This letter presents our initial results in deep learning for channel estimation and signal
detection in orthogonal frequency-division multiplexing (OFDM) systems. In this letter, we …

In this letter, we propose a novel deep learning based approach for joint channel estimation
and signal detection in orthogonal frequency division multiplexing (OFDM) systems by …

In this letter, we propose a model-driven deep learning (DL) approach that combines DL
with the expert knowledge to replace the existing orthogonal frequency-division multiplexing …

This paper develops novel deep learning-based architectures and design methodologies for
an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one …

In this letter we apply deep learning tools to conduct channel estimation for an orthogonal
frequency division multiplexing (OFDM) system based on downlink pilots. To be specific, a …

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 letter, we present a downlink pilot design scheme for Deep Learning (DL) based
channel estimation (ChannelNet) in orthogonal frequency-division multiplexing (OFDM) …

Coherent OFDM detection requires accurate channel state information (CSI). Mobile radio
channels are both time and frequency dispersive, especially at high vehicular speeds, which …

The (inverse) discrete Fourier transform (DFT/IDFT) is often perceived as essential to
orthogonal frequency-division multiplexing (OFDM) systems. In this paper, a deep complex …

Channel estimation is very challenging for multiple-input and multiple-output orthogonal
frequency division multiplexing (MIMO-OFDM) systems in high mobility environments with …