Given the extensive and growing capabilities offered by deep learning (DL), more
researchers are turning to DL to address complex challenges in next-generation (xG) …

The last decade has witnessed the rapid growth of deep learning (DL) applications in
wireless communications, especially for channel estimation and signal detection. However …

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 digital wireless communications, the received signal can be strongly altered by the
environment and may contain Inter-Symbol Interference (ISI). To remove or reduce the ISI ie …

This paper presents SLRTNet, a deep learning model designed for channel estimation in
OFDM systems. SLRTNet employs a super-resolution network in combination with a …

Deep learning (DL) has been gradually applied to wireless communication and has
achieved remarkable results. However, training a DL model requires numerous data, and an …

As only a few parts of wireless resources can be utilized for pilot transmission, channel
estimation, especially the interpolation process, has often been recognized as a challenging …

Estimation of the multipath channel in 60‐GHz communications is challenging, because the
channel may be sparse or dense during beam training. Specifically, because of the variation …

This paper presents SLRTNet, a deep learning model designed for channel estimation in
OFDM systems. SLRTNet employs a super-resolution network in combination with a …

Due to the emerging challenges with future 6G networks such as high data rates and the
need for remarkably low latency, future wireless communication systems must be planned …