学习沟通：通道自动编码器，特定领域的正则化器和注意力
Learning to Communicate: Channel Auto-encoders, Domain Specific Regularizers, and Attention
摘要：
我们解决的问题，学习有效的和自适应的方式来沟通二进制信息受损的通道。我们把这个问题看作是通过通道自动编码器中的损伤层进行重建优化，并引入了几个新的特定于域的正则化层来模拟常见的通道损伤。我们还应用了一个无线电Transformer网络的注意力模型的输入解码器，以帮助恢复规范的信号表示。我们展示了一些有前途的初始容量的结果，从这个架构，并解决几个剩余的挑战，这样的系统可以成为现实。
英文摘要：
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.
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