11 citations · 23 across the 7 of their papers we have counts for
14 papers
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…
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…
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…
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…