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Given a corrupted low-rank matrix, robust principal component analysis performs a low-rank-plus-sparse matrix decomposition by solving a convex program. In this paper we first develop an efficient rank-revealing decomposition algorithm aided by randomization, which provides information about the singular subspaces and singular values of a given data matrix. The proposed factorization termed randomized...
In this work, we present a novel robust distributed beam-forming (RDB) approach to mitigate the effects of channel errors on wireless networks equipped with relays based on the exploitation of the cross-correlation between the received data from the relays at the destination and the system output. The proposed RDB method, denoted cross-correlation and subspace projection (CCSP) RDB, considers a total...
In this paper we propose novel randomized subspace methods to detect anomalies in Internet Protocol networks. Given a data matrix containing information about network traffic, the proposed approaches perform a normal-plus-anomalous matrix decomposition aided by the randomized sampling scheme and subsequently detect traffic anomalies in the anomalous subspace using a statistical test. Simulation results...
This work presents cost-effective low-rank techniques for designing robust adaptive beamforming (RAB) algorithms. At first, we introduce an orthogonal Krylov subspace projection mismatch estimation (OKSPME) method, in which a general linear equation is considered in large dimensions which aims to solve for the steering vector mismatch with known information, then we employ the idea of the full orthogonalization...
In this paper we propose a new robust principal component analysis method to separate the background and foreground scenes in video surveillance. Our approach uses a random projection method called Bilateral Random Projections (BRP) in conjunction with a switching between random projection matrices and a singular value estimation technique to separate the background and moving objects. The proposed...
This work presents a cost-effective low-rank technique for designing robust adaptive beamforming (RAB) algorithms. The proposed technique is based on low-rank modelling of the mismatch and exploitation of the cross-correlation between the received data and the output of the beamformer. We construct a linear system of equations which computes the steering vector mismatch based on prior information...
This paper presents a low-complexity robust data-dependent dimensionality reduction based on a modified joint iterative optimization (MJIO) algorithm for reduced-rank beamforming and steering vector estimation. The proposed robust optimization procedure jointly adjusts the parameters of a rank-reduction matrix and an adaptive beamformer. The optimized rank-reduction matrix projects the received signal...
In this work, we propose a minimum mean squared error (MMSE) robust base station (BS) precoding strategy based on switched relaying (SR) processing and limited transmission of side information for interference suppression in the downlink of multiuser multiple-input multiple-output (MIMO) relay systems. The BS and the MIMO relay station (RS) are both equipped with a codebook of interleaving matrices...
This paper presents adaptive bidirectional minimum mean-square error (MMSE) parameter estimation algorithms for fast-fading channels. The time correlation between successive channel gains is exploited to improve the estimation and tracking capabilities of adaptive algorithms and provide robustness against time-varying channels. Bidirectional normalized least mean-square (NLMS) and conjugate gradient...
We develop an adaptive beamforming algorithm based on the worst-case criterion and the constrained constant modulus approach. Similarly to the existing worst-case optimization-based beamformer with the minimum variance design, the problem can be reformulated as a second-order cone (SOC) program and solved with interior point methods. We also show some relations which are useful for the choice of the...
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