Historically, most of the channel encoding and decoding algorithms have been designed to
deal with and evaluated under the additive white Gaussian noise (AWGN) channel …

Recently, deep learning methods have shown significant improvements in communication
systems. In this paper, we study the equalization problem over the nonlinear channel using …

In this paper, we address the application of deep learning in signal equalization, presenting
an end-to-end learned method based on convolutional neural network (CNN) to directly …

In this paper, we revisit the idea of using deep neural network for channel equalization to
account for nonlinear channel distortions as well as temporal variations of radio signals. Our …

We consider the problem of recovering channel code parameters over a candidate set by
merely analyzing the received encoded signals. We propose a deep learning-based …

The essential criteria of wireless communication system are accurate signal identification.
The channel assessment and adjustment are the two most critical mechanisms for signal …

Deep neural networks (DNNs) based digital receivers can potentially operate in complex
environments. How-ever, the dynamic nature of communication channels implies that in …

In this paper, we introduce a new approach for nonlinear equalization based on Gaussian
processes for classification (GPC). We propose to measure the performance of this equalizer …

Deep learning has a wide application in the area of natural language processing and image
processing due to its strong ability of generalization. In this paper, we propose a novel …

This paper analyzes some emerging techniques from the broad area of Bayesian learning
for the design of iterative receivers for single-carrier transmissions using bit-interleaved …