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Mojtaba Soltanalian - IEEE Xplore Author Profile
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Mojtaba Soltanalian

Also published under: M. Soltanalian

Publications
115
Citations
1,826
Publications by Year
20092025
Co-Authors:
Show All Co-Authors (97)
Image of author Mojtaba Soltanalian

Mojtaba Soltanalian

Also published under: M. Soltanalian

Affiliation

Electrical and Computer Engineering
University of Illinois Chicago
Chicago, IL, USA

Publication Topics

Biography

Mojtaba Soltanalian (S’08–M’14) received the Ph.D. degree in electrical engineering (with specialization in signal processing) from the Department of Information Technology, Uppsala University, Sweden, in 2014.,He held research positions with the Interdisciplinary Centre for Security, Reliability and Trust (SnT, University of Luxembourg), and California Institute of Technology. He is currently with the faculty of the Electrical and Computer Engineering Department, University of Illinois at Chicago. His research interests include the interplay of signal processing and optimization theory, and specifically different ways the optimization theory can facilitate a better processing and design of signals for collecting information, communication,...
Publications
115
Citations
1,826
Publications by Year
20092025
Co-Authors:
Show All Co-Authors (97)
Author's Published Works

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Streamlining UNO: A Generalized Sampling Approach to Optimal One-Bit Modulo Sensing

Arian Eamaz;Farhang Yeganegi;Mojtaba Soltanalian

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
Recently, one-bit modulo sampling, also known as unlimited one-bit (UNO), has been proposed as a bridge between modulo sampling and coarse quantization. This approach successfully combines the benefits of both techniques by providing efficient, low-cost quantization for modulo sampling while also offering a natural method for designing dithers that are uniformly distributed across the signal’s dyn...Show More

Streamlining UNO: A Generalized Sampling Approach to Optimal One-Bit Modulo Sensing

Arian Eamaz;Farhang Yeganegi;Mojtaba Soltanalian

Year: 2025 | Conference Paper |

Collaborative Automotive Radar Sensing via Mixed-Precision Distributed Array Completion

Arian Eamaz;Farhang Yeganegi;Yunqiao Hu;Mojtaba Soltanalian;Shunqiao Sun

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
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. The mixed quantization precision significantly reduces the data amount that needs to be shared from radar nodes to the fusion center for coherent processing. We utilize the low-rank properties inherent ...Show More

Collaborative Automotive Radar Sensing via Mixed-Precision Distributed Array Completion

Arian Eamaz;Farhang Yeganegi;Yunqiao Hu;Mojtaba Soltanalian;Shunqiao Sun

Year: 2025 | Conference Paper |

Beyond Diagonal RIS: Key to Next-Generation Integrated Sensing and Communications?

Tara Esmaeilbeig;Kumar Vijay Mishra;Mojtaba Soltanalian

IEEE Signal Processing Letters
Year: 2025 | Volume: 32 | Journal Article |
Cited by: Papers (2)
Reconfigurable intelligent surfaces (RIS) offer unprecedented flexibility for smart wireless channels. Recent research shows that RIS platforms enhance signal quality, coverage, and link capacity in integrated sensing and communication (ISAC) systems. This paper explores the use of fully-connected beyond diagonal RIS (BD-RIS) in ISAC. BD-RIS provides additional degrees of freedom by allowing non-z...Show More

Beyond Diagonal RIS: Key to Next-Generation Integrated Sensing and Communications?

Tara Esmaeilbeig;Kumar Vijay Mishra;Mojtaba Soltanalian

Year: 2025 | Volume: 32 | Journal Article |

Antenna Failure Resilience: Deep Learning-Enabled Robust DOA Estimation with Single Snapshot Sparse Arrays

Ruxin Zheng;Shunqiao Sun;Hongshan Liu;Honglei Chen;Mojtaba Soltanalian;Jian Li

2024 58th Asilomar Conference on Signals, Systems, and Computers
Year: 2024 | Conference Paper |
Recent advancements in Deep Learning (DL) for Direction of Arrival (DOA) estimation have highlighted its superiority over traditional methods, offering faster inference, enhanced super-resolution, and robust performance in low Signal-to-Noise Ratio (SNR) environments. Despite these advancements, existing research predominantly focuses on multi-snapshot scenarios, a limitation in the context of aut...Show More

Antenna Failure Resilience: Deep Learning-Enabled Robust DOA Estimation with Single Snapshot Sparse Arrays

Ruxin Zheng;Shunqiao Sun;Hongshan Liu;Honglei Chen;Mojtaba Soltanalian;Jian Li

Year: 2024 | Conference Paper |

Towards Data-Driven Guarantees for Sparse Signal Recovery

Farhang Yeganegi;Arian Eamaz;Mojtaba Soltanalian

2024 58th Asilomar Conference on Signals, Systems, and Computers
Year: 2024 | Conference Paper |
The recovery of sparse signals from linear measurements can typically be formulated as quadratic optimization problems while adhering to the sparsity constraint. We present a novel approach to sparse signal recovery, generally referred to as MERIT, that provides data-aware guarantees for the obtained solutions. Specifically, we show that in case theoretical guarantees exist, our data-driven result...Show More

Towards Data-Driven Guarantees for Sparse Signal Recovery

Farhang Yeganegi;Arian Eamaz;Mojtaba Soltanalian

Year: 2024 | Conference Paper |

Deep Learning-Enabled One-Bit DoA Estimation

Farhang Yeganegi;Arian Eamaz;Tara Esmaeilbeig;Mojtaba Soltanalian

2024 IEEE 13rd Sensor Array and Multichannel Signal Processing Workshop (SAM)
Year: 2024 | Conference Paper |
Unrolled deep neural networks have attracted significant attention for their success in various practical applications. In this paper, we explore an application of deep unrolling in the direction of arrival (DoA) estimation problem when coarse quantization is applied to the measurements. We present a compressed sensing formulation for DoA estimation from one-bit data in which estimating target DoA...Show More

Deep Learning-Enabled One-Bit DoA Estimation

Farhang Yeganegi;Arian Eamaz;Tara Esmaeilbeig;Mojtaba Soltanalian

Year: 2024 | Conference Paper |

HDR Imaging with One-Bit Quantization

Arian Eamaz;Farhang Yeganegi;Mojtaba Soltanalian

2024 IEEE 13rd Sensor Array and Multichannel Signal Processing Workshop (SAM)
Year: 2024 | Conference Paper |
Cited by: Papers (2)
Modulo sampling and dithered one-bit quantization frame-works have emerged as promising solutions to overcome the limitations of traditional analog-to-digital converters (ADCs) and sensors. Modulo sampling, with its high-resolution approach utilizing modulo ADCs, offers an unlimited dynamic range, while dithered one-bit quantization offers cost-efficiency and reduced power consumption while operat...Show More

HDR Imaging with One-Bit Quantization

Arian Eamaz;Farhang Yeganegi;Mojtaba Soltanalian

Year: 2024 | Conference Paper |

Matrix Completion from One-Bit Dither Samples

Arian Eamaz;Farhang Yeganegi;Mojtaba Soltanalian

IEEE Transactions on Signal Processing
Year: 2024 | Early Access Article |
Cited by: Papers (2)
We explore the impact of coarse quantization on matrix completion in the extreme scenario of dithered one-bit sensing, where the matrix entries are compared with random dither levels. In particular, instead of observing a subset of high-resolution entries of a low-rank matrix, we have access to a small number of one-bit samples, generated as a result of these comparisons. In order to recover the l...Show More

Matrix Completion from One-Bit Dither Samples

Arian Eamaz;Farhang Yeganegi;Mojtaba Soltanalian

Year: 2024 | Early Access Article |

Low-rank Matrix Sensing With Dithered One-Bit Quantization

Farhang Yeganegi;Arian Eamaz;Mojtaba Soltanalian

2024 IEEE International Symposium on Information Theory (ISIT)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
We explore the impact of coarse quantization on low-rank matrix sensing in the extreme scenario of dithered one-bit sampling, where the high-resolution measurements are compared with random time-varying threshold levels. To recover the low-rank matrix of interest from the highly-quantized collected data, we offer an enhanced randomized Kaczmarz algorithm that efficiently solves the emerging highly...Show More

Low-rank Matrix Sensing With Dithered One-Bit Quantization

Farhang Yeganegi;Arian Eamaz;Mojtaba Soltanalian

Year: 2024 | Conference Paper |

Harnessing the Power of Sample Abundance: Theoretical Guarantees and Algorithms for Accelerated One-Bit Sensing

Arian Eamaz;Farhang Yeganegi;Deanna Needell;Mojtaba Soltanalian

IEEE Transactions on Information Theory
Year: 2024 | Volume: 70, Issue: 9 | Journal Article |
Cited by: Papers (3)
One-bit quantization with time-varying sampling thresholds (also known as random dithering) has recently found significant utilization potential in statistical signal processing applications due to its relatively low power consumption and low implementation cost. In addition to such advantages, an attractive feature of one-bit analog-to-digital converters (ADCs) is their superior sampling rates as...Show More

Harnessing the Power of Sample Abundance: Theoretical Guarantees and Algorithms for Accelerated One-Bit Sensing

Arian Eamaz;Farhang Yeganegi;Deanna Needell;Mojtaba Soltanalian

Year: 2024 | Volume: 70, Issue: 9 | Journal Article |

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

Arian Eamaz;Farhang Yeganegi;Yunqiao Hu;Shunqiao Sun;Mojtaba Soltanalian

2024 IEEE Radar Conference (RadarConf24)
Year: 2024 | Conference Paper |
Cited by: Papers (6)
The design of sparse linear arrays has proven instrumental in the implementation of cost-effective and efficient automotive radar systems for high-resolution imaging. This paper investigates the impact of coarse quantization on measurements obtained from such arrays. To recover azimuth angles from quantized measurements, we leverage the low-rank properties of the constructed Hankel matrix. In part...Show More

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

Arian Eamaz;Farhang Yeganegi;Yunqiao Hu;Shunqiao Sun;Mojtaba Soltanalian

Year: 2024 | Conference Paper |

Space-Time Adaptive Processing for Radars in Connected and Automated Vehicular Platoons

Zahra Esmaeilbeig;Kumar Vijay Mishra;Mojtaba Soltanalian

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2024 | Conference Paper |
In this study, we develop a holistic framework for space-time adaptive processing (STAP) in connected and automated vehicle (CAV) radar systems. We investigate a CAV system consisting of multiple vehicles that transmit frequency-modulated continuous-waveforms (FMCW), thereby functioning as a multistatic radar. Direct application of STAP in a network of radar systems such as in a CAV may lead to ex...Show More

Space-Time Adaptive Processing for Radars in Connected and Automated Vehicular Platoons

Zahra Esmaeilbeig;Kumar Vijay Mishra;Mojtaba Soltanalian

Year: 2024 | Conference Paper |

UNO: Unlimited Sampling Meets One-Bit Quantization

Arian Eamaz;Kumar Vijay Mishra;Farhang Yeganegi;Mojtaba Soltanalian

IEEE Transactions on Signal Processing
Year: 2024 | Volume: 72 | Journal Article |
Cited by: Papers (17)
Recent results in one-bit sampling provide a framework for a relatively low-cost, low-power sampling, at a high rate by employing time-varying sampling threshold sequences. Another recent development in sampling theory is unlimited sampling, which is a high-resolution technique that relies on modulo ADCs to yield an unlimited dynamic range. In this paper, we leverage the appealing attributes of th...Show More

UNO: Unlimited Sampling Meets One-Bit Quantization

Arian Eamaz;Kumar Vijay Mishra;Farhang Yeganegi;Mojtaba Soltanalian

Year: 2024 | Volume: 72 | Journal Article |

Recurrent Waveform Optimization for Desired Range-Doppler Profile With Low Probability of Interception: A Particle Filter Approach

Xiang Feng;Linlong Wu;Yinan Zhao;Zhanfeng Zhao;Keyang Diao;Mojtaba Soltanalian

IEEE Transactions on Aerospace and Electronic Systems
Year: 2024 | Volume: 60, Issue: 2 | Journal Article |
Cited by: Papers (3)
Modern cognitive radars, armed with knowledge-aided waveforms, exhibit considerable effectiveness in detecting low-speed, small radar cross-section (RCS) targets, while demonstrating resilience in electronic countermeasure scenarios. In this article, we introduce a novel technique named particle filter-based recurrent waveform optimization (ALTERATION) to design unimodular waveforms with tailored ...Show More

Recurrent Waveform Optimization for Desired Range-Doppler Profile With Low Probability of Interception: A Particle Filter Approach

Xiang Feng;Linlong Wu;Yinan Zhao;Zhanfeng Zhao;Keyang Diao;Mojtaba Soltanalian

Year: 2024 | Volume: 60, Issue: 2 | Journal Article |

MaRLI: Attack on the Discrete-Phase WISL Minimization Problem

Arian Eamaz;Farhang Yeganegi;Mojtaba Soltanalian

IEEE Transactions on Signal Processing
Year: 2024 | Volume: 72 | Journal Article |
Cited by: Papers (4)
To facilitate target localization, active radar signals or sequences are designed to have low auto-correlation. This goal is typically achieved by the minimization of the auto-correlation integrated sidelobe level (ISL) metric, or the more general, weighted version of the ISL, known as the WISL metric. In this paper, we introduce two problem formulations to address unimodular quartic program, whic...Show More

MaRLI: Attack on the Discrete-Phase WISL Minimization Problem

Arian Eamaz;Farhang Yeganegi;Mojtaba Soltanalian

Year: 2024 | Volume: 72 | Journal Article |

Global One-Bit Phase Retrieval via Sample Abundance-Including an Application to STFT Measurements

Arian Eamaz;Farhang Yeganegi;Mojtaba Soltanalian

2023 57th Asilomar Conference on Signals, Systems, and Computers
Year: 2023 | Conference Paper |
We introduce a novel technique for reconstructing signals from phaseless measurements obtained through one-bit ADCs. Our approach adopts the Sampling Kaczmarz-Motzkin algorithm to efficiently and globally optimize the phase retrieval objective. In particular, it utilizes the sample abundance provided in one-bit sensing with time-varying thresholds to overcome the limitations of traditional convex ...Show More

Global One-Bit Phase Retrieval via Sample Abundance-Including an Application to STFT Measurements

Arian Eamaz;Farhang Yeganegi;Mojtaba Soltanalian

Year: 2023 | Conference Paper |

Unlimited Sampling of FRI Signals with Dithered One-Bit Quantization

Arian Eamaz;Farhang Yeganegi;Kumar Vijay Mishra;Mojtaba Soltanalian

2023 57th Asilomar Conference on Signals, Systems, and Computers
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Recent research in sampling theory suggests utilizing unlimited sampling with one-bit quantization time-varying threshold (UNO) for bandlimited signals. The UNO framework addresses the problems associated with the finite dynamic range and limited quantization levels in conventional ADCs. However, the input signal in many engineering applications is not bandlimited. Of particular interest are the f...Show More

Unlimited Sampling of FRI Signals with Dithered One-Bit Quantization

Arian Eamaz;Farhang Yeganegi;Kumar Vijay Mishra;Mojtaba Soltanalian

Year: 2023 | Conference Paper |

Submodular Optimization for Placement of Intelligent Reflecting Surfaces in Sensing Systems

Zahra Esmaeilbeig;Kumar Vijay Mishra;Arian Eamaz;Mojtaba Soltanalian

2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
Year: 2023 | Conference Paper |
Cited by: Papers (3)
Intelligent reflecting surfaces (IRS) are increasingly being investigated for novel sensing applications for improving target estimation and detection. While the optimization of IRS phase-shifts has been studied extensively, the optimal placement of multiple IRS platforms for sensing applications is relatively unexamined. In this paper, we focus on selecting optimized locations of IRS platforms by...Show More

Submodular Optimization for Placement of Intelligent Reflecting Surfaces in Sensing Systems

Zahra Esmaeilbeig;Kumar Vijay Mishra;Arian Eamaz;Mojtaba Soltanalian

Year: 2023 | Conference Paper |

Deep Learning Meets Adaptive Filtering: A Stein’s Unbiased Risk Estimator Approach

Zahra Esmaeilbeig;Mojtaba Soltanalian

2023 59th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
Year: 2023 | Conference Paper |
Cited by: Papers (3)
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. Building upon the unrolling methodology, we introduce novel task-based deep learning frameworks, denoted as Deep RLS and Deep EASI. These architectures transform the iterations of the original algorithms in...Show More

Deep Learning Meets Adaptive Filtering: A Stein’s Unbiased Risk Estimator Approach

Zahra Esmaeilbeig;Mojtaba Soltanalian

Year: 2023 | Conference Paper |

Unlimited Sampling via One-Bit Quantization

Arian Eamaz;Kumar Vijay Mishra;Farhang Yeganegi;Mojtaba Soltanalian

2023 International Conference on Sampling Theory and Applications (SampTA)
Year: 2023 | Conference Paper |
Cited by: Papers (11)
Shannon’s sampling theorem plays a central role in the discrete-time processing of bandlimited signals. However, the infinite precision assumed by Shannon’s theorem is impractical because of the ADC clipping effect that limits the signal’s dynamic range. Moreover, the power consumption of an analog-to-digital converter (ADC) increases linearly with the sampling frequency and may be prohibitively h...Show More

Unlimited Sampling via One-Bit Quantization

Arian Eamaz;Kumar Vijay Mishra;Farhang Yeganegi;Mojtaba Soltanalian

Year: 2023 | Conference Paper |

One-Bit Matrix Completion With Time-Varying Sampling Thresholds

Arian Eamaz;Farhang Yeganegi;Mojtaba Soltanalian

2023 International Conference on Sampling Theory and Applications (SampTA)
Year: 2023 | Conference Paper |
Cited by: Papers (4)
We explore the impact of coarse quantization on matrix completion in the extreme scenario of generalized one-bit sampling, where the matrix entries are compared with time-varying threshold levels. In particular, instead of observing a subset of high-resolution entries of a low-rank matrix, we have access to a small number of one-bit samples, generated as a result of these comparisons. To recover t...Show More

One-Bit Matrix Completion With Time-Varying Sampling Thresholds

Arian Eamaz;Farhang Yeganegi;Mojtaba Soltanalian

Year: 2023 | Conference Paper |

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

Arian Eamaz;Farhang Yeganegi;Kumar Vijay Mishra;Mojtaba Soltanalian

2023 31st European Signal Processing Conference (EUSIPCO)
Year: 2023 | Conference Paper |
Cited by: Papers (4)
Conventional sensing applications rely on electromagnetic far-field channel models with plane wave propagation. However, recent ultra-short-range automotive radar applications at upper millimeter-wave or low terahertz (THz) frequencies envisage operation in the near-field region, where the wavefront is spherical. Unlike far-field, the near-field beampattern is dependent on both range and angle, th...Show More

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

Arian Eamaz;Farhang Yeganegi;Kumar Vijay Mishra;Mojtaba Soltanalian

Year: 2023 | Conference Paper |

Mutual Interference Mitigation in PMCW Automotive Radar

Zahra Esmaeilbeig;Arindam Bose;Mojtaba Soltanalian

2023 20th European Radar Conference (EuRAD)
Year: 2023 | Conference Paper |
Cited by: Papers (3)
This paper addresses the challenge of mutual interference (MI) in phase-modulated continuous wave (PMCW) millimeter-wave (mmWave) automotive radar systems. The increasing demand for advanced driver assistance systems (ADAS) has led to a proliferation of vehicles equipped with mmWave radar systems that operate in the same frequency band, resulting in MI that can degrade radar performance creating s...Show More

Mutual Interference Mitigation in PMCW Automotive Radar

Zahra Esmaeilbeig;Arindam Bose;Mojtaba Soltanalian

Year: 2023 | Conference Paper |

One-Bit Quadratic Compressed Sensing: From Sample Abundance to Linear Feasibility

Arian Eamaz;Farhang Yeganegi;Deanna Needell;Mojtaba Soltanalian

2023 IEEE International Symposium on Information Theory (ISIT)
Year: 2023 | Conference Paper |
Cited by: Papers (6)
One-bit quantization with time-varying sampling thresholds has recently found significant utilization potential in statistical signal processing applications due to its relatively low power consumption and low implementation cost. In addition to such advantages, an attractive feature of one-bit analog-to-digital converters (ADCs) is their superior sampling rates as compared to their conventional m...Show More

One-Bit Quadratic Compressed Sensing: From Sample Abundance to Linear Feasibility

Arian Eamaz;Farhang Yeganegi;Deanna Needell;Mojtaba Soltanalian

Year: 2023 | Conference Paper |

Quantized Phase-Shift Design Of Active IRS For Integrated Sensing And Communications

Zahra Esmaeilbeig;Arian Eamaz;Kumar Vijay Mishra;Mojtaba Soltanalian

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)
Year: 2023 | Conference Paper |
Cited by: Papers (12)
Integrated sensing and communications (ISAC) is a spectrum-sharing paradigm that allows different users to jointly utilize and access the crowded electromagnetic spectrum. In this context, intelligent reflecting surfaces (IRSs) have lately emerged as an enabler for non-line-of-sight (NLoS) ISAC. Prior IRS-aided ISAC studies assume passive surfaces and rely on the continuous-valued phase-shift mode...Show More

Quantized Phase-Shift Design Of Active IRS For Integrated Sensing And Communications

Zahra Esmaeilbeig;Arian Eamaz;Kumar Vijay Mishra;Mojtaba Soltanalian

Year: 2023 | Conference Paper |

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