Transmitter

Backpropagation

Software-defined radio

Communications system

Training (meteorology)

Telecommunications

Computer science

Computer network

Physics

Mathematics

Channel state information

Computer engineering

Differentiable function

Mathematical analysis

Artificial intelligence

End-to-end principle

Channel (broadcasting)

Meteorology

Wireless

Artificial neural network

IEEE Journal on Selected Areas in Communications

The idea of end-to-end learning of communication systems through neural network (NN)-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 enables training of communication systems with an unknown channel model or with non-differentiable components. It iterates between training of the receiver using the true gradient, and training of the transmitter using an approximation of the gradient. We show that this approach works as well as model-based training for a variety of channels and tasks. Moreover, we demonstrate the algorithm's practical viability through hardware implementation on software defined radios (SDRs) where it achieves state-of-the-art performance over a coaxial cable and wireless channel.

Model-Free Training of End-to-End Communication Systems