Deep Learning Architectures for Modeling Communication Systems

Recently, deep learning for physical layer has been modeled using autoencoders to model the entire communication system end-to-end. We extend these methods to improve the overall performance by adopting various learning strategies when multiple users try to communicate over a shared channel. We consider cooperative and non-cooperative schemes in the 2-user Gaussian interference channel. Additionally, a simple neural network architecture is provided for wireless communication systems where channel gain matrix either attenuates or, in some cases, results in fading of the message signal sent by the transmitter.