Enhanced Feedback Mechanisms for Resource-Efficient Incremental Redundancy

2026-07-15

AI Summary

This work addresses the inefficiencies of conventional incremental redundancy schemes, which suffer from coarse-grained feedback leading to excessive retransmission overhead, decoding redundancy, and increased end-to-end latency. To overcome these limitations, the authors propose an enhanced feedback and scheduling mechanism that accurately predicts the minimum redundancy required for successful decoding by leveraging either channel statistics or codeword-level reliability metrics from the initial transmission, enabling dynamic resource allocation. The core innovations include a channel-quality-based redundancy budget mapping and an early feedback mechanism driven by real-time reliability estimates. Furthermore, the study derives a theoretical lower bound on achievable reliability for HARQ systems. Evaluated with polar codes and 5G NR LDPC codes across a wide SNR range, the approach reduces retransmission overhead by up to 60%, achieves 96% feedback prediction accuracy, and significantly improves decoding success within two transmission attempts.

Abstract

Incremental redundancy (IR) can reduce error rates by spreading coded bits across multiple transmission attempts. However, conventional stop-and-wait operation with coarse feedback often over-provisions retransmissions, triggers unnecessary decoding attempts, and increases end-to-end latency. This paper develops enhanced feedback and scheduling mechanisms that predict the additional redundancy needed for successful decoding and allocate only the required resources. We study two complementary strategies. First, using channel statistics, we learn a one- or two-shot mapping from channel quality to the minimum redundancy budget. As a byproduct, we derive an achievable reliability lower bound on the error probability of hybrid automatic repeat request (HARQ) systems. Numerical results with polar-coded IR-HARQ scheme show that the bound can be closely approached by appropriately selecting the second-transmission redundancy over a wide SNR range with savings up to 60\% in retransmission size. Second, we propose a realization-aware early-feedback mechanism that uses first-transmission reliability information to make per-codeword decisions before decoding: whether the codeword is already decodable, if not, how many additional redundancy versions are needed, or whether decoding is unlikely and rate adaptation is preferable. Link-level simulations with 5G NR LDPC codes show that both predictors achieve high accuracy (about 96\% in our study), increasing the probability of successful decoding within at most two transmission occasions.

Problem

• incremental redundancy
• feedback mechanism
• resource efficiency
• HARQ
• retransmission overhead

Innovation

• incremental redundancy
• enhanced feedback
• resource efficiency
• HARQ
• early decoding decision