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

We propose generative channel modeling to learn statistical channel models from channel
input-output measurements. Generative channel models can learn more complicated …

Consider an Internet-of-Things (IoT) scenario in which devices transmit sporadically using
short packets with few pilot symbols. Each device transmits over a fading channel and is …

We propose and practically demonstrate a joint detection and decoding scheme for short-
packet wireless communications in scenarios that require to first detect the presence of a …

In this paper, we aim to efficiently and accurately predict the static channel impulse response
(CIR) with only the user's position information and a set of channel instances obtained within …

Deep neural networks (NNs) have exhibited considerable potential for efficiently balancing
the performance and complexity of multiple-input and multiple-output (MIMO) detectors …

The increasing complexity on channel modeling and the cost on collecting plenty of high-
quality wireless channel data have become the main bottlenecks of developing deep …

Channel state information (CSI) plays a vital role in wireless communication systems.
However, the CSI acquisition overhead is an enormous obstacle to realize the system …

Improving the accuracy of channel estimation is a significant topic in the context of wireless
communications. For training-based channel estimations, increasing the length of a training …

High quality data is essential in deep learning to train a robust model. While in other fields
data is sparse and costly to collect, in error decoding it is free to query and label thus …