We extend the idea of end-to-end learning of communications systems through deep neural
network (NN)-based autoencoders to orthogonal frequency division multiplexing (OFDM) …

The idea of end-to-end learning of communications systems through neural network (NN)-
based autoencoders has the shortcoming that it requires a differentiable channel model. We …

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

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 …

The benefits of end-to-end learning has been demonstrated over AWGN channels but has
not yet been quantified over realistic wireless channel models. This work aims to fill this gap …

End-to-end learning of communications systems is a fascinating novel concept that has so
far only been validated by simulations for block-based transmissions. It allows learning of …

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 …

The idea of end-to-end learning of communication systems through neural network (NN)-
based autoencoders has the shortcoming that it requires a differentiable channel model. We …

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 …

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 …