Inaki Esnaola

Power Allocation Strategies in Energy Harvesting Wireless Cooperative Networks

In this paper, a wireless cooperative network is considered, in which multiple source-destination pairs communicate with each other via an energy harvesting relay. The focus of this paper is on the relays strategies to distribute the harvested energy among the multiple users and their impact on the system performance. Specifically, a non-cooperative strategy that uses the energy harvested from the i-th source as the relay transmission power to the i-th destination is considered first, and asymptotic results show that its outage performance decays as log SNR/SNR. A faster decay rate, 1/SNR, can be achieved by two centralized strategies proposed next, of which a water filling based one can achieve optimal performance with respect to several criteria, at the price of high complexity. An auction based power allocation scheme is also proposed to achieve a better tradeoff between system performance and complexity. Simulation results are provided to confirm the accuracy of the developed analytical results.

Sparse Attack Construction and State Estimation in the Smart Grid: Centralized and Distributed Models

New methods that exploit sparse structures arising in smart grid networks are proposed for the state estimation problem when data injection attacks are present. First, construction strategies for unobservable sparse data injection attacks on power grids are proposed for an attacker with access to all network information and nodes. Specifically, novel formulations for the optimization problem that provide a flexible design of the trade-off between performance and false alarm are proposed. In addition, the centralized case is extended to a distributed framework for both the estimation and attack problems. Different distributed scenarios are proposed depending on assumptions that lead to the spreading of the resources, network nodes and players. Consequently, for each of the presented frameworks a corresponding optimization problem is introduced jointly with an algorithm to solve it. The validity of the presented procedures in real settings is studied through extensive simulations in the IEEE test systems.

SPADEDH: a sparsity-based denoising method of digital holograms without knowing the noise statistics

In this paper we propose a robust method to suppress the noise components in digital holography (DH), called SPADEDH (SPArsity DEnoising of Digital Holograms), that does not consider any prior knowledge or estimation about the statistics of the noise. In the full digital holographic process we must mainly deal with two kinds of noise. The first one is an additive uncorrelated noise that corrupts the observed irradiance, the other one is the multiplicative phase noise called speckle noise. We consider both lensless and microscope configurations and we prove that the proposed algorithm works efficiently in all considered cases suppressing the aforementioned noise components. In addition, for digital holograms recorded in lensless configuration, we show the improvement in a display test by using a Spatial Light Modulator (SLM).

Machine Learning Methods for Attack Detection in the Smart Grid

Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semisupervised) are employed with decision- and feature-level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than attack detection algorithms that employ state vector estimation methods in the proposed attack detection framework.

Exploiting Prior Knowledge in The Recovery of Signals from Noisy Random Projections

It has been recently shown that if a, signal can be compressed in some basis, then it can be reconstructed in such basis from, a certain number of random, projections. By allowing additional distortion, this holds even if the projections are corrupted by noise. We extend this result by showing that it is possible to exploit prior knowledge (e.g., if the signal is a realization of a stochastic process,) to significantly improve reconstruction performance. This is done in a fashion resembling standard joint source-channel coding of digital sources. Moreover, the exploitation of such knowledge allows for reconstruction in bases where the signal is not sparse

Exploiting Prior Knowledge in the Recovery of Non-Sparse Signals from Noisy Random Projections

This paper illustrates that exploiting the source statistics in the recovery process results in significant performance gains, even if the signal is reconstructed in a basis in which it does not admit a sparse representation. Successful recovery will depend on the capability of exploiting all available a priori information in the basis where reconstruction is performed. The proposed framework is similar to joint source-channel coding schemes in digital communications, but applies on the analog domain.

Distributed models for sparse attack construction and state vector estimation in the smart grid

Two distributed attack models and two distributed state vector estimation methods are introduced to handle the sparsity of smart grid networks in order to employ unobservable false data injection attacks and estimate state vectors. First, Distributed Sparse Attacks in which attackers process local measurements in order to achieve consensus for an attack vector are introduced. In the second attack model, called Collective Sparse Attacks, it is assumed that the topological information of the network and the measurements is available to attackers. However, attackers employ attacks to the groups of state vectors. The first distributed state vector estimation method, called Distributed State Vector Estimation, assumes that observed measurements are distributed in groups or clusters in the network. The second method, called Collaborative Sparse State Vector Estimation, consists of different operators estimating subsets of state variables. Therefore, state variables are assumed to be distributed in groups and accessed by the network operators locally. The network operators compute their local estimates and send the estimated values to a centralized network operator in order to update the estimated values.

Joint Source-Channel Coding of Sources with Memory using Turbo Codes and the Burrows-Wheeler Transform

The Burrows-Wheeler Transform (BWT) is a block sorting algorithm which has been proven to be useful in compressing text data . More recently, schemes based on the BWT have been proposed for lossless data compression using LDPC and Fountain codes, as well as for joint source-channel coding of sources with memory. In this paper we propose a source-controlled Turbo coding scheme for the transmission of sources with memory over AWGN channels also based on the Burrows-Wheeler Transform. Our approach combines the BWT with a Turbo code and employs different energy allocation techniques for the encoded symbols before their transmission. Simulation results show that the performance of the designed scheme is close (within 1.5 dB) to the theoretical Shannon limit.

Distributed compression of correlated real sequences using random projections

Recent developments in compressed sensing have shown that if a signal can be compressed in some basis, then it can be reconstructed in such basis from a certain number of random projections. Distributed compressed sensing, where several correlated signals are compressed in a distributed manner, has also been proposed in the literature. By allowing additional distortion, successful recovery in distributed compressed sensing can be achieved even if the projections are corrupted by noise. We extend this result by showing that in addition to sparsity, it is possible to exploit prior knowledge existing in the correlation between the signals of interest to significantly improve reconstruction performance. This is done in a fashion resembling distributed coding of digital sources.

Distributed Compression of Correlated Signals Using Random Projections

Recent developments in compressed sensing have shown that if a signal can be compressed in some basis, then it can be reconstructed in such basis from a certain number of random projections. Distributed compressed sensing, where several correlated signals are compressed in a distributed manner, has also been proposed in the literature. By allowing additional distortion, successful recovery in distributed compressed sensing can be achieved even if the projections are corrupted by noise. We extend this result by showing that in addition to sparsity, it is possible to exploit prior knowledge existing in the correlation between the signals of interest to significantly improve reconstruction performance. This is done in a fashion resembling distributed coding of digital sources.