no code implementations • 13 Mar 2024 • Arian Eamaz, Farhang Yeganegi, Yunqiao Hu, Mojtaba Soltanalian, Shunqiao Sun
This paper investigates the effects of coarse quantization with mixed precision on measurements obtained from sparse linear arrays, synthesized by a collaborative automotive radar sensing strategy.
no code implementations • 26 Feb 2024 • Zahra Esmaeilbeig, Arindam Bose, Mojtaba Soltanalian
In this paper, we develop a novel code optimization method that attenuates the side-lobes of the radar's ambiguity function.
no code implementations • 21 Feb 2024 • Tara Esmaeilbeig, Kumar Vijay Mishra, Mojtaba Soltanalian
In particular, we consider the joint design objective of maximizing the weighted sum of the signal-to-noise ratio (SNR) at the radar receiver and communication users by leveraging the extra degrees-of-freedom offered in the BD-RIS setting.
no code implementations • 9 Dec 2023 • Arian Eamaz, Farhang Yeganegi, Yunqiao Hu, Shunqiao Sun, Mojtaba Soltanalian
The design of sparse linear arrays has proven instrumental in the implementation of cost-effective and efficient automotive radar systems for high-resolution imaging.
no code implementations • 22 Oct 2023 • Zahra Esmaeilbeig, Kumar Vijay Mishra, Arian Eamaz, Mojtaba Soltanalian
In this paper, we design the placement of IRS platforms for sensing by maximizing the mutual information.
no code implementations • 13 Sep 2023 • Zahra Esmaeilbeig, Kumar Vijay Mishra, Mojtaba Soltanalian
Direct application of STAP in a network of radar systems such as in a CAV may lead to excess interference.
no code implementations • 7 Sep 2023 • Arian Eamaz, Farhang Yeganegi, Mojtaba Soltanalian
Additionally, we introduce a sufficient condition specifically designed for UNO sampling to perfectly recover non-bandlimited signals within spline spaces.
no code implementations • 31 Jul 2023 • Zahra Esmaeilbeig, Mojtaba Soltanalian
This paper revisits two prominent adaptive filtering algorithms, namely recursive least squares (RLS) and equivariant adaptive source separation (EASI), through the lens of algorithm unrolling.
no code implementations • 16 Jun 2023 • 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.
no code implementations • 8 Mar 2023 • Arian Eamaz, Farhang Yeganegi, Kumar Vijay Mishra, Mojtaba Soltanalian
Conventional sensing applications rely on electromagnetic far-field channel models with plane wave propagation.
no code implementations • 24 Feb 2023 • Zahra Esmaeilbeig, Arian Eamaz, Kumar Vijay Mishra, Mojtaba Soltanalian
In this paper, we consider a multi-IRS-aided orthogonal frequency-division multiplexing (OFDM) radar and study the theoretically achievable accuracy of target detection.
no code implementations • 11 Oct 2022 • Zahra Esmaeilbeig, Kumar Vijay Mishra, Arian Eamaz, Mojtaba Soltanalian
Intelligent reflecting surface (IRS) is a rapidly emerging paradigm to enable non-line-of-sight (NLoS) wireless transmission.
no code implementations • 8 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.
no code implementations • 16 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.
no code implementations • 16 Mar 2022 • Arian Eamaz, Farhang Yeganegi, Mojtaba Soltanalian
Similar to the case of the arcsine law, the Bussgang law only considers a zero sampling threshold.
no code implementations • 13 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.
no code implementations • 3 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.
no code implementations • 25 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.
1 code implementation • 5 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.
no code implementations • 28 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.
no code implementations • 21 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.
no code implementations • 15 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).
no code implementations • 9 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.
1 code implementation • 13 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.
no code implementations • 3 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.
no code implementations • 17 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.
no code implementations • 10 Dec 2019 • Shahin Khobahi, Arindam Bose, Mojtaba Soltanalian
Parameterized mathematical models play a central role in understanding and design of complex information systems.
1 code implementation • 27 Nov 2019 • Shahin Khobahi, Mojtaba Soltanalian
Parameterized mathematical models play a central role in understanding and design of complex information systems.
no code implementations • 6 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.
no code implementations • 30 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.