  Recently, deep learning is considered to optimize the end-to-end performance
of digital communication systems. The promise of learning a digital
communication scheme from data is attractive, since this makes the scheme
adaptable and precisely tunable to many scenarios and channel models. In this
paper, we analyse a widely used neural network architecture and show that the
training of the end-to-end architecture suffers from normalization errors
introduced by an average power constraint. To solve this issue, we propose a
modified architecture: shifting the batch slicing after the normalization
layer. This approach meets the normalization constraints better, especially in
the case of small batch sizes. Finally, we experimentally demonstrate that our
modified architecture leads to significantly improved performance of trained
models, even for large batch sizes where normalization constraints are more
easily met.
