11 citations · 32 across the 5 of their papers we have counts for
10 papers · 1 filter
One-Bit Compressive Sensing: Can We Go Deep and Blind?
Yiming Zeng, Shahin Khobahi, Mojtaba Soltanalian
One-bit compressive sensing is concerned with the accurate recovery of an underlying sparse signal of interest from its one-bit noisy measurements. The conventional signal recovery…
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…
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…
Efficient Waveform Covariance Matrix Design and Antenna Selection for MIMO Radar
Arindam Bose, Shahin Khobahi, Mojtaba Soltanalian
Controlling the radar beam-pattern by optimizing the transmit covariance matrix is a well-established approach for performance enhancement in multiple-input-multiple-output (MIMO)…
Deep Radar Waveform Design for Efficient Automotive Radar Sensing
Shahin Khobahi, Arindam Bose, Mojtaba Soltanalian
In radar systems, unimodular (or constant-modulus) waveform design plays an important role in achieving better clutter/interference rejection, as well as a more accurate estimation…