In model-driven deep learning (DL)-based channel estimation methods for orthogonal
frequency division multiplexing (OFDM) systems, all input features obtained through …

Channel estimation using neural networks has proven to be a breakthrough technology in
the communications field. However, to achieve good performance, the existing studies …

In various practical orthogonal frequency-division multiplexing (OFDM) systems, the
estimation accuracy at the receiver is challenging, and, specifically when operate over time …

In this paper, we apply deep learning to the channel estimation problem of OFDM. Precisely
speaking, to reduce the influence of noise on LS channel estimation, we design a channel …

In wireless communication orthogonal frequency division multiplexing (OFDM) systems with
high mobility, channel estimation becomes challenging due to the double selectivity of …

Deep learning (DL) has exhibited notable effectiveness in channel estimation for orthogonal
frequency division multiplexing (OFDM) systems. However, most existing neural networks …

Deep learning (DL) has been widely utilized for channel estimation and has resulted in
significant performance improvements. However, most existing research only performs …

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 …

Recently, in order to achieve performance improvement in scenarios where the channel is
either unknown, or too complex for an analytical description, Neural Network (NN) based …

As the key to wireless communication, channel estimation has become a hot research topic
in recent years. In this paper, we propose a deep learning method based on the channel …