  Deep neural networks (DNNs) allow digital receivers to learn to operate in
complex environments. To do so, DNNs should preferably be trained using large
labeled data sets with a similar statistical relationship as the one under
which they are to infer. For DNN-aided receivers, obtaining labeled data
conventionally involves pilot signalling at the cost of reduced spectral
efficiency, typically resulting in access to limited data sets. In this paper,
we study how one can enrich a small set of labeled pilots data into a larger
data set for training deep receivers. Motivated by the widespread use of data
augmentation techniques for enriching visual and text data, we propose
dedicated augmentation schemes that exploits the characteristics of digital
communication data. We identify the key considerations in data augmentations
for deep receivers as the need for domain orientation, class (constellation)
diversity, and low complexity. Following these guidelines, we devise three
complementing augmentations that exploit the geometric properties of digital
constellations. Our combined augmentation approach builds on the merits of
these different augmentations to synthesize reliable data from a momentary
channel distribution, to be used for training deep receivers. Furthermore, we
exploit previous channel realizations to increase the reliability of the
augmented samples.
