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

## TL;DR

This paper introduces a new QoS-aware scheduling method for multi-flow 5G devices in smart factories, improving resource allocation and fairness while supporting detailed simulation of QoS flows.

## Contribution

It extends Simu5G to model per-QFI behavior and proposes a novel QoS-aware Proportional Fairness scheduler for industrial 5G networks.

## Key findings

- Enhanced deadline adherence and fairness in simulations
- Improved resource management for heterogeneous flows
- Open-source implementation supporting future research

## 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.