%0 Journal Article %J IEEE Transactions on Signal Processing %D 2017 %T Learning Power Spectrum Maps from Quantized Power Measurements %A Daniel Romero %A Seung-Jun Kim %A Georgios Giannakis %A R. López-Valcarce %K cognitive radio %K compass %K compressed sensing %K spectrum sensing %K winter %K wsn %X
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 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 Int. Conf. Acoust., Speech, Signal Process. (ICASSP) %D 2013 %T Compressive wideband spectrum sensing with spectral prior information %A Daniel Romero %A R. López-Valcarce %A Geert Leus %K cognitive radio %K dynacs %K spectrum sensing %X
Wideband spectrum sensing provides a means to determine
the occupancy of channels spanning a broad range of frequencies.
Practical limitations impose that the acquisition should
be accomplished at a low rate, much below the Nyquist lower
bound. Dramatic rate reductions can be obtained by the observation
that only a few parameters need to be estimated in
typical spectrum sensing applications. This paper discusses
the joint estimation of the power of a number of channels,
whose power spectral density (PSD) is known up to a scale
factor, using compressive measurements. First, relying on
a Gaussian assumption, an efficient approximate maximum
likelihood (ML) technique is presented. Next, a least-squares
estimator is applied for the general non-Gaussian case.
%B Int. Conf. Acoust., Speech, Signal Process. (ICASSP) %G eng %R 10.1109/ICASSP.2013.6638505 %0 Conference Paper %B IEEE 14th Int. Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Darmstadt (Germany) %D 2013 %T Spectrum Sensing in Time-Varying Channels Using Multiple Antennas %A Daniel Romero %A R. López-Valcarce %K cognitive radio %K dynacs %K spectrum sensing %B IEEE 14th Int. Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Darmstadt (Germany) %8 06/2013 %G eng %R 10.1109/SPAWC.2013.6612027 %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 %0 Conference Paper %B IEEE Statistical Signal Processing Workshop (SSP 2012) %D 2012 %T Detection of Gaussian signals in unknown time-varying channels %A Daniel Romero %A Javier Vía %A R. López-Valcarce %A Ignacio Santamaría %K cognitive radio %K dynacs %K spectrum sensing %X
Detecting the presence of a white Gaussian signal distorted by a noisy time-varying channel is addressed by means of three different detectors. First, the generalized likelihood ratio test (GLRT) is found for the case where the channel has no temporal structure, resulting in the well-known Bartlett’s test. Then it is shown that, under the transformation group given by scaling factors, a locally most powerful invariant test (LMPIT) does not exist. Two alternative approaches are explored in the low signal-to-noise ratio (SNR) regime: the first assigns a prior probability density function (pdf) to the channel (hence modeled as random), whereas the second assumes an underlying basis expansion model (BEM) for the (now deterministic) channel and obtains the maximum likelihood (ML) estimates of the parameters relevant for the detection problem. The performance of these detectors is evaluated via Monte Carlo simulation.
%B IEEE Statistical Signal Processing Workshop (SSP 2012) %I IEEE %C Ann Arbor, MI %8 08/2012 %G eng %R 10.1109/SSP.2012.6319858 %0 Conference Paper %B 3rd Int. Workshop on Cognitive Information Processing (CIP 2012) %D 2012 %T Detection of unknown constant magnitude signals in time-varying channels %A Daniel Romero %A R. López-Valcarce %K cognitive radio %K dynacs %K spectrum sensing %X

Spectrum sensing constitutes a key ingredient in many cognitive radio paradigms in order to detect and protect primary transmissions. Most sensing schemes in the literature assume a time-invariant channel. However, when operating in low Signal-to-Noise Ratio (SNR) conditions, observation times are necessarily long and may become larger than the coherence time of the channel. In this paper the problem of detecting an unknown constant-magnitude waveform in frequency-flat time-varying channels with noise background of unknown variance is considered. The channel is modeled using a basis expansion model (BEM) with random coefficients. Adopting a generalized likelihood ratio (GLR) approach in order to deal with nuisance parameters, a non-convex optimization problem results. We discuss different possibilities to circumvent this problem, including several low complexity approximations to the GLR test as well as an efficient fixed-point iterative method to obtain the true GLR statistic. The approximations exhibit a performance ceiling in terms of probability of detection as the SNR increases, whereas the true GLR test does not. Thus, the proposed fixed-point iteration constitutes the preferred choice in applications requiring a high probability of detection.

%B 3rd Int. Workshop on Cognitive Information Processing (CIP 2012) %C Baiona, Spain %8 05/2012 %G eng %R 10.1109/CIP.2012.6232933 %0 Conference Paper %B IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP 2012) %D 2012 %T Generalized matched filter detector for fast fading channels %A Daniel Romero %A R. López-Valcarce %A Geert Leus %K cognitive radio %K dynacs %K spectrum sensing %X
We consider the problem of detecting a known signal with constant magnitude immersed in noise of unknown variance,
when the propagation channel is frequency-flat and randomly
time-varying within the observation window. A Basis Expansion
Model with random coefficients is used for the channel, and a Generalized Likelihood Ratio approach is adopted in order to cope with deterministic nuisance parameters. The resulting scheme can be seen as a generalization of the well-known
Matched Filter detector, to which it reduces for timeinvariant
channels. Closed-form analytical expressions are provided for the distribution of the test statistic under both hypotheses, which allow to assess the detection performance.
%B IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP 2012) %I IEEE %C Kyoto, Japan %8 07/2012 %G eng %R 10.1109/ICASSP.2012.6288587 %0 Conference Paper %B IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP 2011) %D 2011 %T Detection diversity of multiantenna spectrum sensors %A Gonzalo Vázquez-Vilar %A R. López-Valcarce %A Ashish Pandharipande %K cognitive radio %K detection diversity %K spectrum sensing %X
In the context of spectrum sensing, we investigate the performance of detectors equipped with M antennas (co-located or distributed) under Rayleigh fading, in terms of detection diversity. Rather than the high-SNR concept of diversity order common in the communications literature, we adopt the notion recently advocated by Daher and Adve in the radar community: the slope of the average probability of detection (\bar{P}_D) vs. SNR curve at \bar{P}_D = 0.5. This definition is well suited to spectrum sensing, which invariably deals with low SNR levels. It is shown that the diversity order grows as M for an optimal centralized detector having access to all observations, whereas for the two distributed schemes considered (the multiantenna energy detector and the OR detector) it grows no faster than √M.
%B IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP 2011) %I IEEE %C Prague, Czech Republic %P 2936-2939 %8 05/2011 %G eng %0 Journal Article %J IEEE Trans. on Signal Processing %D 2011 %T Detection of Rank-P Signals in Cognitive Radio Networks With Uncalibrated Multiple Antennas %A David Ramírez %A Gonzalo Vázquez-Vilar %A R. López-Valcarce %A Javier Vía %A Ignacio Santamaría %K cognitive radio %K dynacs %K spectrum sensing %X
Spectrum sensing is a key component of the Cognitive Radio paradigm. Typically, primary signals have to be detected with uncalibrated receivers at signal-to-noise ratios (SNRs) well below decodability levels. Multiantenna detectors exploit spatial independence of receiver thermal noise to boost detection performance and robustness. We study the problem
of detecting a Gaussian signal with rank-P unknown spatial
covariance matrix in spatially uncorrelated Gaussian noise with
unknown covariance using multiple antennas. The generalized
likelihood ratio test (GLRT) is derived for two scenarios. In the
first one, the noises at all antennas are assumed to have the same (unknown) variance, whereas in the second, a generic diagonal noise covariance matrix is allowed in order to accommodate calibration uncertainties in the different antenna frontends. In the latter case, the GLRT statistic must be obtained numerically, for which an efficient method is presented. Furthermore, for asymptotically low SNR, it is shown that the GLRT does admit a closed form, and the resulting detector performs well in practice. Extensions are presented in order to account for unknown temporal correlation in both signal and noise, as well as frequency-selective channels.
%B IEEE Trans. on Signal Processing %V 59 %P 3764-3774 %8 08/2012 %G eng %N 8 %R 10.1109/TSP.2011.2146779 %0 Conference Paper %B IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC) %D 2011 %T Distributed spectrum sensing with multiantenna sensors under calibration errors %A Daniel Romero %A R. López-Valcarce %K ad hoc detectors %K antenna arrays %K calibration %K calibration errors %K cognitive radio %K energy detector %K generalized likelihood ratio %K log normal distribution %K lognormal shadowing %K multiantenna sensors %K Ricean fading %K spectrum sensing %K wireless medium %X

Spectrum sensing design for Cognitive Radio systems is challenged by the nature of the wireless medium, which makes the detection requirements difficult to achieve by standalone sensors. To combat shadowing and fading, distributed strategies are usually proposed. However, most distributed approaches are based on the energy detector, which is not robust to noise uncertainty. This phenomenon can be overcome by multi-antenna sensors exploiting spatial independence of the noise process. We combine both ideas to develop distributed detectors for multiantenna sensors. Fusion rules are provided for sensors based on the Generalized Likelihood Ratio as well as for ad hoc detectors derived from geometric considerations. Simulation results are provided comparing the performance of the different strategies under lognormal shadowing and Ricean fading.

%B IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC) %P 441 -445 %8 june %G eng %R 10.1109/SPAWC.2011.5990448 %0 Thesis %D 2011 %T Interference and Network Management in Cognitive Communication Systems %A Gonzalo Vázquez-Vilar %K cognitive radio %K spectrum sensing %I University of Vigo %C Vigo, Spain %V Ph.D. %8 06/2011 %G eng %9 Ph.D. thesis %0 Conference Paper %B IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC) %D 2011 %T Multiantenna detection of constant-envelope signals in noise of unknown variance %A Daniel Romero %A R. López-Valcarce %K cognitive radio %K multiantenna detection %K spectrum sensing %X

Detection of unknown signals with constant modulus (CM) using multiple antennas in additive white Gaussian noise of unknown variance is considered. The channels from the source to each antenna are assumed frequency-flat and unknown. This problem is of interest for spectrum sensing in cognitive radio systems in which primary signals are known to have the CM property. Examples include analog frequency modulated signals such as those transmitted by wireless microphones in the TV bands and Gaussian Minimum Shift Keying modulated signals as in the GSM cellular standard. The proposed detector, derived from a Generalized Likelihood Ratio (GLR) approach, exploits both the CM property and the spatial independence of noise, outperforming the GLR test for Gaussian signals as shown by simulation.

%B IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC) %P 446 -450 %8 june %G eng %R 10.1109/SPAWC.2011.5990449 %0 Conference Paper %B IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) %D 2011 %T Multiantenna detection under noise uncertainty and primary user's spatial structure %A David Ramírez %A Gonzalo Vázquez-Vilar %A R. López-Valcarce %A Javier Vía %A Ignacio Santamaría %K cognitive radio %K generalized likelihood ratio test (GLRT) %K spectrum sensing %B IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) %I IEEE %C Prage, Czech Republic %P 2948-2951 %8 May %G eng %0 Journal Article %J IEEE Trans. on Wireless Communications %D 2011 %T Multiantenna Spectrum Sensing Exploiting Spectral a priori Information %A Gonzalo Vázquez-Vilar %A R. López-Valcarce %A Josep Sala %K cognitive radio %K dynacs %K spectrum sensing %B IEEE Trans. on Wireless Communications %V 10 %P 4345 - 4355 %8 12/2011 %G eng %N 12 %R 10.1109/TWC.2011.101211.110665 %0 Journal Article %J IEEE Trans. on Signal Processing %D 2011 %T Spectrum Sensing Exploiting Guard Bands and Weak Channels %A Gonzalo Vázquez-Vilar %A R. López-Valcarce %K cognitive radio %K dynacs %K spectrum sensing %X
We address the problem of primary user detection in Cognitive Radio from a wideband signal comprising multiple primary channels, exploiting a priori knowledge about the primary network: channelization and spectral shape of primary transmissions. Using this second-order statistical information, a multichannel Gaussian model is formulated. In order to obtain a Generalized Likelihood Ratio Test, we first address Maximum Likelihood (ML) estimation of the power levels at the different channels, as well as of the noise variance. The ML conditions suggest a suboptimal closed-form estimate, which takes the form of a constrained Least Squares estimator whose asymptotic efficiency is shown for flat bandpass spectra in white noise, a case of practical importance. The resulting detectors exploit those frequency bins corresponding to guard bands and to primary channels perceived as weak to improve noise variance estimation. Analytical expressions for the probabilities of detection and false alarm are presented. Performance is evaluated via simulations in the setting of a terrestrial TV primary network with realistic channelization parameters.
%B IEEE Trans. on Signal Processing %V 59 %P 6045-6057 %8 12/2011 %G eng %N 12 %R 10.1109/TSP.2011.2167615