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

• URL: http://arxiv.org/abs/2602.07886v1
• Date: Sun, 08 Feb 2026 09:37:42 GMT
• Title: Rich-ARQ: From 1-bit Acknowledgment to Rich Neural Coded Feedback
• Authors: Enhao Chen, Yulin Shao,
• Abstract summary: We present Rich-ARQ, a paradigm that introduces neural-coded feedback for collaborative physical-layer channel coding between transmitter and receiver.<n>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.
• Score: 13.956883631679121
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Precoding-Oriented CSI Feedback Design with Mutual Information Regularized VQ-VAE [7.998767647416901]
  We propose a precoding-oriented CSI feedback framework based on a vector quantized variational autoencoder.<n> Numerical results demonstrate that the proposed method achieves rates comparable to variable-length neural compression schemes.
  arXiv  Detail & Related papers  (2026-01-22T18:47:49Z)
• Joint Source-Channel-Generation Coding: From Distortion-oriented Reconstruction to Semantic-consistent Generation [58.67925548779465]
  We propose Joint Source-Channel-Generation Coding (JSCGC), a novel paradigm that shifts the focus from perceptual reconstruction to probabilistic generation.<n>JSCGC improves substantially semantic quality and semantic fidelity, significantly outperforming conventional distortion-oriented J SCC methods.
  arXiv  Detail & Related papers  (2026-01-19T08:12:47Z)
• Rydberg Atomic Quantum Receivers for Classical Wireless Communications and Sensing: Their Models and Performance [78.76421728334013]
  Rydberg atomic quantum receivers (RAQRs) are an eminent solution for detecting the electric field of radio frequency (RF) signals.<n>We introduce the RAQR to the wireless community by presenting an end-to-end reception scheme.<n>We then develop a corresponding equivalent baseband signal model relying on a realistic reception flow.
  arXiv  Detail & Related papers  (2024-12-07T06:25:54Z)
• Reversible Decoupling Network for Single Image Reflection Removal [15.763420129991255]
  We propose a reversible encoder to secure valuable information while flexibly decoupling transmission- and reflection-relevant features.<n>RDNet achieves the best performance in the NTIRE 2025 Single Image Reflection Removal in the Wild Challenge in both fidelity and perceptual comparison.
  arXiv  Detail & Related papers  (2024-10-10T15:58:27Z)
• Semantic Communication for Cooperative Perception using HARQ [51.148203799109304]
  We leverage an importance map to distill critical semantic information, introducing a cooperative perception semantic communication framework. To counter the challenges posed by time-varying multipath fading, our approach incorporates the use of frequency-division multiplexing (OFDM) along with channel estimation and equalization strategies. We introduce a novel semantic error detection method that is integrated with our semantic communication framework in the spirit of hybrid automatic repeated request (HARQ)
  arXiv  Detail & Related papers  (2024-08-29T08:53:26Z)
• Coding for Gaussian Two-Way Channels: Linear and Learning-Based Approaches [27.356869653157677]
  We propose two different two-way coding strategies: linear coding and learning-based coding.<n>For learning-based coding, we introduce a novel recurrent neural network (RNN)-based coding architecture.<n>Our two-way coding methodologies outperform conventional channel coding schemes significantly in sum-error performance.
  arXiv  Detail & Related papers  (2023-12-31T12:40:18Z)
• Non-Markovian feedback for optimized quantum error correction [0.0]
  We train a recurrent neural network that provides a quantum error correction scheme based on memory.<n>This approach significantly outperforms current strategies and paves the way for more powerful measurement-based QEC protocols.
  arXiv  Detail & Related papers  (2023-12-12T16:05:54Z)
• Joint Channel Estimation and Feedback with Masked Token Transformers in Massive MIMO Systems [74.52117784544758]
  This paper proposes an encoder-decoder based network that unveils the intrinsic frequency-domain correlation within the CSI matrix. The entire encoder-decoder network is utilized for channel compression. Our method outperforms state-of-the-art channel estimation and feedback techniques in joint tasks.
  arXiv  Detail & Related papers  (2023-06-08T06:15:17Z)
• Feedback is Good, Active Feedback is Better: Block Attention Active Feedback Codes [13.766611137136168]
  We show that GBAF codes can also be used for channels with active feedback. We implement a pair of transformer architectures, at the transmitter and the receiver, which interact with each other sequentially. We achieve a new state-of-the-art BLER performance, especially in the low SNR regime.
  arXiv  Detail & Related papers  (2022-11-03T11:44:06Z)
• Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access [65.21117658030235]
  This work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS) and average training overhead. Results reveal that the MBDL outperforms by a significant margin the SIC receiver with imperfect CSIR.
  arXiv  Detail & Related papers  (2022-05-02T12:23:55Z)
• DRF Codes: Deep SNR-Robust Feedback Codes [2.6074034431152344]
  We present a new deep-neural-network (DNN) based error correction code for fading channels with output feedback, called deep SNR-robust feedback (DRF) code. We show that the DRF codes significantly outperform state-of-the-art in terms of both the SNR-robustness and the error rate in additive white Gaussian noise (AWGN) channel with feedback.
  arXiv  Detail & Related papers  (2021-12-22T10:47:25Z)
• FedRec: Federated Learning of Universal Receivers over Fading Channels [92.15358738530037]
  We propose a neural network-based symbol detection technique for downlink fading channels. Multiple users collaborate to jointly learn a universal data-driven detector, hence the name FedRec. The performance of the resulting receiver is shown to approach the MAP performance in diverse channel conditions without requiring knowledge of the fading statistics.
  arXiv  Detail & Related papers  (2020-11-14T11:29:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.