6 papers
Nonlinear Posterior-Mean Feedback Codes for AWGN Channels
Yingyao Zhou, Natasha Devroye, Milos Zefran +1
Feedback can improve the reliability of communication over additive white Gaussian noise (AWGN) channels. Classical feedback codes are interpretable, or easy to understand, but oft…
Deep Broadcast Feedback Codes
Jacqueline Malayter, Yingyao Zhou, Natasha Devroye +3
Recent advances in deep learning for wireless communications have renewed interest in channel output feedback codes. In the additive white Gaussian broadcast channel with feedback…
Feedback Lunch: Learned Feedback Codes for Secure Communications
Yingyao Zhou, Natasha Devroye, Onur Günlü
We consider reversely-degraded secure-communication channels, for which the secrecy capacity is zero if there is no channel feedback. Specifically, we focus on a seeded modular cod…
Learned codes for broadcast channels with feedback
Yingyao Zhou, Natasha Devroye
We focus on designing error-correcting codes for the symmetric Gaussian broadcast channel with feedback. Feedback not only expands the capacity region of the broadcast channel but…
Higher-order Interpretations of Deepcode, a Learned Feedback Code
Yingyao Zhou, Natasha Devroye, Gyorgy Turan +1
We present an interpretation of Deepcode, a learned feedback code that showcases higher-order error correction relative to an earlier interpretable model. By interpretation, we mea…
Interpreting Deepcode, a learned feedback code
Yingyao Zhou, Natasha Devroye, Gyorgy Turan +1
Deep learning methods have recently been used to construct non-linear codes for the additive white Gaussian noise (AWGN) channel with feedback. However, there is limited understand…