-Jun Kim
A1 - R. López-Valcarce
A1 - Georgios Giannakis
KW - cognitive radio
KW - compass
KW - compressed sensing
KW - spectrum sensing
AB - 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.

JF - IEEE Int. Conference on Acoustics, Speech and Signal Processing (ICASSP)
PB - IEEE
CY - Brisbane, Australia
ER -
TY - CONF
T1 - Circular Sparse Rulers Based On Co-prime Sampling For Compressive Power Spectrum Estimation
T2 - IEEE Global Communications Conference
Y1 - 2014
A1 - Nuria González-Prelcic
A1 - M. E. Domíngez-Jiménez
KW - cognitive radio
KW - compass
KW - compressed sensing
JF - IEEE Global Communications Conference
PB - IEEE
CY - Austin, TX
ER -
TY - JOUR
T1 - Wideband Spectrum Sensing From Compressed Measurements Using Spectral Prior Information
JF - IEEE Trans. Signal Process.
Y1 - 2013
A1 - Daniel Romero
A1 - Geert Leus
KW - cognitive radio
KW - compressed sensing
KW - dynacs
KW - spectrum sensing
VL - 61
ER -