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.

翻译：本文重新构想了无线通信中的基础反馈机制，将当前主流的1比特二进制ACK/NACK转变为高维、信息丰富的向量，从而将被动确认转化为主动协作。我们提出了Rich-ARQ这一范式，它引入了神经编码反馈，用于发射机与接收机之间的协作式物理层信道编码。为了在实践中实现这一愿景，我们开发了一种新颖的异步反馈编码方案，它消除了反馈延迟导致的停滞，能动态适应信道波动，并具有适合设备端部署的轻量级编码器。我们将这一概念具体化为首个全栈、符合标准的软件定义无线电原型，该原型将人工智能推理与严格的无线电时序解耦。全面的空中实验表明，与传统的1比特混合ARQ相比，Rich-ARQ实现了显著的信噪比增益；与先前基于学习的反馈编码方案相比，它显著降低了延迟，从而将智能反馈的承诺从理论推向了下一代网络实用、高性能的现实。