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 …

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 model-driven deep learning (DL) approach that combines DL
with the expert knowledge to replace the existing orthogonal frequency-division multiplexing …

Accurate signal detection is one of the most important requirements of wireless
communication systems. The two most important processes of the signal detection are …

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 …

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

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 …

In this paper, an iterative receiver for a joint data-detection and channel-estimation scheme
is presented for orthogonal frequency-division multiplexing systems, which incorporates …

Orthogonal frequency division multiplexing (OFDM), usually with sufficient cyclic prefix (CP),
has been widely applied in various communication systems. The CP in OFDM consumes …

In this paper, we devise a highly efficient machine learning-based channel estimation for
orthogonal frequency division multiplexing (OFDM) systems, in which the training of the …