Learning to Communicate: Channel Auto-encoders, Domain Specific Regularizers, and Attention

Understand

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.