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Optimized Asymmetric Feedback Detection for Rate-adaptive HARQ with Unreliable Feedback

Weihang Ding, Mohammad Shikh-Bahaei

TL;DR

This paper addresses downlink IR-HARQ with unreliable feedback by introducing an asymmetric feedback detection scheme that prioritizes protecting NACK errors. It models rate-adaptive HARQ as a Markov Decision Process and solves it with dynamic programming, while employing a Gaussian MI-accumulation approximation to enable tractable rate optimization; an iterative algorithm jointly tunes transmission rates and asymmetric detection thresholds under a specified outage constraint. The proposed approach yields higher throughput and lower outage compared to symmetric-detection and duplicated-ACK methods, demonstrated through simulations on downlink Rayleigh fading and 5G NR PUCCH channels. The results highlight a practical, low-feedback-cost path to improving HARQ performance in next-generation wireless systems where feedback reliability is limited.

Abstract

This work considers downlink incremental redundancy Hybrid Automatic Repeat Request (IR-HARQ) over unreliable feedback channels. Since the impact of positive feedback (i.e., ACK) error is smaller than that of negative feedback (i.e., NACK) error, an asymmetric feedback detection scheme is proposed to protect NACK and further reduce the outage probability. We formulate the HARQ process as a Markov Decision Process (MDP) model to adapt to the transmission rate of each transmission attempt without enriched feedback and additional feedback cost. We aim to optimize the performance of HARQ process under certain outage probability requirements by finding optimal asymmetric detection thresholds. Numerical results obtained on the downlink Rayleigh fading channel and 5G new radio (NR) PUCCH feedback channel show that by applying asymmetric feedback detection and adaptive rate allocation, higher throughput can be achieved under outage probability limitations.

Optimized Asymmetric Feedback Detection for Rate-adaptive HARQ with Unreliable Feedback

TL;DR

This paper addresses downlink IR-HARQ with unreliable feedback by introducing an asymmetric feedback detection scheme that prioritizes protecting NACK errors. It models rate-adaptive HARQ as a Markov Decision Process and solves it with dynamic programming, while employing a Gaussian MI-accumulation approximation to enable tractable rate optimization; an iterative algorithm jointly tunes transmission rates and asymmetric detection thresholds under a specified outage constraint. The proposed approach yields higher throughput and lower outage compared to symmetric-detection and duplicated-ACK methods, demonstrated through simulations on downlink Rayleigh fading and 5G NR PUCCH channels. The results highlight a practical, low-feedback-cost path to improving HARQ performance in next-generation wireless systems where feedback reliability is limited.

Abstract

This work considers downlink incremental redundancy Hybrid Automatic Repeat Request (IR-HARQ) over unreliable feedback channels. Since the impact of positive feedback (i.e., ACK) error is smaller than that of negative feedback (i.e., NACK) error, an asymmetric feedback detection scheme is proposed to protect NACK and further reduce the outage probability. We formulate the HARQ process as a Markov Decision Process (MDP) model to adapt to the transmission rate of each transmission attempt without enriched feedback and additional feedback cost. We aim to optimize the performance of HARQ process under certain outage probability requirements by finding optimal asymmetric detection thresholds. Numerical results obtained on the downlink Rayleigh fading channel and 5G new radio (NR) PUCCH feedback channel show that by applying asymmetric feedback detection and adaptive rate allocation, higher throughput can be achieved under outage probability limitations.
Paper Structure (12 sections, 27 equations, 5 figures, 1 algorithm)

This paper contains 12 sections, 27 equations, 5 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System model
  3. Downlink channel
  4. Uplink channel
  5. HARQ performance
  6. HARQ with unreliable feedback
  7. Proposed algorithm
  8. Optimize transmission rates
  9. Optimize asymmetric detection thresholds
  10. Iterative algorithm
  11. Numerical results
  12. Conclusion

Figures (5)

  • Figure 1: A general model of unreliable HARQ feedback channel
  • Figure 2: Two HARQ feedback detection schemes.
  • Figure 3: Minimum achievable HARQ outage probability when SNR$_d$=3dB
  • Figure 4: The performance of HARQ process with asymmetric feedback detection compared with duplicated-ACK HARQ under the same outage probability constraints.
  • Figure 5: The comparison of HARQ with fixed detection thresholds ($\alpha$) and variable thresholds when asymmetric feedback detection is applied.