Search Results for author: Mojtaba Soltanalian

Found 18 papers, 3 papers with code

Waveform Design for Mutual Interference Mitigation in Automotive Radar

no code implementations8 Aug 2022 Arindam Bose, Bo Tang, Wenjie Huang, Mojtaba Soltanalian, Jian Li

The mutual interference between similar radar systems can result in reduced radar sensitivity and increased false alarm rates.

One-Bit Phase Retrieval: More Samples Means Less Complexity?

no code implementations16 Mar 2022 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.

One-Bit Compressive Sensing: Can We Go Deep and Blind?

no code implementations13 Mar 2022 Yiming Zeng, Shahin Khobahi, Mojtaba Soltanalian

The proposed deep architecture is able to learn an alternative sensing matrix by taking advantage of the underlying unfolded algorithm such that the resulting learned recovery algorithm can accurately and quickly (in terms of the number of iterations) recover the underlying compressed signal of interest from its one-bit noisy measurements.

Compressive Sensing

Unfolding-Aided Bootstrapped Phase Retrieval in Optical Imaging

no code implementations3 Mar 2022 Samuel Pinilla, Kumar Vijay Mishra, Igor Shevkunov, Mojtaba Soltanalian, Vladimir Katkovnik, Karen Egiazarian

Phase retrieval in optical imaging refers to the recovery of a complex signal from phaseless data acquired in the form of its diffraction patterns.

IRS-Aided Radar: Enhanced Target Parameter Estimation via Intelligent Reflecting Surfaces

no code implementations25 Oct 2021 Zahra Esmaeilbeig, Kumar Vijay Mishra, Mojtaba Soltanalian

We demonstrate that the IRS can provide a virtual or non-line-of-sight (NLOS) link between the radar and target leading to an enhanced radar performance.

LoRD-Net: Unfolded Deep Detection Network with Low-Resolution Receivers

1 code implementation5 Feb 2021 Shahin Khobahi, Nir Shlezinger, Mojtaba Soltanalian, Yonina C. Eldar

The need to recover high-dimensional signals from their noisy low-resolution quantized measurements is widely encountered in communications and sensing.

On the Performance of One-Bit DoA Estimation via Sparse Linear Arrays

no code implementations28 Dec 2020 Saeid Sedighi, M. R. Bhavani Shankar, Mojtaba Soltanalian, Björn Ottersten

Specifically, we first investigate the identifiability conditions for the DoA estimation problem from one-bit SLA data and establish an equivalency with the case when DoAs are estimated from infinite-bit unquantized measurements.

Unfolded Algorithms for Deep Phase Retrieval

no code implementations21 Dec 2020 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.

Deep-RLS: A Model-Inspired Deep Learning Approach to Nonlinear PCA

no code implementations15 Nov 2020 Zahra Esmaeilbeig, Shahin Khobahi, Mojtaba Soltanalian

In this work, we consider the application of model-based deep learning in nonlinear principal component analysis (PCA).

UPR: A Model-Driven Architecture for Deep Phase Retrieval

no code implementations9 Mar 2020 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.

Efficient Waveform Covariance Matrix Design and Antenna Selection for MIMO Radar

1 code implementation13 Feb 2020 Arindam Bose, Shahin Khobahi, Mojtaba Soltanalian

In this paper, we investigate the joint optimization of the waveform covariance matrix and the antenna position vector for a MIMO radar system to approximate a given transmit beam-pattern, as well as to minimize the cross-correlation between the probing signals at a number of given target locations.

Deep-URL: A Model-Aware Approach To Blind Deconvolution Based On Deep Unfolded Richardson-Lucy Network

no code implementations3 Feb 2020 Chirag Agarwal, Shahin Khobahi, Arindam Bose, Mojtaba Soltanalian, Dan Schonfeld

The lack of interpretability in current deep learning models causes serious concerns as they are extensively used for various life-critical applications.

Deep Radar Waveform Design for Efficient Automotive Radar Sensing

no code implementations17 Dec 2019 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 of the target parameters.

Autonomous Vehicles Radar waveform design

Deep One-bit Compressive Autoencoding

no code implementations10 Dec 2019 Shahin Khobahi, Arindam Bose, Mojtaba Soltanalian

Parameterized mathematical models play a central role in understanding and design of complex information systems.

Model-Aware Deep Architectures for One-Bit Compressive Variational Autoencoding

1 code implementation27 Nov 2019 Shahin Khobahi, Mojtaba Soltanalian

Parameterized mathematical models play a central role in understanding and design of complex information systems.

Compressive Sensing Quantization

Comprehensive Personalized Ranking Using One-Bit Comparison Data

no code implementations6 Jun 2019 Aria Ameri, Arindam Bose, Mojtaba Soltanalian

The task of a personalization system is to recommend items or a set of items according to the users' taste, and thus predicting their future needs.

Deep Signal Recovery with One-Bit Quantization

no code implementations30 Nov 2018 Shahin Khobahi, Naveed Naimipour, Mojtaba Soltanalian, Yonina C. Eldar

Machine learning, and more specifically deep learning, have shown remarkable performance in sensing, communications, and inference.

BIG-bench Machine Learning Inference Optimization +1

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