End-to-end learning of a communications system using the deep learning-based
autoencoder concept has drawn interest in recent research due to its simplicity, flexibility …

Present-day communication systems routinely use codes that approach the channel
capacity when coupled with a computationally efficient decoder. However, the decoder is …

Standard decoding approaches rely on model-based channel estimation methods to
compensate for varying channel effects, which degrade in performance whenever there is a …

With the rapid emergence of advanced technologies for wireless communications, automatic
modulation classification (AMC) has been deployed in the physical layer to blindly identify …

We present and discuss several novel applications of deep learning for the physical layer.
By interpreting a communications system as an autoencoder, we develop a fundamental …

The design and implementation of conventional communication systems are based on
strong probabilistic models and assumptions. These fixed and conventional communication …

In this paper, we propose MCformer-a novel deep neural network for the automatic
modulation classification task of complex-valued raw radio signals. MCformer architecture …

We investigate joint source channel coding (JSCC) for wireless image transmission over
multipath fading channels. Inspired by recent works on deep learning based JSCC and …

Deep Learning has been widely applied in the area of image processing and natural
language processing. In this paper, we propose an end-to-end communication structure …

We demonstrate a novel method for the automatic modulation classification based on a
deep learning autoencoder network, trained by a nonnegativity constraint algorithm. The …