Search Results for author: Arian Eamaz

Found 9 papers, 0 papers with code

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.

Retrieval

Cramer-Rao Lower Bound Optimization for Hidden Moving Target Sensing via Multi-IRS-Aided Radar

no code implementations11 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.

Moving Target Detection via Multi-IRS-Aided OFDM Radar

no code implementations24 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.

Near-Field Low-WISL Unimodular Waveform Design for Terahertz Automotive Radar

no code implementations8 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.

HDR Imaging With One-Bit Quantization

no code implementations7 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.

Quantization

Submodular Optimization for Placement of Intelligent Reflecting Surfaces in Sensing Systems

no code implementations22 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.

Automotive Radar Sensing with Sparse Linear Arrays Using One-Bit Hankel Matrix Completion

no code implementations9 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.

Matrix Completion Quantization

Collaborative Automotive Radar Sensing via Mixed-Precision Distributed Array Completion

no code implementations13 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.

Matrix Completion Quantization

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