Estimating the unknown parameters of a statistical model based on the observations collected by a sensor network is an important problem with application in multiple fields. In this setting, distributed processing, by which computations are carried out within the network in order to avoid raw data transmission to a fusion centre, is a desirable feature resulting in improved robustness and energy savings. In the presence of incomplete data, the expectation-maximisation (EM) algorithm is a popular means to iteratively compute the maximum likelihood (ML) estimate. It has found application in diverse fields such as computational biology, anomaly detection, speech segmentation, reinforcement learning, and motion estimation, among others. In this chapter we will review the formulation of the centralised EM estimation algorithm as a starting point and then discuss distributed versions well suited for implementation in sensor networks. The first class of these distributed versions requires specialised routing through the network in terms of a linear or circular path visiting all nodes, whereas the second class does away with this requirement by using the concept of network consensus to diffuse information through the network. Our focus will be on a relevant sensor network application, in which the parameter of a linear model is to be estimated in the presence of an unknown number of randomly malfunctioning sensors.

JF - Data Fusion in Wireless Sensor Networks: A statistical signal processing perspective PB - The Institution of Engineering and Technology (IET) CY - London, UK SN - 978-1-78561-584-9 ER - TY - CONF T1 - Flexible spectral precoding for sidelobe suppression in OFDM systems T2 - IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP) Y1 - 2019 A1 - Khawar Hussain A1 - Ana Lojo A1 - R. López-Valcarce KW - spectrum shaping KW - winter AB -Spectral precoding is a popular approach to reduce out-of-band radiation (OBR) in multicarrier systems in order to avoid adjacent channel interference. Since precoding will introduce signal distortion, appropriate decoding is required at the receiver side. We present a novel linear precoder design with flexibility to trade off OBR reduction, precoding/decoding complexity, and error rate at the receiver. The precoding matrices have low rank, which translates into significant computational savings. In this way, the requirements of different systems can be satisfied with varying complexity levels.

JF - IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP) PB - IEEE CY - Brighton, UK ER - TY - CONF T1 - Frequency-selective hybrid precoding and combining for mmWave MIMO systems with per-antenna power constraints T2 - IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP) Y1 - 2019 A1 - Javier Rodríguez-Fernández A1 - R. López-Valcarce A1 - Nuria González-Prelcic KW - mmWave KW - winter AB -Configuring hybrid precoders and combiners is the main challenge to be solved to operate at millimeter wave (mmWave) frequencies. The use of hybrid architectures imposse hardware constraints on the analog precoder that need to be carefully dealt with. In this paper, we develop hybrid precoders and combiners aiming at minimizing the Euclidean distance with respect to the approximate all-digital precoders and combiners maximizing the spectral efficiency under per-antenna power constraints. Numerical results demonstrate the effectiveness of the proposed design method, whose performance is close to that of the all-digital solution.

JF - IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP) PB - IEEE CY - Brighton, UK ER - TY - JOUR T1 - MMSE filter design for Full-Duplex filter-and-forward MIMO relays under limited dynamic range JF - Signal Processing Y1 - 2019 A1 - Emilio Antonio-Rodríguez A1 - Stefan Werner A1 - R. López-Valcarce A1 - Risto Wichman KW - full-duplex KW - winter AB -We study the problem of optimizing the end-to-end performance of a full-duplex filter-and-forward MIMO relay link, consisting

of a source, a relay, and a destination node, by employing linear filtering at each node. The system model accounts for multipath

propagation and self-interference at the relay, as well as transmitter impairments and limited dynamic range at every node. The

design accommodates signals with arbitrary spectra and includes the direct link between the source and destination nodes. Under

the minimum mean square error criterion, the resulting non-convex problem is approximated by a sequence of convex problems

and solved by means of an alternating minimization method. Linear constraints allocate some of the degrees of freedom in the relay

to guarantee a sufficiently small residual self-interference. Simulations quantify the impact of degrees of freedom, the dynamic

range, and the balance between direct and relay paths on the link performance.

VL - 156
ER -
TY - JOUR
T1 - Partial-Duplex Amplify-and-Forward Relaying: Spectral Efficiency Analysis under Self-Interference
JF - IEEE Transactions on Wireless Communications
Y1 - 2019
A1 - R. López-Valcarce
A1 - C. Mosquera
KW - full-duplex
KW - myrada
KW - winter
AB - We propose a novel mode of operation for Amplify-and-

Forward relays in which the spectra of the relay input and

output signals partially overlap. This partial-duplex relaying mode

encompasses half- and full-duplex as particular cases. By viewing

the partial-duplex relay as a bandwidth-preserving Linear

Periodically Time-Varying system, a spectral efficiency analysis

under self-interference is developed. In contrast with previous

works, self-interference is regarded as a useful informationbearing

component rather than simply assimilated to noise. This

approach reveals that previous results regarding the impact of

self-interference on (full-duplex) relay performance are overly

pessimistic. Based on a frequency-domain interpretation of the

effect of self-interference, a number of suboptimal decoding

architectures at the destination node are also discussed. It is found

that the partial-duplex relaying mode may provide an attractive

tradeoff between spectral efficiency and receiver complexity.

VL - 18
IS - 4
ER -
TY - CONF
T1 - Locally optimal invariant detector for testing equality of two power spectral densities
T2 - IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Y1 - 2018
A1 - David Ramírez
A1 - Daniel Romero
A1 - Javier Vía
A1 - R. López-Valcarce
A1 - Ignacio Santamaría
KW - cognitive radio
KW - winter
AB - This work addresses the problem of determining whether two multivariate random time series have the same power spectral density

(PSD), which has applications, for instance, in physical-layer security and cognitive radio. Remarkably, existing detectors for this

problem do not usually provide any kind of optimality. Thus, we study here the existence under the Gaussian assumption of optimal

invariant detectors for this problem, proving that the uniformly most powerful invariant test (UMPIT) does not exist. Thus, focusing on

close hypotheses, we show that the locally most powerful invariant test (LMPIT) only exists for univariate time series. In the multivariate

case, we prove that the LMPIT does not exist. However, this proof suggests two LMPIT-inspired detectors, one of which outperforms

previously proposed approaches, as computer simulations show.

JF - IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
CY - Calgary, Canada
ER -
TY - JOUR
T1 - Parameter estimation in wireless sensor networks with faulty transducers: A distributed EM approach
JF - Signal Processing
Y1 - 2018
A1 - Silvana Silva Pereira
A1 - R. López-Valcarce
A1 - Alba Pagès-Zamora
KW - winter
KW - wsn
AB - We address the problem of distributed estimation of a vector-valued parameter performed by a wireless sensor network in the presence of noisy observations which may be unreliable due to faulty transducers. The proposed distributed estimator is based on the Expectation-Maximization (EM) algorithm and combines consensus and diffusion techniques: a term for information diffusion is gradually turned off, while a term for updated information averaging is turned on so that all nodes in the network approach the same value of the estimate. The proposed method requires only local exchanges of information among network nodes and, in contrast with previous approaches, it does not assume knowledge of the a priori probability of transducer failures or the noise variance. A convergence analysis is provided, showing that the convergent points of the centralized EM iteration are locally asymptotically convergent points of the proposed distributed scheme. Numerical examples show that the distributed algorithm asymptotically attains the performance of the centralized EM method.

VL - 144 UR - https://authors.elsevier.com/a/1W90XbZX4rsob ER - TY - JOUR T1 - Testing equality of multiple power spectral density matrices JF - IEEE Trans. Signal Processing Y1 - 2018 A1 - David Ramírez A1 - Daniel Romero A1 - Javier Vía A1 - R. López-Valcarce A1 - Ignacio Santamaría KW - winter VL - 66 IS - 23 ER - TY - CONF T1 - Defending Surveillance Sensor Networks Against Data-Injection Attacks via Trusted Nodes T2 - European Signal Processing Conference (EUSIPCO) Y1 - 2017 A1 - R. López-Valcarce A1 - Daniel Romero KW - winter KW - wsn AB -By injecting false data through compromised sensors, an adversary can drive the probability of detection in a sensor network-based spatial field surveillance system to arbitrarily low values. As a countermeasure, a small subset of sensors may be secured. Leveraging the theory of Matched Subspace Detection, we propose and evaluate several detectors that add robustness to attacks when such trusted nodes are available. Our results reveal the performance-security tradeoff of these schemes and can be used to determine the number of trusted nodes required for a given performance target.

JF - European Signal Processing Conference (EUSIPCO)
CY - Kos Island, Greece
ER -
TY - JOUR
T1 - FER Estimation in a Memoryless BSC with Variable Frame Length and Unreliable ACK/NAK Feedback
JF - IEEE Transactions on Wireless Communications
Y1 - 2017
A1 - Alberto Rico-Alvariño
A1 - R. López-Valcarce
A1 - Carlos Mosquera
A1 - Robert W. Heath Jr.
KW - adaptive signal processing
KW - compass
KW - Cramér-Rao bound (CRB)
KW - frame error rate
KW - myrada
KW - winter
AB - We consider the problem of estimating the frame error rate (FER) of a given memoryless binary symmetric channel by observing the success or failure of transmitted packets. Whereas FER estimation is relatively straightforward if all observations correspond to packets with equal length, the problem becomes considerably more complex when this is not the case. We develop FER estimators when

transmissions of different lengths are observed, together with the Cramer-Rao Lower Bound (CRLB). Although the main focus is on Maximum Likelihood (ML) estimation, we also obtain low complexity schemes performing close to optimal in some scenarios. In a second stage, we consider the case in which FER estimation is performed at a node different from the receiver, and incorporate the impairment of unreliable observations by considering noisy ACK/NAK feedback links. The impact of unreliable feedback is analyzed by means of the corresponding CRLB. In this setting, the ML estimator is obtained by applying the Expectation-Maximization algorithm to jointly estimate the error probabilities of the data and feedback links. Simulation results illustrate the benefits of the proposed estimators.

VL - 16
IS - 6
ER -
TY - CONF
T1 - Filter design for delay-based anonymous communications
T2 - IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Y1 - 2017
A1 - Simon Oya
A1 - Fernando Pérez-González
A1 - Carmela Troncoso
KW - adversarial signal processing
KW - compass
KW - Multimedia security
KW - winter
JF - IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
ER -
TY - CONF
T1 - Is Geo-Indistinguishability what you are looking for?
T2 - Workshop on Privacy in the Electronic Society (WPES)
Y1 - 2017
A1 - Simon Oya
A1 - Carmela Troncoso
A1 - Fernando Pérez-González
KW - adversarial signal processing
KW - Multimedia security
KW - winter
JF - Workshop on Privacy in the Electronic Society (WPES)
ER -
TY - JOUR
T1 - Learning Power Spectrum Maps from Quantized Power Measurements
JF - IEEE Transactions on Signal Processing
Y1 - 2017
A1 - Daniel Romero
A1 - Seung-Jun Kim
A1 - Georgios Giannakis
A1 - R. López-Valcarce
KW - cognitive radio
KW - compass
KW - compressed sensing
KW - spectrum sensing
KW - winter
KW - wsn
AB - 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 - Number Theoretic Transforms for Secure Signal Processing
JF - IEEE Transactions on Information Forensics and Security
Y1 - 2017
A1 - Alberto Pedrouzo-Ulloa
A1 - Juan Ramón Troncoso-Pastoriza
A1 - Fernando Pérez-González
KW - lattice cryptography
KW - Multimedia security
KW - Number Theoretic Transforms
KW - Secure Signal Processing
KW - Signal Processing in the Encrypted Domain
KW - Somewhat Homomorphic Encryption
KW - winter
VL - 12
IS - 5
ER -
TY - JOUR
T1 - A Random Matrix Approach to the Forensic Analysis of Upscaled Images
JF - IEEE Transactions on Information Forensics and Security
Y1 - 2017
A1 - David Vázquez-Padín
A1 - Fernando Pérez-González
A1 - Pedro Comesaña
KW - Multimedia security
KW - winter
VL - 12
IS - 9
ER -
TY - CONF
T1 - Random Matrix Theory for Modeling the Eigenvalue Distribution of Images under Upscaling
T2 - International Tyrrhenian Workshop on Digital Communication 2017
Y1 - 2017
A1 - David Vázquez-Padín
A1 - Fernando Pérez-González
A1 - Pedro Comesaña
KW - Multimedia security
KW - winter
AB - The stochastic representation of digital images through a two-dimensional autoregressive (2D-AR) model offers a proper way to approximate the empirical distribution of the eigenvalues coming from genuine images. By considering this model, we apply random matrix theory to analytically derive the asymptotic eigenvalue distribution of causal 2D-AR random fields that have undergone an upscaling operation with a particular interpolation kernel. This eigenvalue characterization is useful in developing new forensic techniques for image resampling detection since we can use theoretical bounds to drive the decision of detectors based on subspace decomposition. Moreover, experimental results with real images show that the obtained asymptotic limits turn out to be excellent approximations, even when working with images of small size.

JF - International Tyrrhenian Workshop on Digital Communication 2017 PB - Springer International Publishing CY - Palermo, Italy ER - TY - CONF T1 - Robust clustering of data collected via crowdsourcing T2 - IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) Y1 - 2017 A1 - Alba Pagès-Zamora A1 - Georgios Giannakis A1 - R. López-Valcarce A1 - Pere Gimenez-Febrer KW - winter KW - wsn AB -Crowdsourcing approaches rely on the collection of multiple individuals to solve problems that require analysis of large data sets in a timely accurate manner. The inexperience of participants or annotators motivates well robust techniques. Focusing on clustering setups, the data provided by all annotators is suitably modeled here as a mixture of Gaussian components plus a uniformly distributed random variable to capture outliers. The proposed algorithm is based on the expectation-maximization algorithm and allows for soft assignments of data to clusters, to rate annotators according to their performance, and to estimate the number of Gaussian components in the non-Gaussian/Gaussian mixture model, in a jointly manner.

JF - IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) CY - New Orleans ER - TY - JOUR T1 - Wideband full-duplex MIMO relays with blind adaptive self-interference cancellation JF - Signal Processing Y1 - 2017 A1 - Emilio Antonio-Rodríguez A1 - Stefan Werner A1 - R. López-Valcarce A1 - Taneli Riihonen A1 - Risto Wichman KW - adaptive signal processing KW - compass KW - full duplex KW - winter AB -We develop adaptive self-interference cancellation algorithms for both filter-and-forward and decode-and-forward

multiple-input multiple-output relays. The algorithms are blind in the sense that they only exploit the spectral properties

of the transmitted signal to identify the self-interference channel, while dealing with frequency-selective channels and

arbitrary signal spectra. Our approach is non-intrusive in the sense that the algorithms can successfully identify, track,

and cancel the self-interference distortion while the relay is operating in its normal mode. We study the stationary

points of the algorithms and analyze under which conditions they achieve perfect cancellation of the self-interference.

Simulation results show that the algorithms provide residual self-interference levels below the noise floor by using

the time samples of only a few OFDM symbols.

VL - 130
ER -