We introduce a novel physical layer scheme for Multiple Input Multiple Output (MIMO)
communications based on unsupervised deep learning using an autoencoder. This method …

We introduce a novel physical layer scheme for single user Multiple-Input Multiple-Output
(MIMO) communications based on unsupervised deep learning using an autoencoder. This …

End-to-end data-driven machine learning (ML) of multiple-input multiple-output (MIMO)
systems has been shown to have the potential of exceeding the performance of engineered …

This paper presents a novel method for synthesizing new physical layer modulation and
coding schemes for communications systems using a learning-based approach which does …

This paper presents a novel physical layer scheme for multiple-input multiple-output (MIMO)
communication systems based on unsupervised deep learning (DL) using an autoencoder …

End-to-end autoencoder (AE) learning has the potential of exceeding the performance of
human-engineered transceivers and encoding schemes, without a priori knowledge of …

We address the problem of learning an efficient and adaptive physical layer encoding to
communicate binary information over an impaired channel. In contrast to traditional work, we …

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 (DL) provides a framework for designing new communication systems that
embrace practical impairments. In this paper, we present an exploration of DL as applied to …

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 …