Deep Learning-Based End-to-End Wireless Communication Systems With Conditional GANs as Unknown Channels

By Hao Ye, Le Liang, Geoffrey Y. Li, Biing-Hwang Fred Juang. IEEE Transactions on Wireless Communications, 2019

353 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a1065c59096bfd49e6a352a9ba6487d2e4e00fc4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Zhijin Qin
2. Huiqiang Xie
3. Geoffrey Y. Li
4. Jianhua Lu
5. Xiaoming Tao
6. Zhenzi Weng
7. Khaled Ben Letaief

In Signal Processing

1. Huiqiang Xie
2. Zhijin Qin

Citations in the corpus

• Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges (2022, neutral): “In [134], a conditional GAN is used to model the channel conditions.”
• A Lite Distributed Semantic Communication System for Internet of Things (2020, background): “There are several works for mitigating this problem [15]–[17].”