Delay Analysis of 5G HARQ in the Presence of Decoding and Feedback Latencies

2025-02-12

AI Summary

Existing analyses of delay violation probability (DVP) for ARQ/HARQ retransmission in 5G URLLC neglect critical practical factors—including decoding complexity, feedback latency, and parallel HARQ processes—leading to inaccurate DVP estimation. Method: We propose the first multi-server queuing model integrating decoding delay, feedback delay, and multiple parallel HARQ processes. Our framework jointly models finite-blocklength channel coding, 3GPP-compliant system parameters, and parallel transmission mechanisms, and derives a closed-form DVP expression along with an efficient numerical algorithm. Contribution/Results: Experiments demonstrate that our method achieves significantly higher DVP estimation accuracy than state-of-the-art approaches. It further reveals the coupled impact of resource allocation and protocol parameters on both DVP and throughput, providing theoretical foundations and design guidelines for deterministic latency guarantees in URLLC.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Security and Privacy: Large-scale security measurements

Abstract

The growing demand for stringent quality of service (QoS) guarantees in 5G networks requires accurate characterisation of delay performance, often measured using Delay Violation Probability (DVP) for a given target delay. Widely used retransmission schemes like Automatic Repeat reQuest (ARQ) and Hybrid ARQ (HARQ) improve QoS through effective feedback, incremental redundancy (IR), and parallel retransmission processes. However, existing works to quantify the DVP under these retransmission schemes overlook practical aspects such as decoding complexity, feedback delays, and the resulting need for multiple parallel ARQ/HARQ processes that enable packet transmissions without waiting for previous feedback, thus exploiting valuable transmission opportunities. This work proposes a comprehensive multi-server delay model for ARQ/HARQ that incorporates these aspects. Using a finite blocklength error model, we derive closed-form expressions and algorithms for accurate DVP evaluation under realistic 5G configurations aligned with 3GPP standards. Our numerical evaluations demonstrate notable improvements in DVP accuracy over the state-of-the-art, highlight the impact of parameter tuning and resource allocation, and reveal how DVP affects system throughput.

Problem

• Analyzes 5G HARQ delay performance
• Incorporates decoding and feedback latencies
• Improves Delay Violation Probability accuracy

Innovation

• Multi-server delay model
• Finite blocklength error model
• Closed-form DVP algorithms