VNU-UET Repository: No conditions. Results ordered -Date Deposited. 2023-10-01T08:50:20ZEPrintshttp://eprints.uet.vnu.edu.vn/images/sitelogo.pnghttps://eprints.uet.vnu.edu.vn/eprints/2022-03-21T00:46:35Z2022-03-21T00:46:35Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4711This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/47112022-03-21T00:46:35ZMisspecified Cramer–Rao Bounds for Blind Channel Estimation Under Channel Order MisspecificationIn estimation, the misspecified Cramer–Rao bound (MCRB), which is an extension of the well-known Cramer–Rao bound (CRB) when the underlying system model is misspecified, has recently attracted much attention. In this paper, we introduce a new interpretation of the MCRB, called the generalized MCRB (GMCRB), via the Moore–Penrose inverse operator. This bound is useful for singular problems and particularly blind channel estimation problems in which the Hessian matrix is noninvertible. Two closed-form expressions of the GMCRB are derived for unbiased blind estimators when the channel order is misspecified. The first bound deals with deterministic models where both the channel and unknown symbols are deterministic. The second one is devoted to stochastic models where we assume that transmitted symbols are unknown random variables i.i.d. drawn from a Gaussian distribution. Two case studies of channel order misspecification are investigated to demonstrate the effectiveness of the proposed GMCRBs over the classical CRBs. When the channel order is known or accurately estimated, both generalized bounds reduce to the classical bounds. Besides, the stochastic GMCRB is lower than the deterministic one, especially at high SNR.Trung Thanh Leletrungthanhtbt@gmail.comAbed Meraim Karimkarim.abed-meraim@univ-orleans.frLinh Trung Nguyenlinhtrung@vnu.edu.vn2022-03-21T00:30:45Z2022-03-21T00:30:45Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4712This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/47122022-03-21T00:30:45ZSparse Subspace Tracking in High DimensionsWe studied the problem of sparse subspace tracking in the
high-dimensional regime where the dimension is comparable
to or much larger than the sample size. Leveraging power iteration and thresholding methods, a new provable algorithm
called OPIT was derived for tracking the sparse principal subspace of data streams over time. We also presented a theoretical result on its convergence to verify its consistency in high dimensions. Several experiments were carried out on both synthetic and real data to demonstrate the effectiveness of OPIT.Trung Thanh Leletrungthanhtbt@gmail.comAbed Meraim Karimkarim.abed-meraim@univ-orleans.frHafiane Adeladel.hafiane@insa-cvl.frLinh Trung Nguyenlinhtrung@vnu.edu.vn2021-06-18T10:55:39Z2021-06-18T10:55:39Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4454This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/44542021-06-18T10:55:39ZPerformance lower bounds of blind system identification techniques in the presence of channel order estimation errorIn this paper, we derive two performance lower bounds
for blind system identification in the presence of channel order
estimation error. The first bound deals with models where
both the channel and unknown symbols are deterministic, and
obtained via the constrained misspecified Cramer-Rao bound
(MCRB). When transmitted symbols are unknown random variables i.i.d. drawn from a stochastic Gaussian process, variance
of any unbiased estimators is always higher than the second
MCRB bound. Both proposed MCRB bounds reduce to the
classical Cramer-Rao bounds when the channel order is known
or accurately estimated. Besides, the stochastic MCRB is lower than the deterministic bound, especially at high SNRsTrung Thanh Leletrungthanhtbt@gmail.comAbed Meraim Karimkarim.abed-meraim@univ-orleans.frLinh Trung Nguyenlinhtrung@vnu.edu.vn2021-06-18T10:55:27Z2021-06-18T10:55:27Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4453This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/44532021-06-18T10:55:27ZA Fast Randomized Adaptive CP Decomposition For Streaming TensorsIn this paper, we introduce a fast adaptive algorithm for CAN- DECOMP/PARAFAC decomposition of streaming three-way tensors using randomized sketching techniques. By leveraging randomized least-squares regression and approximating matrix multiplication, we propose an efficient first-order estimator to minimize an exponentially weighted recursive least- squares cost function. Our algorithm is fast, requiring a low computational complexity and memory storage. Experiments indicate that the proposed algorithm is capable of adaptive tensor decomposition with a competitive performance evaluation on both synthetic and real data.Trung Thanh Leletrungthanhtbt@gmail.comAbed Meraim Karimkarim.abed-meraim@univ-orleans.frLinh Trung Nguyenlinhtrung@vnu.edu.vnHafiane Adel2020-12-25T11:15:15Z2020-12-25T11:15:15Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4335This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/43352020-12-25T11:15:15ZOn the Gaussian Cramér-Rao Bound for Blind Single-Input
Multiple-Output System Identification: Fast and Asymptotic
ComputationsThe Cramér-Rao Bound (CRB) is a powerful tool to assess the performance limits of a parameter estimation problem for a given statistical model. In particular, the Gaussian CRB (i.e., the CRB obtained assuming the data are Gaussian) corresponds to the worst case; giving the largest CRB among a large class of data distributions. This makes it very useful in practice since optimizing under the Gaussian data assumption can be interpreted as a min-max optimization (i.e., minimizing the largest CRB). The Gaussian CRB is also the corresponding bound of Second-Order Statistics (SOS)-based estimation methods, which are frequently used in practice. Despite its practicality, computing this bound might be cumbersome in some cases, particularly in the case where the input is assumed deterministic and has a large number of samples. In this paper, we address this computational issue by proposing a fast computation for the deterministic Gaussian CRB of Single-Input Multiple Output (SIMO) blind system identification. More precisely, we exploit circulant matrix properties to reduce the cost from cubic to quadratic with respect to the sample size. Moreover, we derive a closed-form formula for the asymptotic (large sample size) Gaussian CRB and show how it can be computed using the residue theorem.Nait-Meziane MohamedAbed Meraim Karimkarim.abed-meraim@univ-orleans.frZhao ZhipengLinh Trung Nguyenlinhtrung@vnu.edu.vn2019-12-04T07:33:25Z2019-12-04T07:33:25Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/3703This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/37032019-12-04T07:33:25ZRobust subspace tracking with missing data and outliers via ADMMRobust subspace tracking is crucial when dealing with data in the presence of both outliers and missing observations. In this paper, we propose a new algorithm, namely PETRELS-ADMM, to improve performance of subspace tracking in such scenarios. Outliers residing in the observed data are first detected in an efficient way and removed by the alternating direction method of multipliers (ADMM) solver. The underlying subspace is then updated by the algorithm of parallel estimation and tracking by recursive least squares (PETRELS) in which each row of the subspace matrix was estimated in parallel. Based on PETRELS-ADMM, we also derive an efficient way for robust matrix completion. Performance studies show the superiority of PETRELS-ADMM as compared to the state-ofthe-art algorithms. We also illustrate its effectiveness for the application of background-foreground separation.Trung Thanh Leletrungthanhtbt@gmail.comViet Dung Nguyennvdung@vnu.edu.vnLinh Trung Nguyenlinhtrung@vnu.edu.vnAbed Meraim Karimkarim.abed-meraim@univ-orleans.fr2019-12-04T07:30:12Z2019-12-04T07:30:12Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/3702This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/37022019-12-04T07:30:12ZRobust subspace tracking: Novel algorithm and performance guaranteeSubspace tracking, which is refered to online PCA, is a classical problem in signal processing with various applications in wireless communications, rada and image/video processing. Since outliers and missing data are ubiquitous and more common in big data regime, robust variants of subspace tracking (RST) are crucial. In this paper, we propose a novel algorithm, namely PETRELSADMM, to improve RST performance in such scenario. The proposed approach consists of two main stages, including outlier rejection and subspace estimation. In the first stage, alternating direction method of multipliers (ADMM) solver is used to detect outliers residing in the observed data in an efficient way. In the second stage, we propose a modification of the parallel estimation and tracking by recursive least squares (PETRELS) algorithm to update the underlying subspace. A theoretical convegence analysis is provided, i.e., we prove that PETRELS-ADMM can generate a sequence of subspace solutions converging to the optimum of its batch counterpart. Performance studies show the superiority of our algorithms as compared to the state-of-the-art algorithms on both synthesis data and real data.Trung Thanh Leletrungthanhtbt@gmail.comViet Dung Nguyennvdung@vnu.edu.vnLinh Trung Nguyenlinhtrung@vnu.edu.vnAbed Meraim Karimkarim.abed-meraim@univ-orleans.fr2016-12-19T16:38:19Z2016-12-19T16:38:19Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/2013This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/20132016-12-19T16:38:19ZOn Optimization of Antennas without Phase Center for DOA estimationAntennas without phase center (AWPC) are applied to resolve the less-sensors-than-sources problem in direction-ofarrival (DOA) estimation using subspace methods, typically, the well-known multiple signal classiﬁcation (MUSIC) algorithm. This paper focuses on optimization of some design parameters of such antennas, including the rotation angle, the rotation step number and distances of two dipole couples, in order to improve the ambiguity and accuracy of all estimators which use AWPC structure. These optimization problems are formulated and solved by using Cramer-Rao bound (CRB) and ambiguity checking criteria. Specially, the parameters were optimized to avoid issues related to the ambiguity for half space localization problem while minimizing the CRB for improving the DOA estimation accuracy.Thi Thuy Quynh Tranquynhttt@vnu.edu.vnLinh Trung Nguyenlinhtrung@vnu.edu.vnAnh Phanphananh@rev.org.vnAbed Meraim Karimkarim.abed-meraim@univ-orleans.fr