  Attracted by its scalability towards practical codeword lengths, we revisit
the idea of Turbo-autoencoders for end-to-end learning of PHY-Layer
communications. For this, we study the existing concepts of Turbo-autoencoders
from the literature and compare the concept with state-of-the-art classical
coding schemes. We propose a new component-wise training algorithm based on the
idea of Gaussian a priori distributions that reduces the overall training time
by almost a magnitude. Further, we propose a new serial architecture inspired
by classical serially concatenated Turbo code structures and show that a
carefully optimized interface between the two component autoencoders is
required. To the best of our knowledge, these serial Turbo autoencoder
structures are the best known neural network based learned sequences that can
be trained from scratch without any required expert knowledge in the domain of
channel codes.
