Deep Extended Feedback Codes

TL;DR

This paper introduces Deep Extended Feedback (DEF) codes, a novel deep neural network-based error correction scheme for channels with feedback, enhancing error correction and spectral efficiency over previous models.

Contribution

The paper proposes DEF codes, extending Deepcode by using longer observation intervals and high-order modulation, achieving improved error correction and spectral efficiency.

Findings

DEF codes outperform other DNN-based feedback codes in error correction.

Longer observation intervals improve error correction capabilities.

High-order modulation increases spectral efficiency.

Abstract

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.