OFDM-Autoencoder for End-to-End Learning of Communications Systems

Understand

We extend the idea of end-to-end learning of communications systems through deep neural network (NN)-based autoencoders to orthogonal frequency division multiplexing (OFDM) with cyclic prefix (CP).

• Our implementation has the same benefits as a conventional OFDM system, namely singletap equalization and robustness against sampling synchronization errors, which turned out to be one of the major challenges in previous single-carrier implementations.
• This enables reliable communication over multipath channels and makes the communication scheme suitable for commodity hardware with imprecise oscillators.
• We show that the proposed scheme can be realized with state-of-the-art deep learning software libraries as transmitter and receiver solely consist of differentiable layers required for gradient-based training.