Component Training of Turbo Autoencoders
Jannis Clausius Marvin Geiselhart Stephan ten Brink
Isolated training with Gaussian priors (TGP) of the component autoencoders of
turbo-autoencoder architectures enables faster, more consistent training and
better generalization to arbitrary decoding iterations than training based on
deep unfolding. We propose fitting the components via extrinsic information
transfer (EXIT) charts to a desired behavior which enables scaling to larger
message lengths ($k \approx 1000$) while retaining competitive performance. To
the best of our knowledge, this is the first autoencoder that performs close to
classical codes in this regime. Although the binary cross-entropy (BCE) loss
function optimizes the bit error rate (BER) of the components, the design via
EXIT charts enables to focus on the block error rate (BLER). In serially
concatenated systems the component-wise TGP approach is well known for inner
components with a fixed outer binary interface, e.g., a learned inner code or
equalizer, with an outer binary error correcting code. In this paper we extend
the component training to structures with an inner and outer autoencoder, where
we propose a new 1-bit quantization strategy for the encoder outputs based on
the underlying communication problem. Finally, we discuss the model complexity
of the learned components during design time (training) and inference and show
that the number of weights in the encoder can be reduced by 99.96 %.