arXiv subjects

Marius Pesavento

Publications and source records attributed to Marius Pesavento.

47 records · Page 3Linked to original sources

arXiv2016

Calibration of Radio Interferometers Using a Sparse DoA Estimation Framework

The calibration of modern radio interferometers is a significant challenge, specifically at low frequencies. In this perspective, we propose a novel iterative calibration algorithm, which employs the popular sparse representation framework, in the regime where the propagation conditions shift dissimilarly the directions of the sources. More precisely, our algorithm is designed to estimate the apparent directions of the calibration sources, their powers, the directional and undirectional complex gains of the array elements and their noise powers, with a reasonable computational complexity. Numerical simulations reveal that the proposed scheme is statistically efficient at low SNR and even with additional non-calibration sources at unknown directions.

astro-ph.IM↗

arXiv2016

A Unified Successive Pseudo-Convex Approximation Framework

In this paper, we propose a successive pseudo-convex approximation algorithm to efficiently compute stationary points for a large class of possibly nonconvex optimization problems. The stationary points are obtained by solving a sequence of successively refined approximate problems, each of which is much easier to solve than the original problem. To achieve convergence, the approximate problem only needs to exhibit a weak form of convexity, namely, pseudo-convexity. We show that the proposed framework not only includes as special cases a number of existing methods, for example, the gradient method and the Jacobi algorithm, but also leads to new algorithms which enjoy easier implementation and faster convergence speed. We also propose a novel line search method for nondifferentiable optimization problems, which is carried out over a properly constructed differentiable function with the benefit of a simplified implementation as compared to state-of-the-art line search techniques that directly operate on the original nondifferentiable objective function. The advantages of the proposed algorithm are shown, both theoretically and numerically, by several example applications, namely, MIMO broadcast channel capacity computation, energy efficiency maximization in massive MIMO systems and LASSO in sparse signal recovery.

math.OC↗

arXiv2016

MIMO Radar Target Localization and Performance Evaluation under SIRP Clutter

Multiple-input multiple-output (MIMO) radar has become a thriving subject of research during the past decades. In the MIMO radar context, it is sometimes more accurate to model the radar clutter as a non-Gaussian process, more specifically, by using the spherically invariant random process (SIRP) model. In this paper, we focus on the estimation and performance analysis of the angular spacing between two targets for the MIMO radar under the SIRP clutter. First, we propose an iterative maximum likelihood as well as an iterative maximum a posteriori estimator, for the target's spacing parameter estimation in the SIRP clutter context. Then we derive and compare various Cramér-Rao-like bounds (CRLBs) for performance assessment. Finally, we address the problem of target resolvability by using the concept of angular resolution limit (ARL), and derive an analytical, closed-form expression of the ARL based on Smith's criterion, between two closely spaced targets in a MIMO radar context under SIRP clutter. For this aim we also obtain the non-matrix, closed-form expressions for each of the CRLBs. Finally, we provide numerical simulations to assess the performance of the proposed algorithms, the validity of the derived ARL expression, and to reveal the ARL's insightful properties.

stat.AP↗

arXiv2016

Maximum Likelihood and Maximum A Posteriori Direction-of-Arrival Estimation in the Presence of SIRP Noise

The maximum likelihood (ML) and maximum a posteriori (MAP) estimation techniques are widely used to address the direction-of-arrival (DOA) estimation problems, an important topic in sensor array processing. Conventionally the ML estimators in the DOA estimation context assume the sensor noise to follow a Gaussian distribution. In real-life application, however, this assumption is sometimes not valid, and it is often more accurate to model the noise as a non-Gaussian process. In this paper we derive an iterative ML as well as an iterative MAP estimation algorithm for the DOA estimation problem under the spherically invariant random process noise assumption, one of the most popular non-Gaussian models, especially in the radar context. Numerical simulation results are provided to assess our proposed algorithms and to show their advantage in terms of performance over the conventional ML algorithm.

stat.AP↗

arXiv2016

Multidimensional Sparse Recovery for MIMO Channel Parameter Estimation

Multipath propagation is a common phenomenon in wireless communication. Knowledge of propagation path parameters such as complex channel gain, propagation delay or angle-of-arrival provides valuable information on the user position and facilitates channel response estimation. A major challenge in channel parameter estimation lies in its multidimensional nature, which leads to large-scale estimation problems which are difficult to solve. Current approaches of sparse recovery for multidimensional parameter estimation aim at simultaneously estimating all channel parameters by solving one large-scale estimation problem. In contrast to that we propose a sparse recovery method which relies on decomposing the multidimensional problem into successive one-dimensional parameter estimation problems, which are much easier to solve and less sensitive to off-grid effects, while providing proper parameter pairing. Our proposed decomposition relies on convex optimization in terms of nuclear norm minimization and we present an efficient implementation in terms of the recently developed STELA algorithm.

cs.IT↗

arXiv2016

Apply Artificial Noise To Secure Fading Relay Networks: A SER-Based Approach

We apply the concept of artificial and controlled interference in a triangular relay network with an untrusted relay aiming at enhancing the wireless communication secrecy between the source and the destination node. In order to shield the square quadrature amplitude modulated (QAM) signals transmitted from the source node to the relay, the destination node designs and transmits artificial noise (AN) symbols to jam the relay reception. The objective of our considered AN design is to degrade the error probability performance at the untrusted relay, for different types of channel state information (CSI) at the destination. By considering perfect knowledge of the instantaneous CSI of the source-to-relay and relay-to-destination links, we first present an analytical expression for the symbol error rate (SER) performance at the relay. Based on the assumption of an average power constraint at the destination node, we then derive the optimal phase and power distribution of the AN that maximizes the SER at the relay. Furthermore, we obtain the optimal AN design for the case where only statistical CSI is available at the destination node. For both cases, our study reveals that the Gaussian distribution is generally not optimal to generate AN symbols. In particular, the conducted numerical results show that the Gaussian distributed AN is far from optimal. The presented AN design takes into account practical parameters for the communication links, such as QAM signaling and maximum likelihood decoding.

cs.IT↗

arXiv2015

An Online Parallel and Distributed Algorithm for Recursive Estimation of Sparse Signals

In this paper, we consider a recursive estimation problem for linear regression where the signal to be estimated admits a sparse representation and measurement samples are only sequentially available. We propose a convergent parallel estimation scheme that consists in solving a sequence of $\ell_{1}$-regularized least-square problems approximately. The proposed scheme is novel in three aspects: i) all elements of the unknown vector variable are updated in parallel at each time instance, and convergence speed is much faster than state-of-the-art schemes which update the elements sequentially; ii) both the update direction and stepsize of each element have simple closed-form expressions, so the algorithm is suitable for online (real-time) implementation; and iii) the stepsize is designed to accelerate the convergence but it does not suffer from the common trouble of parameter tuning in literature. Both centralized and distributed implementation schemes are discussed. The attractive features of the proposed algorithm are also numerically consolidated.

math.OC↗

arXiv2015

Rank-Two Beamforming and Power Allocation in Multicasting Relay Networks

In this paper, we propose a novel single-group multicasting relay beamforming scheme. We assume a source that transmits common messages via multiple amplify-and-forward relays to multiple destinations. To increase the number of degrees of freedom in the beamforming design, the relays process two received signals jointly and transmit the Alamouti space-time block code over two different beams. Furthermore, in contrast to the existing relay multicasting scheme of the literature, we take into account the direct links from the source to the destinations. We aim to maximize the lowest received quality-of-service by choosing the proper relay weights and the ideal distribution of the power resources in the network. To solve the corresponding optimization problem, we propose an iterative algorithm which solves sequences of convex approximations of the original non-convex optimization problem. Simulation results demonstrate significant performance improvements of the proposed methods as compared with the existing relay multicasting scheme of the literature and an algorithm based on the popular semidefinite relaxation technique.

cs.IT↗

arXiv2015

General Rank Multiuser Downlink Beamforming With Shaping Constraints Using Real-valued OSTBC

In this paper we consider optimal multiuser downlink beamforming in the presence of a massive number of arbitrary quadratic shaping constraints. We combine beamforming with full-rate high dimensional real-valued orthogonal space time block coding (OSTBC) to increase the number of beamforming weight vectors and associated degrees of freedom in the beamformer design. The original multi-constraint beamforming problem is converted into a convex optimization problem using semidefinite relaxation (SDR) which can be solved efficiently. In contrast to conventional (rank-one) beamforming approaches in which an optimal beamforming solution can be obtained only when the SDR solution (after rank reduction) exhibits the rank-one property, in our approach optimality is guaranteed when a rank of eight is not exceeded. We show that our approach can incorporate up to 79 additional shaping constraints for which an optimal beamforming solution is guaranteed as compared to a maximum of two additional constraints that bound the conventional rank-one downlink beamforming designs. Simulation results demonstrate the flexibility of our proposed beamformer design.

cs.IT↗

arXiv2014

A Parallel Stochastic Approximation Method for Nonconvex Multi-Agent Optimization Problems

Consider the problem of minimizing the expected value of a (possibly nonconvex) cost function parameterized by a random (vector) variable, when the expectation cannot be computed accurately (e.g., because the statistics of the random variables are unknown and/or the computational complexity is prohibitive). Classical sample stochastic gradient methods for solving this problem may empirically suffer from slow convergence. In this paper, we propose for the first time a stochastic parallel Successive Convex Approximation-based (best-response) algorithmic framework for general nonconvex stochastic sum-utility optimization problems, which arise naturally in the design of multi-agent systems. The proposed novel decomposition enables all users to update their optimization variables in parallel by solving a sequence of strongly convex subproblems, one for each user. Almost surely convergence to stationary points is proved. We then customize our algorithmic framework to solve the stochastic sum rate maximization problem over Single-Input-Single-Output (SISO) frequency-selective interference channels, multiple-input-multiple-output (MIMO) interference channels, and MIMO multiple-access channels. Numerical results show that our algorithms are much faster than state-of-the-art stochastic gradient schemes while achieving the same (or better) sum-rates.

cs.MA↗

arXiv2013

Angular resolution limit for deterministic correlated sources

This paper is devoted to the analysis of the angular resolution limit (ARL), an important performance measure in the directions-of-arrival estimation theory. The main fruit of our endeavor takes the form of an explicit, analytical expression of this resolution limit, w.r.t. the angular parameters of interest between two closely spaced point sources in the far-field region. As by-products, closed-form expressions of the Cramér-Rao bound have been derived. Finally, with the aid of numerical tools, we confirm the validity of our derivation and provide a detailed discussion on several enlightening properties of the ARL revealed by our expression, with an emphasis on the impact of the signal correlation.

cs.IT↗