Luis Blanco

Sparse covariance fitting for direction of arrival estimation

This article proposes a new algorithm for finding the angles of arrival of multiple uncorrelated sources impinging on a uniform linear array of sensors. The method is based on sparse signal representation and does not require either the knowledge of the number of the sources or a previous initialization. The proposed technique considers a covariance matrix model based on overcomplete basis representation and tries to fit the unknown signal powers to the sample covariance matrix. Sparsity is enforced by means of a l1-norm penalty. The final problem is reduced to an objective function with a non-negative constraint that can be solved efficiently using the LARS/homotopy algorithm. The method described herein is able to provide high resolution with a low computational burden. It proceeds in an iterative fashion solving at each iteration a small linear system of equations until a stopping condition is fulfilled. The proposed stopping criterion is based on the residual spectrum and arises in a natural way when the LARS/homotopy is applied to the considered objective function.

Sparse Multiple Relay Selection for Network Beamforming With Individual Power Constraints Using Semidefinite Relaxation

This paper deals with the multiple relay selection problem in two-hop wireless cooperative networks with individual power constraints at the relays. In particular, it addresses the problem of selecting the best subset of K cooperative nodes and their corresponding beamforming weights so that the signal-to-noise ratio (SNR) is maximized at the destination. This problem is computationally demanding and requires an exhaustive search over all the possible combinations. In order to reduce the complexity, a new suboptimal method is proposed. This technique exhibits a near-optimal performance with a computational burden that is far less than the one needed in the combinatorial search. The proposed method is based on the use of the l1-norm squared and the Charnes-Cooper transformation and naturally leads to a semidefinite programming relaxation with an affordable computational cost. Contrary to other approaches in the literature, the technique exposed herein is based on the knowledge of the second-order statistics of the channels and the relays are not limited to cooperate with full power.

Subset relay selection in wireless cooperative networks using sparsity-inducing norms

This paper addresses the problem of multiple relay selection in a two-hop wireless cooperative network. In particular, the proposed technique selects the best subset of relays, in a distributed beamforming scheme, which maximizes the signal-to-noise ratio at the destination node subject to individual power constraints at the relays. The selection of the best subset of K relays out of a set of N potential relay nodes, under individual power constraints, is a hard combinatorial problem with a high computational burden. The approach considered herein consists in relaxing this problem into a convex one by considering a sparsity-inducing norm. The method exposed in this paper is based on the knowledge of the second-order statistics of the channels and achieves a near-optimal performance with a computational burden which is far less than the one needed in the combinatorial search. Furthermore, in the proposed technique, contrary to other approaches in the literature, the relays are not limited to cooperate with full power.

Fast communication: Minimum variance time of arrival estimation for positioning

Positioning systems based on time of arrival (TOA) rely on an accurate estimation of the first signal arrival which is the only one bearing position information. In this context, first arrival path detectors based on the minimum variance (MV) and the normalized minimum variance (NMV) criteria are robust against multipath propagation and non-line-of-sight (NLOS) situations. The aim of the paper is twofold. On the one hand, efficient implementations of those criteria will be presented. On the other hand, two polynomial rooting procedures based on the MV criterion will be exposed. As it will be shown, they lead to better performance than the traditional MV versions based on a grid search. In general, these methods imply finding the roots of a polynomial with complex coefficients. In order to reduce their computational burden a conformal mapping is proposed herein.

Conformal transformation for efficient ROOT-TOA estimation

Wireless location based on TOA (time-of-arrival) requires an accurate estimation of the first time delay. In this context, polynomial rooting techniques can provide an accurate estimation of the first arrival even in adverse multipath scenarios and when the direct path is highly attenuated. In general, these methods imply finding the roots of a complex polynomial. The aim of this paper is to present a conformal mapping that circumvents this problem, transforming it into a root search of a real polynomial with the resulting decrease in computational load.

SPC12-2: Low Complexity Toa Estimation for Wireless Location

Positioning estimation based on time of arrival (TOA) estimation needs high accurate first arrival detector. High-resolution TOA estimators based on minimum variance (MV) and normalized minimum variance (NMV) provide an accurate estimation of the first arrival even in high multipath environments at the expenses of a high computational cost. The aim of this paper is to reduce the computational burden of high resolution TOA estimators. First, reduced complexity MV and NMV implementations based on the FFT are presented. Next, polynomial root versions of both estimators are proposed yielding an improvement in positioning accuracy. Finally, a near TOA maximum likelihood (ML) estimator is proposed providing a good trade off between complexity and accuracy.

Relay subset selection in cognitive networks with imperfect CSI and individual power constraints

This paper considers the relay subset selection problem in an underlay cognitive network in which two secondary users communicate assisted by a set of N potential relays. More specifically, this paper deals with the joint problem of choosing the best subset of L secondary relays and their corresponding weights which maximize the Signal-to-Interference-plus-Noise ratio (SINR) at the secondary user receiver, subject to per-relay power constraints and interference power constraints at the primary user. This problem is a combinatorial problem with a high computational burden. Nevertheless, we propose a sub-optimal technique, based on a convex relaxation of the problem, which achieves a near-optimal performance with a reduced complexity. Contrary to other approaches in the literature, the secondary relays are not limited to cooperate at full power.