Variable-Length Feedback Codes via Deep Learning
Abstract
Variable-length feedback coding has the potential to significantly enhance communication reliability in finite block length scenarios by adapting coding strategies based on real-time receiver feedback. Designing such codes, however, is challenging. While deep learning (DL) has been employed to design sophisticated feedback codes, existing DL-aided feedback codes are predominantly fixed-length and suffer performance degradation in the high code rate regime, limiting their adaptability and efficiency. This paper introduces deep variable-length feedback (DeepVLF) code, a novel DL-aided variable-length feedback coding scheme. By segmenting messages into multiple bit groups and employing a threshold-based decoding mechanism for independent decoding of each bit group across successive communication rounds, DeepVLF outperforms existing DL-based feedback codes and establishes a new benchmark in feedback channel coding.
Cite
@article{arxiv.2411.08481,
title = {Variable-Length Feedback Codes via Deep Learning},
author = {Wenwei Lai and Yulin Shao and Yu Ding and Deniz Gunduz},
journal= {arXiv preprint arXiv:2411.08481},
year = {2024}
}