11 citations · 23 across the 4 of their papers we have counts for
9 papers
LoRD-Net: Unfolded Deep Detection Network with Low-Resolution Receivers
Shahin Khobahi, Nir Shlezinger, Mojtaba Soltanalian +1
The need to recover high-dimensional signals from their noisy low-resolution quantized measurements is widely encountered in communications and sensing. In this paper, we focus on…
Unfolded Algorithms for Deep Phase Retrieval
Naveed Naimipour, Shahin Khobahi, Mojtaba Soltanalian
Exploring the idea of phase retrieval has been intriguing researchers for decades, due to its appearance in a wide range of applications. The task of a phase retrieval algorithm is…
Deep-RLS: A Model-Inspired Deep Learning Approach to Nonlinear PCA
Zahra Esmaeilbeig, Shahin Khobahi, Mojtaba Soltanalian
In this work, we consider the application of model-based deep learning in nonlinear principal component analysis (PCA). Inspired by the deep unfolding methodology, we propose a tas…
UPR: A Model-Driven Architecture for Deep Phase Retrieval
Naveed Naimipour, Shahin Khobahi, Mojtaba Soltanalian
The problem of phase retrieval has been intriguing researchers for decades due to its appearance in a wide range of applications. The task of a phase retrieval algorithm is typical…
Deep One-bit Compressive Autoencoding
Shahin Khobahi, Arindam Bose, Mojtaba Soltanalian
Parameterized mathematical models play a central role in understanding and design of complex information systems. However, they often cannot take into account the intricate interac…
Joint Optimization of Waveform Covariance Matrix and Antenna Selection for MIMO Radar
Arindam Bose, Shahin Khobahi, Mojtaba Soltanalian
In this paper, we investigate the problem of jointly optimizing the waveform covariance matrix and the antenna position vector for multiple-input-multiple-output (MIMO) radar syste…