Deep Extended Feedback Codes
This work addresses error correction in communication systems with feedback, offering incremental improvements over prior DNN-based methods like Deepcode.
The paper tackles error correction for channels with feedback by proposing Deep Extended Feedback (DEF) codes, a DNN-based encoder architecture that improves performance over existing DNN-based codes, as shown in evaluations.
A new deep-neural-network (DNN) based error correction encoder architecture for channels with feedback, called Deep Extended Feedback (DEF), is presented in this paper. The encoder in the DEF architecture transmits an information message followed by a sequence of parity symbols which are generated based on the message as well as the observations of the past forward channel outputs sent to the transmitter through a feedback channel. DEF codes generalize Deepcode [1] in several ways: parity symbols are generated based on forward-channel output observations over longer time intervals in order to provide better error correction capability; and high-order modulation formats are deployed in the encoder so as to achieve increased spectral efficiency. Performance evaluations show that DEF codes have better performance compared to other DNN-based codes for channels with feedback.