Encoder-Decoder Networks for Self-Supervised Pretraining and Downstream
  Signal Bandwidth Regression on Digital Antenna Arrays
Rajib Bhattacharjea Nathan West
This work presents the first applications of self-supervised learning applied
to data from digital antenna arrays. Encoder-decoder networks are pretrained on
digital array data to perform a self-supervised noisy-reconstruction task
called channel in-painting, in which the network infers the contents of array
data that has been masked with zeros. The self-supervised step requires no
human-labeled data. The encoder architecture and weights from pretraining are
then transferred to a new network with a task-specific decoder, and the new
network is trained on a small volume of labeled data. We show that pretraining
on the unlabeled data allows the new network to perform the task of bandwidth
regression on the digital array data better than an equivalent network that is
trained on the same labeled data from random initialization.