Reliability Modeling for Beyond-5G Mission Critical Networks Using Effective Capacity

2025-01-31

AI Summary

For task-critical B5G networks, end-to-end reliability modeling in URLLC/HRLLC scenarios remains challenging, and QoS guarantees lack rigorous theoretical foundations. Method: We propose a joint delay-reliability model integrating RLC-layer acknowledgment modes, segregated retransmission buffers, and dynamic physical-channel characteristics. We extend the effective capacity (EC) framework—originally designed for single-attempt transmission—to multi-attempt retransmissions and segmented-buffer RLC architectures, explicitly characterizing the fundamental EC–reliability trade-off and deriving the optimal operating point under stringent statistical QoS constraints. Leveraging stochastic network calculus and hierarchical queue modeling, the model captures cross-layer interactions while preserving analytical tractability. Contribution/Results: In typical deployments, the model achieves 99.999% reliability with less than 3% EC loss, providing operators with a theoretically grounded, deployment-ready configuration guideline for ultra-reliable low-latency communication systems.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Accurate reliability modeling for ultra-reliable low latency communication (URLLC) and hyper-reliable low latency communication (HRLLC) networks is challenging due to the complex interactions between network layers required to meet stringent requirements. In this paper, we propose such a model. We consider the acknowledged mode of the radio link control (RLC) layer, utilizing separate buffers for transmissions and retransmissions, along with the behavior of physical channels. Our approach leverages the effective capacity (EC) framework, which quantifies the maximum constant arrival rate a time-varying wireless channel can support while meeting statistical quality of service (QoS) constraints. We derive a reliability model that incorporates delay violations, various latency components, and multiple transmission attempts. Our method identifies optimal operating conditions that satisfy URLLC/HRLLC constraints while maintaining near-optimal EC, ensuring the system can handle peak traffic with a guaranteed QoS. Our model reveals critical trade-offs between EC and reliability across various use cases, providing guidance for URLLC/HRLLC network design for service providers and system designers.

Problem

• Ultra-Reliable Low Latency Communication
• Wireless Link Control
• Beyond 5G Networks

Innovation

• Effective Capacity
• Ultra Reliable Low Latency Communication
• Wireless Link Control