486 citations · 3.5k across the 73 of their papers we have counts for
76 papers
Optimal Robust Adaptive Beamforming for a General-Rank Signal Model via Equivalence of Maximin and Minimax SINR Problems
Yongwei Huang, Zhenhui Huang, Sergiy A. Vorobyov +1
The globally optimal robust adaptive beamforming (RAB) solution is studied for worst-case signal-to-interference-plus-noise ratio (SINR) maximization (the maximin SINR problem) und…
An Efficient Approximate Method for Online Convolutional Dictionary Learning
Farshad G. Veshki, Sergiy A. Vorobyov
Most existing convolutional dictionary learning (CDL) algorithms are based on batch learning, where the dictionary filters and the convolutional sparse representations are optimize…
Twenty-Five Years of Advances in Beamforming: From Convex and Nonconvex Optimization to Learning Techniques
Ahmet M. Elbir, Kumar Vijay Mishra, Sergiy A. Vorobyov +1
Beamforming is a signal processing technique to steer, shape, and focus an electromagnetic wave using an array of sensors toward a desired direction. It has been used in several en…
Robust Adaptive Beamforming via Worst-Case SINR Maximization with Nonconvex Uncertainty Sets
Yongwei Huang, Hao Fu, Sergiy A. Vorobyov +1
This paper considers a formulation of the robust adaptive beamforming (RAB) problem based on worst-case signal-to-interference-plus-noise ratio (SINR) maximization with a nonconvex…
Convolutional Simultaneous Sparse Approximation with Applications to RGB-NIR Image Fusion
Farshad G. Veshki, Sergiy A. Vorobyov
Simultaneous sparse approximation (SSA) seeks to represent a set of dependent signals using sparse vectors with identical supports. The SSA model has been used in various signal an…
Graph Convolutional Neural Networks Sensitivity under Probabilistic Error Model
Xinjue Wang, Esa Ollila, Sergiy A. Vorobyov
Graph Neural Networks (GNNs), particularly Graph Convolutional Neural Networks (GCNNs), have emerged as pivotal instruments in machine learning and signal processing for processing…