Rich-ARQ: From 1-bit Acknowledgment to Rich Neural Coded Feedback

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Authors: Enhao Chen, Yulin Shao
Cited by: 0

Abstract

This paper reimagines the foundational feedback mechanism in wireless communication, transforming the prevailing 1-bit binary ACK/NACK with a high-dimensional, information-rich vector to transform passive acknowledgment into an active collaboration. We present Rich-ARQ, a paradigm that introduces neural-coded feedback for collaborative physical-layer channel coding between transmitter and receiver. To realize this vision in practice, we develop a novel asynchronous feedback code that eliminates stalling from feedback delays, adapts dynamically to channel fluctuations, and features a lightweight encoder suitable for on-device deployment. We materialize this concept into the first full-stack, standard-compliant software-defined radio prototype, which decouples AI inference from strict radio timing. Comprehensive over-the-air experiments demonstrate that Rich-ARQ achieves significant SNR gains over conventional 1-bit hybrid ARQ and remarkable latency reduction over prior learning-based feedback codes, moving the promise of intelligent feedback from theory to a practical, high-performance reality for next-generation networks.

Fields of Study & Subjects

Error Correcting Code Techniques Advanced Wireless Communication Techniques Advanced Wireless Network Optimization

Publication details

Journal / source
arXiv (Cornell University)
Publication date
8 February 2026
Language
EN
Document type
Preprint