Abstract

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 encoding, decoding, modulation, and demodulation. However, an accurate estimation of instantaneous channel transfer function, i.e., channel state information (CSI), is needed in order for the transmitter DNN to learn to optimize the receiver gain in decoding. This is very much a challenge since CSI varies with time and location in wireless communications and is hard to obtain when designing transceivers. We propose to use a conditional generative adversarial net (GAN) to represent channel effects and to bridge the transmitter DNN and the receiver DNN so that the gradient of the transmitter DNN can be back-propagated from the receiver DNN. In particular, a conditional GAN is employed to model the channel effects in a data-driven way, where the received signal corresponding to the pilot symbols is added as a part of the conditioning information of the GAN. To address the curse of dimensionality when the transmit symbol sequence is long, convolutional layers are utilized. From the simulation results, the proposed method is effective on additive white Gaussian noise (AWGN) channels, Rayleigh fading channels, and frequency-selective channels, which opens a new door for building data-driven DNNs for end-to-end communication systems.

Highlights

  • I N A traditional wireless communication system shown in Fig. 1(a), the data transmission entails multiple signal processing blocks in the transmitter and the receiver

  • By iteratively training the conditional generative adversarial net (GAN), the transmitter, and the receiver, the end-to-end loss can be optimized in a supervised way

  • We show that the conditional distribution of the channel can be modeled by a conditional GAN

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Summary

Introduction

I N A traditional wireless communication system shown in Fig. 1(a), the data transmission entails multiple signal processing blocks in the transmitter and the receiver. I N A traditional wireless communication system shown, the data transmission entails multiple signal processing blocks in the transmitter and the receiver. While the technologies in this system are quite mature, individual blocks therein are separately designed and optimized, often. Manuscript received March 4, 2019; revised July 8, 2019 and November 27, 2019; accepted January 20, 2020. Date of publication February 6, 2020; date of current version May 8, 2020. The associate editor coordinating the review of this article and approving it for publication was E.

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