Channel estimation is a fundamental challenge in wireless communication systems, as
accurate knowledge of the communication channel is essential for achieving reliable data …

In this paper, we propose an encoder-decoder neural architecture (called Channelformer) to
achieve improved channel estimation for orthogonal frequency-division multiplexing (OFDM) …

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 …

Research on machine learning for channel estimation, especially neural network solutions
for wireless communications, is attracting significant current interest. This is because …

Channel estimation for Orthogonal Frequency Division Multiplexing (OFDM) transmission is
well investigated with model based approaches. Recent effort also explores the data driven …

In this paper, we deploy the self-attention mechanism to achieve improved channel
estimation for orthogonal frequency-division multiplexing waveforms in the downlink …

The growth of frequency bandwidths and applications with the forthcoming generations of
wireless networks will give rise to a multitude of wireless transmission scenarios, topologies …

Due to the easy affordable access to powerful mobile devices, cloud services, and high
global mobile broadband internet penetration, users consume more data than ever. This …

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

In this paper, we propose a method to design the training data that can support robust
generalization of trained neural networks to unseen channels. The proposed design that …