Mahmoud Farhang | Searcharxiv

arXiv2025

Neural Network-Based Intelligent Reflecting Surface Assisted Direction of Arrival Estimation

Direction-of-Arrival (DoA) estimation assisted with an Intelligent Reflecting Surface (IRS) is crucial for various wireless applications, especially in challenging Non-Line-of-Sight (NLoS) environments. This paper presents a novel neural network-based architecture to address this challenge. The key innovation is the introduction of a dedicated, learnable IRS layer integrated within a carefully designed end-to-end system established upon the physical and geometrical basis of the problem. Unlike conventional neural network layers, this specific one incorporates block diagonal sinusoidal weight constraints, where the phase arguments of these sinusoids are learned during training to directly emulate the phase shifts of the IRS elements. This allows the end-to-end system to optimize the IRS configuration for enhanced DoA estimation, eliminating the need for separate IRS optimization algorithms. Moreover, different DoA regression networks, including a proposed structure, are presented and examined. Numerical simulations, conducted under various conditions and noise levels, where controlled coherent multi-path components are introduced due to the presence of the IRS, demonstrate the superior performance of the novel end-to-end system compared to others and highlight its potential to significantly improve the accuracy of DoA estimation in complex IRS-assisted wireless systems. Besides, corresponding computational complexities of different approaches are also compared.

eess.SP↗

arXiv2023

A deep convolutional neural network for salt-and-pepper noise removal using selective convolutional blocks

In recent years, there has been an unprecedented upsurge in applying deep learning approaches, specifically convolutional neural networks (CNNs), to solve image denoising problems, owing to their superior performance. However, CNNs mostly rely on Gaussian noise, and there is a conspicuous lack of exploiting CNNs for salt-and-pepper (SAP) noise reduction. In this paper, we proposed a deep CNN model, namely SeConvNet, to suppress SAP noise in gray-scale and color images. To meet this objective, we introduce a new selective convolutional (SeConv) block. SeConvNet is compared to state-of-the-art SAP denoising methods using extensive experiments on various common datasets. The results illustrate that the proposed SeConvNet model effectively restores images corrupted by SAP noise and surpasses all its counterparts at both quantitative criteria and visual effects, especially at high and very high noise densities.

cs.CV↗