  We address the problem of learning efficient and adaptive ways to communicate
binary information over an impaired channel. We treat the problem as
reconstruction optimization through impairment layers in a channel autoencoder
and introduce several new domain-specific regularizing layers to emulate common
channel impairments. We also apply a radio transformer network based attention
model on the input of the decoder to help recover canonical signal
representations. We demonstrate some promising initial capacity results from
this architecture and address several remaining challenges before such a system
could become practical.
