Learning to Communicate: Channel Auto-encoders, Domain Specific Regularizers, and Attention
This addresses the problem of robust communication in noisy environments for applications like wireless systems, though it appears incremental with remaining challenges noted.
The paper tackles the problem of learning efficient and adaptive communication of binary information over impaired channels by using a channel autoencoder with domain-specific regularizers and attention mechanisms, demonstrating promising initial capacity results.
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.