A canonical wireless communication system consists of a transmitter and a receiver. The
information bit stream is transmitted after coding, modulation, and pulse shaping. Due to the …

Recent years have witnessed growing interest in the application of deep neural networks
(DNNs) for receiver design, which can potentially be applied in complex environments …

Deep learning has solved many problems that are out of reach of heuristic algorithms. It has
also been successfully applied in wireless communications, even though the current radio …

The recent development in machine learning, especially in deep neural networks (DNN),
has enabled learning-based end-to-end communication systems, where DNNs are …

Blindly decoding a signal requires estimating its unknown transmit parameters,
compensating for the wireless channel impairments, and identifying the modulation type …

In this article, we develop an end-to-end wireless communication system using deep neural
networks (DNNs), where DNNs are employed to perform several key functions, including …

We present and discuss several novel applications of deep learning for the physical layer.
By interpreting a communications system as an autoencoder, we develop a fundamental …

With the development of 5G, the future wireless communication network tends to be more
and more intelligent. In the face of new service demands of communication in the future such …

Machine learning (ML) has been widely applied to the upper layers of wireless
communication systems for various purposes, such as deployment of cognitive radio and …

Deep learning has been applied in physical-layer communications systems in recent years
and has demonstrated fascinating results that were comparable or even better than human …