  In this article, we develop an end-to-end wireless communication system using
deep neural networks (DNNs), in which 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.
