QoS-Aware Proportional Fairness Scheduling for Multi-Flow 5G UEs: A Smart Factory Perspective

2025-08-29

AI Summary

Existing simulation frameworks lack QoS Flow Identifier (QFI)-level multi-flow modeling capabilities, hindering support for intelligent factory scenarios where a single device must concurrently serve multiple heterogeneous traffic flows with distinct QoS requirements. To address this, we extend Simu5G to enable fine-grained, QFI-granular traffic modeling—the first such implementation. We further propose a QoS-aware proportional fair scheduling algorithm that jointly optimizes latency constraints, guaranteed bit rates, and flow priorities. Integrated with an edge computing architecture, the framework supports coexistence of diverse industrial services—including machine vision, real-time control, and bulk data transfer. Evaluation on realistic industrial scenarios demonstrates a 32.7% improvement in critical task deadline satisfaction, a 0.18 increase in scheduling fairness (Jain’s index), and sustained throughput above 92% of baseline performance. All modules are open-sourced, providing a reproducible, extensible methodology and platform foundation for industrial 5G QoS modeling and scheduling research.

Technology Category

• Planning, Routing, and Scheduling: Scheduling
• Search and Optimization: Sampling/Simulation-based Search
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Private 5G networks are emerging as key enablers for smart factories, where a single device often handles multiple concurrent traffic flows with distinct Quality of Service (QoS) requirements. Existing simulation frameworks, however, lack the fidelity to model such multi-flow behavior at the QoS Flow Identifier (QFI) level. This paper addresses this gap by extending Simu5G to support per-QFI modeling and by introducing a novel QoS-aware Proportional Fairness (QoS-PF) scheduler. The scheduler dynamically balances delay, Guaranteed Bit Rate (GBR), and priority metrics to optimize resource allocation across heterogeneous flows. We evaluate the proposed approach in a realistic smart factory scenario featuring edge-hosted machine vision, real-time control loops, and bulk data transfer. Results show that QoS-PF improves deadline adherence and fairness without compromising throughput. All extensions are implemented in a modular and open-source manner to support future research. Our work provides both a methodological and architectural foundation for simulating and analyzing advanced QoS policies in industrial 5G deployments.

Problem

• Modeling multi-flow QoS at QFI level in 5G networks
• Balancing delay, GBR, and priority for resource allocation
• Improving deadline adherence and fairness in smart factories

Innovation

• Extended Simu5G for per-QFI modeling
• Introduced QoS-aware Proportional Fairness scheduler
• Dynamically balances delay, GBR, and priority metrics