225 citations · 888 across the 36 of their papers we have counts for
38 papers
The Rank-Collapse Principle for Quadratic Optimization
Mojtaba Soltanalian, Ahmad Mousavi
Quadratic optimization becomes hard as soon as either the matrix in the quadratic form has an unfavorable curvature or the feasible set is discrete, combinatorial, or otherwise non…
Quantum Compressive Sensing Meets Quantum Noise: A Practical Exploration
Naveed Naimipour, Collin Frink, Harry Shaw +2
Compressive sensing is a signal processing technique that enables the reconstruction of sparse signals from a limited number of measurements, leveraging the signal's inherent spars…
Antenna Failure Resilience: Deep Learning-Enabled Robust DOA Estimation with Single Snapshot Sparse Arrays
Ruxin Zheng, Shunqiao Sun, Hongshan Liu +3
Recent advancements in Deep Learning (DL) for Direction of Arrival (DOA) estimation have highlighted its superiority over traditional methods, offering faster inference, enhanced s…
Ambiguity Function Shaping in FMCW Automotive Radar
Zahra Esmaeilbeig, Arindam Bose, Mojtaba Soltanalian
Frequency-modulated continuous wave (FMCW) radar with inter-chirp coding produces high side-lobes in the Doppler and range dimensions of the radar's ambiguity function. The high si…
Mutual Interference Mitigation in PMCW Automotive Radar
Zahra Esmaeilbeig, Arindam Bose, Mojtaba Soltanalian
This paper addresses the challenge of mutual interference in phase-modulated continuous wave (PMCW) millimeter-wave (mmWave) automotive radar systems. The increasing demand for adv…
ORKA: Accelerated Kaczmarz Algorithms for Signal Recovery from One-Bit Samples
Arian Eamaz, Farhang Yeganegi, Deanna Needell +1
One-bit quantization with time-varying sampling thresholds has recently found significant utilization potential in statistical signal processing applications due to its relatively…
Waveform Design for Mutual Interference Mitigation in Automotive Radar
Arindam Bose, Bo Tang, Wenjie Huang +2
The mutual interference between similar radar systems can result in reduced radar sensitivity and increased false alarm rates. To address the synchronous and asynchronous interfere…
Quantum Compressive Sensing: Mathematical Machinery, Quantum Algorithms, and Quantum Circuitry
Kyle Sherbert, Naveed Naimipour, Haleh Safavi +2
Compressive sensing is a sensing protocol that facilitates reconstruction of large signals from relatively few measurements by exploiting known structures of signals of interest, t…
Covariance Recovery for One-Bit Sampled Data With Time-Varying Sampling Thresholds-Part II: Non-Stationary Signals
Arian Eamaz, Farhang Yeganegi, Mojtaba Soltanalian
The recovery of the input signal covariance values from its one-bit sampled counterpart has been deemed a challenging task in the literature. To deal with its difficulties, some as…
Covariance Recovery for One-Bit Sampled Data With Time-Varying Sampling Thresholds-Part I: Stationary Signals
Arian Eamaz, Farhang Yeganegi, Mojtaba Soltanalian
One-bit quantization, which relies on comparing the signals of interest with given threshold levels, has attracted considerable attention in signal processing for communications an…
One-Bit Phase Retrieval: More Samples Means Less Complexity?
Arian Eamaz, Farhang Yeganegi, Mojtaba Soltanalian
The classical problem of phase retrieval has found a wide array of applications in optics, imaging and signal processing. In this paper, we consider the phase retrieval problem in…
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…
IRS-Aided Radar: Enhanced Target Parameter Estimation via Intelligent Reflecting Surfaces
Zahra Esmaeilbeig, Kumar Vijay Mishra, Mojtaba Soltanalian
The intelligent reflecting surface (IRS) technology has recently attracted a lot of interest in wireless communications research. An IRS consists of passive reflective elements cap…
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…
On the Performance of One-Bit DoA Estimation via Sparse Linear Arrays
Saeid Sedighi, M. R. Bhavani Shankar, Mojtaba Soltanalian +1
Direction of Arrival (DoA) estimation using Sparse Linear Arrays (SLAs) has recently gained considerable attention in array processing thanks to their capability to provide enhance…
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-URL: A Model-Aware Approach To Blind Deconvolution Based On Deep Unfolded Richardson-Lucy Network
Chirag Agarwal, Shahin Khobahi, Arindam Bose +2
The lack of interpretability in current deep learning models causes serious concerns as they are extensively used for various life-critical applications. Hence, it is of paramount…
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…
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…
Waveform Design for One-Bit Radar Systems Under Uncertain Interference Statistics
Arindam Bose, Aria Ameri, Mojtaba Soltanalian
An important problem in cognitive radar is to enhance the estimation performance of the system by a joint design of its probing signal and receive filter using the a priori informa…
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…