Accretionary Learning with Deep Neural Networks

no code implementations • 21 Nov 2021 • Xinyu Wei, Biing-Hwang Fred Juang, Ouya Wang, Shenglong Zhou, Geoffrey Ye Li

In this paper, we propose a new learning method named Accretionary Learning (AL) to emulate human learning, in that the set of objects to be recognized may not be pre-specified.

Deep Learning based End-to-End Wireless Communication Systems with Conditional GAN as Unknown Channel

no code implementations • 6 Mar 2019 • Hao Ye, Le Liang, Geoffrey Ye Li, Biing-Hwang Fred Juang

We propose to use a conditional generative adversarial net (GAN) to represent channel effects and to bridge the transmitter DNN and the receiver DNN so that the gradient of the transmitter DNN can be back-propagated from the receiver DNN.

Information Theory Information Theory

Channel Agnostic End-to-End Learning based Communication Systems with Conditional GAN

no code implementations • 2 Jul 2018 • Hao Ye, Geoffrey Ye Li, Biing-Hwang Fred Juang, Kathiravetpillai Sivanesan

In this article, we use deep neural networks (DNNs) to develop a wireless end-to-end communication system, in which DNNs are employed for all signal-related functionalities, such as encoding, decoding, modulation, and equalization.

Information Theory Information Theory