End-to-End Learning of Communications Systems Without a Channel Model

Understand

The idea of end-to-end learning of communications systems through neural network -based autoencoders has the shortcoming that it requires a differentiable channel model.

• We present in this paper a novel learning algorithm which alleviates this problem.
• The algorithm iterates between supervised training of the receiver and reinforcement learning -based training of the transmitter.
• We demonstrate that this approach works as well as fully supervised methods on additive white Gaussian noise (AWGN) and Rayleigh block-fading (RBF) channels.