The (inverse) discrete Fourier transform (DFT/IDFT) is often perceived as essential to
orthogonal frequency-division multiplexing (OFDM) systems. In this paper, a deep complex …

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 …

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 …

We investigate joint source channel coding (JSCC) for wireless image transmission over
multipath fading channels. Inspired by recent works on deep learning based JSCC and …

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 wireless communication, modulation classification is an important part of the non-
cooperative communication, and it is difficult to classify the various modulation schemes …

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 …

With the depletion of spectrum, wireless communication systems turn to exploit large
antenna arrays to achieve the degree of freedom in the space domain, such as millimeter …

We consider a trainable point-to-point communication system, where both transmitter and
receiver are implemented as neural networks (NNs), and demonstrate that training on the bit …

This paper proposes a novel convolutional neural network (CNN) based joint classification
method to characterize the signal-to-noise power ratio (SNR) and Doppler shift using …