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

Channel Agnostic End-to-End Learning based Communication Systems with Conditional GAN