Traditional communication system design has always been based on the paradigm of first
establishing a mathematical model of the communication channel, then designing and …

Machine learning methods adapt the parameters of a model, constrained to lie in a given
model class, by using a fixed learning procedure based on data or active observations …

End-to-end learning of communication systems enables joint optimization of transmitter and
receiver, implemented as deep neural network (NN)-based autoencoders, over any type of …

This paper studies the fast adaptive beamforming for the multiuser multiple-input single-
output downlink. Existing deep learning-based approaches assume that training and testing …

In this article, we use deep neural networks (DNNs) to develop an end-to-end wireless
communication system, in which DNNs are employed for all signal-related functionalities …

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 …

In this paper, we propose an end-to-end deep learning-based joint transceiver design
algorithm for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) …

Recent research in the design of end to end communication system using deep learning has
produced models which can outperform traditional communication schemes. Most of these …

Large antenna arrays and beamforming are necessary for the mmWave communication
system, resulting in heavy time and energy consumption in the beam training stage …

In this paper, we propose two deep learning (DL) based receiver schemes in uplink multiple-
input multiple-output (MIMO) systems. In the first scheme, we design a pilot-assisted MIMO …