In this paper, we propose a new deep neural network (DNN)-based channel estimation
method for the Rayleigh fading channel model. While deep learning has been considered …

Channel Estimation is one of the essential tasks to realize a reconfigurable intelligent
surface (RIS)-aided orthogonal frequency division multiplexing (OFDM) communication …

In this paper, we present a two‐stage scalable channel estimator (TSCE), a deep learning
(DL)‐based scalable, and robust channel estimator for wireless cellular networks, which is …

In this paper, we propose a deep learning model for Demodulation Reference Signal
(DMRS) based channel estimation task. Specifically, a novel Denoise, Linear interpolation …

The big success of deep learning (DL) has motivated the application of DL to wireless
systems in recent years. In this paper, we explain issues and corresponding solutions to be …

The use of machine learning methods to tackle challenging physical layer signal processing
tasks has attracted significant attention. In this work, we focus on the use of neural networks …

Wireless channel estimation (CE) in vehicular communication networks is challenging due
to mobility, and various data-pilot-aided (DPA) and deep learning (DL) techniques have …

Deep learning (DL) has emerged as an effective tool for channel estimation in wireless
communication systems, especially under some imperfect environments. However, even …

In vehicular communications using IEEE 802.11 p, estimating channel frequency response
(CFR) is a remarkably challenging task. The challenge for channel estimation (CE) lies in …

Deep Learning has rendered overwhelming potential to support the rising interest of high
dependability and maximum capacity wireless communication systems. Channel estimation …