Sergiy A . Vorobyov - Academia.edu (original) (raw)

Papers by Sergiy A . Vorobyov

Research paper thumbnail of Tensorized Neural Layer Decomposition for 2-D DOA Estimation

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Restoration of Ultrasound Images Using Spatially-Variant Kernel Deconvolution

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Lossless Dimension Reduction for Integer Least Squares With Application to Sphere Decoding

IEEE Transactions on Signal Processing, 2020

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Complexity Efficient DMT-Optimal Tree Pruning Based Sphere Decoding

arXiv (Cornell University), Oct 21, 2019

We present a diversity multiplexing tradeoff (DMT) optimal tree pruning sphere decoding algorithm... more We present a diversity multiplexing tradeoff (DMT) optimal tree pruning sphere decoding algorithm which visits merely a single branch of the search tree of the sphere decoding (SD) algorithm, while maintaining the DMT optimality at high signal to noise ratio (SNR) regime. The search tree of the sphere decoding algorithm is pruned via intersecting one dimensional spheres with the hypersphere of the SD algorithm, and the radii are chosen to guarantee the DMT optimality. In contrast to the conventional DMT optimal SD algorithm, which is known to have a polynomial complexity at high SNR regime, we show that the proposed method achieves the DMT optimality by solely visiting a single branch of the search tree at high SNR regime. The simulation results are corroborated with the claimed characteristics of the algorithm in two different scenarios.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Spectrum sharing in wireless networks: A QOS-aware secondary multicast approach with worst user performance optimization

Bookmarks Related papers MentionsView impact

Research paper thumbnail of On Achievable Rates for Massive Mimo System with Imperfect Channel Covariance Information

An analytical lower bound on uplink channel capacity of a user in a massive multiple-input multip... more An analytical lower bound on uplink channel capacity of a user in a massive multiple-input multiple-output system where the channel vector and the covariance matrices of the users in that cell are unknown is derived in this paper. This analytical bound enables us to choose appropriate sample size for covariance matrix estimation to meet the spectral efficiency requirements. The accurate agreement between the derived bound and the simulated bound based on random samples of channel vectors and covariance matrices is shown.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Robust Least Mean Squares Estimation of Graph Signals

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Coupled Feature Learning Via Structured Convolutional Sparse Coding for Multimodal Image Fusion

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 23, 2022

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Time-multiplexed / superimposed pilot selection for massive MIMO pilot decontamination

In massive multiple-input multiple-output (MIMO) systems, superimposed (SP) and time-multiplexed ... more In massive multiple-input multiple-output (MIMO) systems, superimposed (SP) and time-multiplexed (TM) pilots exhibit a complementary behavior, with the former and latter schemes offering a higher throughput in high and low inter-cell interference scenarios, respectively. Based on this observation, in this paper, we propose an algorithm for partitioning users into two disjoint sets comprising users that transmit TM and SP pilots. This selection of user sets is accomplished by minimizing the total inter-cell and intra-cell interference, and since this problem is found to be non-convex, a greedy approach is proposed to perform the partitioning. Based on simulations, it is shown that the proposed method is versatile and offers an improved performance in both high and low-interference scenarios.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of New estimation methods for autoregressive process in the presence of white observation noise

Signal Processing, Jun 1, 2020

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Generalizing Nesterov’s Acceleration Framework by Embedding Momentum Into Estimating Sequences: New Algorithm and Bounds

2022 IEEE International Symposium on Information Theory (ISIT), Jun 26, 2022

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Enhanced Standard Esprit For Overcoming Imperfections In DOA Estimation

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Direction-of-arrival (DOA) estimation problem is a challenging one in the presence of coherent so... more Direction-of-arrival (DOA) estimation problem is a challenging one in the presence of coherent sources, when the sample size is small, and the signal-to-noise ratio is low. We address this problem by developing a new method called enhanced standard ESPRIT (ES ESPRIT), and also its unitary extension called enhanced unitary ESPRIT (EU ESPRIT). The proposed methods use statistics of the subspace perturbation. First, they generate 2K DOA candidates for K sources, and then discreetly select K of them. Numerical results show the superiority of EU ESPRIT over other existing methods especially in improving threshold performance and separating closely located sources with a small sample size.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A New Class of Composite Objective Multistep Estimating Sequence Techniques

SSRN Electronic Journal

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Enhanced robust adaptive beamforming designs for general-rank signal model via an induced norm of matrix errors

Signal Processing, 2021

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Worst-Case SINR Maximization Based Robust Adaptive Beamforming Problem with a Nonconvex Uncertainty Set

2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2019

The optimal robust adaptive beamforming problem based on worst-case signal-to-noise-plus-interfer... more The optimal robust adaptive beamforming problem based on worst-case signal-to-noise-plus-interference ratio (SINR) maximization with a nonconvex uncertainty set of the desired steering vectors is considered. The uncertainty set consists of a similarity constraint and a (nonconvex) double-sided ball constraint. The worst-case SINR maximization problem is turned into a quadratic matrix inequality (QMI) problem using the strong duality of semidefinite programs. Then the linear matrix inequality (LMI) relaxation for the QMI problem is formulated, and is further restricted by adding an equivalent representation for the second largest eigenvalue of the positive semidefinite beamforming matrix to be nonnegative. It turns out that the restricted LMI problem is a bilinear matrix inequality (BLMI) relaxation problem. We propose an iterative algorithm to solve the BLMI problem that finds an optimal/suboptimal solution for the original QMI problem for the worst-case SINR maximization problem. To validate our results, simulation examples are presented and demonstrate the improved performance of the proposed robust beamformer in terms of the array output SINR.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Blind Source Separation of Graph Signals

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020

With a change of signal notion to graph signal, new means of performing blind source separation (... more With a change of signal notion to graph signal, new means of performing blind source separation (BSS) appear. Particularly, existing independent component analysis (ICA) methods exploit the non-Gaussianity of the signals or other types of prior information. For graph signals, such prior information is present in a graph of dependencies in the signals. We propose BSS of graph signals which uses the prior information presented by the signal graph together with nonGaussianity. We derive the identifiability conditions for the proposed method and compare them to the conditions when only graph or non-Gaussianity approach is used. In simulation studies, we verify that the new method can separate a broader range of graph signals and show that it is also more efficient when both approaches are useful.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Downlink Performance of Superimposed Pilots in Massive MIMO Systems

IEEE Transactions on Wireless Communications, 2018

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Joint Cancelation of Autocorrelation Sidelobe and Cross Correlation in MIMO-SAR

IEEE Geoscience and Remote Sensing Letters, 2017

Bookmarks Related papers MentionsView impact

Research paper thumbnail of An OFDM-based waveform separation approach for MIMO-SAR

2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015

The waveform separation based on matched filtering leads to the cross correlation interference, w... more The waveform separation based on matched filtering leads to the cross correlation interference, which deteriorates the performance of multiple-input multiple-output (MIMO) radar system. This paper investigates the performance of a waveform separation approach employing a novel orthogonal frequency division multiplexing scheme for MIMO synthetic aperture radar. The approach enables to separate the waveforms perfectly even though the waveforms are on common spectral support. By means of theoretical analysis confirmed also by simulations, we show that the proposed scheme decreases sidelobe ratio. Thus, the high-quality imaging can be achieved.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Superimposed pilots: An alternative pilot structure to mitigate pilot contamination in massive MIMO

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Tensorized Neural Layer Decomposition for 2-D DOA Estimation

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Restoration of Ultrasound Images Using Spatially-Variant Kernel Deconvolution

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Lossless Dimension Reduction for Integer Least Squares With Application to Sphere Decoding

IEEE Transactions on Signal Processing, 2020

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A Complexity Efficient DMT-Optimal Tree Pruning Based Sphere Decoding

arXiv (Cornell University), Oct 21, 2019

We present a diversity multiplexing tradeoff (DMT) optimal tree pruning sphere decoding algorithm... more We present a diversity multiplexing tradeoff (DMT) optimal tree pruning sphere decoding algorithm which visits merely a single branch of the search tree of the sphere decoding (SD) algorithm, while maintaining the DMT optimality at high signal to noise ratio (SNR) regime. The search tree of the sphere decoding algorithm is pruned via intersecting one dimensional spheres with the hypersphere of the SD algorithm, and the radii are chosen to guarantee the DMT optimality. In contrast to the conventional DMT optimal SD algorithm, which is known to have a polynomial complexity at high SNR regime, we show that the proposed method achieves the DMT optimality by solely visiting a single branch of the search tree at high SNR regime. The simulation results are corroborated with the claimed characteristics of the algorithm in two different scenarios.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Spectrum sharing in wireless networks: A QOS-aware secondary multicast approach with worst user performance optimization

Bookmarks Related papers MentionsView impact

Research paper thumbnail of On Achievable Rates for Massive Mimo System with Imperfect Channel Covariance Information

An analytical lower bound on uplink channel capacity of a user in a massive multiple-input multip... more An analytical lower bound on uplink channel capacity of a user in a massive multiple-input multiple-output system where the channel vector and the covariance matrices of the users in that cell are unknown is derived in this paper. This analytical bound enables us to choose appropriate sample size for covariance matrix estimation to meet the spectral efficiency requirements. The accurate agreement between the derived bound and the simulated bound based on random samples of channel vectors and covariance matrices is shown.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Robust Least Mean Squares Estimation of Graph Signals

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Coupled Feature Learning Via Structured Convolutional Sparse Coding for Multimodal Image Fusion

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 23, 2022

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Time-multiplexed / superimposed pilot selection for massive MIMO pilot decontamination

In massive multiple-input multiple-output (MIMO) systems, superimposed (SP) and time-multiplexed ... more In massive multiple-input multiple-output (MIMO) systems, superimposed (SP) and time-multiplexed (TM) pilots exhibit a complementary behavior, with the former and latter schemes offering a higher throughput in high and low inter-cell interference scenarios, respectively. Based on this observation, in this paper, we propose an algorithm for partitioning users into two disjoint sets comprising users that transmit TM and SP pilots. This selection of user sets is accomplished by minimizing the total inter-cell and intra-cell interference, and since this problem is found to be non-convex, a greedy approach is proposed to perform the partitioning. Based on simulations, it is shown that the proposed method is versatile and offers an improved performance in both high and low-interference scenarios.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of New estimation methods for autoregressive process in the presence of white observation noise

Signal Processing, Jun 1, 2020

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Generalizing Nesterov’s Acceleration Framework by Embedding Momentum Into Estimating Sequences: New Algorithm and Bounds

2022 IEEE International Symposium on Information Theory (ISIT), Jun 26, 2022

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Enhanced Standard Esprit For Overcoming Imperfections In DOA Estimation

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Direction-of-arrival (DOA) estimation problem is a challenging one in the presence of coherent so... more Direction-of-arrival (DOA) estimation problem is a challenging one in the presence of coherent sources, when the sample size is small, and the signal-to-noise ratio is low. We address this problem by developing a new method called enhanced standard ESPRIT (ES ESPRIT), and also its unitary extension called enhanced unitary ESPRIT (EU ESPRIT). The proposed methods use statistics of the subspace perturbation. First, they generate 2K DOA candidates for K sources, and then discreetly select K of them. Numerical results show the superiority of EU ESPRIT over other existing methods especially in improving threshold performance and separating closely located sources with a small sample size.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A New Class of Composite Objective Multistep Estimating Sequence Techniques

SSRN Electronic Journal

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Enhanced robust adaptive beamforming designs for general-rank signal model via an induced norm of matrix errors

Signal Processing, 2021

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Worst-Case SINR Maximization Based Robust Adaptive Beamforming Problem with a Nonconvex Uncertainty Set

2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2019

The optimal robust adaptive beamforming problem based on worst-case signal-to-noise-plus-interfer... more The optimal robust adaptive beamforming problem based on worst-case signal-to-noise-plus-interference ratio (SINR) maximization with a nonconvex uncertainty set of the desired steering vectors is considered. The uncertainty set consists of a similarity constraint and a (nonconvex) double-sided ball constraint. The worst-case SINR maximization problem is turned into a quadratic matrix inequality (QMI) problem using the strong duality of semidefinite programs. Then the linear matrix inequality (LMI) relaxation for the QMI problem is formulated, and is further restricted by adding an equivalent representation for the second largest eigenvalue of the positive semidefinite beamforming matrix to be nonnegative. It turns out that the restricted LMI problem is a bilinear matrix inequality (BLMI) relaxation problem. We propose an iterative algorithm to solve the BLMI problem that finds an optimal/suboptimal solution for the original QMI problem for the worst-case SINR maximization problem. To validate our results, simulation examples are presented and demonstrate the improved performance of the proposed robust beamformer in terms of the array output SINR.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Blind Source Separation of Graph Signals

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020

With a change of signal notion to graph signal, new means of performing blind source separation (... more With a change of signal notion to graph signal, new means of performing blind source separation (BSS) appear. Particularly, existing independent component analysis (ICA) methods exploit the non-Gaussianity of the signals or other types of prior information. For graph signals, such prior information is present in a graph of dependencies in the signals. We propose BSS of graph signals which uses the prior information presented by the signal graph together with nonGaussianity. We derive the identifiability conditions for the proposed method and compare them to the conditions when only graph or non-Gaussianity approach is used. In simulation studies, we verify that the new method can separate a broader range of graph signals and show that it is also more efficient when both approaches are useful.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Downlink Performance of Superimposed Pilots in Massive MIMO Systems

IEEE Transactions on Wireless Communications, 2018

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Joint Cancelation of Autocorrelation Sidelobe and Cross Correlation in MIMO-SAR

IEEE Geoscience and Remote Sensing Letters, 2017

Bookmarks Related papers MentionsView impact

Research paper thumbnail of An OFDM-based waveform separation approach for MIMO-SAR

2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015

The waveform separation based on matched filtering leads to the cross correlation interference, w... more The waveform separation based on matched filtering leads to the cross correlation interference, which deteriorates the performance of multiple-input multiple-output (MIMO) radar system. This paper investigates the performance of a waveform separation approach employing a novel orthogonal frequency division multiplexing scheme for MIMO synthetic aperture radar. The approach enables to separate the waveforms perfectly even though the waveforms are on common spectral support. By means of theoretical analysis confirmed also by simulations, we show that the proposed scheme decreases sidelobe ratio. Thus, the high-quality imaging can be achieved.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Superimposed pilots: An alternative pilot structure to mitigate pilot contamination in massive MIMO

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016

Bookmarks Related papers MentionsView impact