Deep learning (DL) has proven its unprecedented success in diverse fields such as
computer vision, natural language processing, and speech recognition by its strong …

In wireless communication, the transmitted signal is subjected to distortion, noise, frequency
shift, non-linear attenuation, fading, and so on due to the inherent nature of the physical …

We design a self-exploratory reinforcement learning (RL) framework, based on the Q-
learning algorithm, that enables the base station (BS) to choose a suitable modulation and …

Deep learning (DL) based autoencoder (AE) has been proposed recently as a promising,
and potentially disruptive approach to design the physical layer of beyond-5G …

Molecular communication (MC) system is an emerging technology for nanoscale networks.
Therefore, there is a requirement to develop a new end-to-end MC model, which may deliver …

End-to-end deep learning for communication systems, ie, systems whose encoder and
decoder are learned, has attracted significant interest recently, due to its performance which …

Spurred by the recent advances in deep learning to harness rich information hidden in large
volumes of data and to tackle problems that are hard to model/solve (eg, resource allocation …

Emerging recently as a novel concept in communication system design, end-to-end learning
introduces deep neural networks (NNs) to represent the transmitter and receiver functions …

We design short blocklength codes for the Gaussian wiretap channel under information-
theoretic security guarantees. Our approach consists in decoupling the reliability and …

Leveraging powerful deep learning techniques, the end-to-end (E2E) learning of
communication system is able to outperform the classical communication system …