  In this article, we use deep neural networks (DNNs) to develop a wireless
end-to-end communication system, in which DNNs are employed for all
signal-related functionalities, such as encoding, decoding, modulation, and
equalization. However, accurate instantaneous channel transfer function,
\emph{i.e.}, the channel state information (CSI), is necessary to compute the
gradient of the DNN representing. In many communication systems, the channel
transfer function is hard to obtain in advance and varies with time and
location. In this article, this constraint is released by developing a channel
agnostic end-to-end system that does not rely on any prior information about
the channel. We use a conditional generative adversarial net (GAN) to represent
the channel effects, where the encoded signal of the transmitter will serve as
the conditioning information. In addition, in order to deal with the
time-varying channel, the received signal corresponding to the pilot data can
also be added as a part of the conditioning information. From the simulation
results, the proposed method is effective on additive white Gaussian noise
(AWGN) and Rayleigh fading channels, which opens a new door for building
data-driven communication systems.
