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Performance Analysis of 5G RAN Slicing Deployment Options in Industry 4.0 Factories

Oscar Adamuz-Hinojosa, Abdelhilah Abdeselam, Pablo Muñoz, Pablo Ameigeiras, Juan M. Lopez-Soler

Abstract

This paper studies Radio Access Network (RAN) slicing strategies for 5G Industry~4.0 networks with ultra-reliable low-latency communication (uRLLC) requirements. We comparatively analyze four RAN slicing deployment options that differ in slice sharing and per-line or per-flow isolation. Unlike prior works that focus on management architectures or resource allocation under a fixed slicing structure, this work addresses the design of RAN slicing deployment options in the presence of multiple production lines and heterogeneous industrial flows. An SNC-based analytical framework and a heuristic slice planner are used to evaluate these options in terms of per-flow delay guarantees and radio resource utilization. Results show that under resource scarcity only per-flow slicing prevents delay violations by tightly matching resources to per-flow delay targets, while slice-sharing and hybrid deployments improve aggregation efficiency at the cost of weaker protection for the most delay-critical flows. Execution-time results confirm that the planner operates at Non-RT time scales, enabling its integration within O-RAN Non-RT RIC loops.

Performance Analysis of 5G RAN Slicing Deployment Options in Industry 4.0 Factories

Abstract

This paper studies Radio Access Network (RAN) slicing strategies for 5G Industry~4.0 networks with ultra-reliable low-latency communication (uRLLC) requirements. We comparatively analyze four RAN slicing deployment options that differ in slice sharing and per-line or per-flow isolation. Unlike prior works that focus on management architectures or resource allocation under a fixed slicing structure, this work addresses the design of RAN slicing deployment options in the presence of multiple production lines and heterogeneous industrial flows. An SNC-based analytical framework and a heuristic slice planner are used to evaluate these options in terms of per-flow delay guarantees and radio resource utilization. Results show that under resource scarcity only per-flow slicing prevents delay violations by tightly matching resources to per-flow delay targets, while slice-sharing and hybrid deployments improve aggregation efficiency at the cost of weaker protection for the most delay-critical flows. Execution-time results confirm that the planner operates at Non-RT time scales, enabling its integration within O-RAN Non-RT RIC loops.

Paper Structure

This paper contains 17 sections, 5 equations, 3 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Network Slicing Deployment Options
  3. System Model
  4. Traffic Model
  5. Radio Resource Model
  6. Channel Model
  7. Network Slice Planner
  8. Packet Delay Modeling via Stochastic Network Calculus
  9. Problem Formulation
  10. Heuristic Solution
  11. Window-Based Operation of the SNC-Based Slice Planner
  12. Performance Results
  13. Experimental Setup
  14. Comparison of Network Slicing Strategies
  15. Execution Time Analysis
  16. ...and 2 more sections

Figures (3)

  • Figure 1: Network slicing deployment options in a 5G-based industrial network. We assume production lines in a factory connect wirelessly to the system, which then interfaces with the enterprise edge cloud through a transport network 5GACIA-whitepaperI. A Protocol Data Unit (PDU) session represents the logical connection established between an UE and the 5G core, Data Radio Bearers (DRBs) are radio-layer channels that transport the user traffic, and QoS Flow Identifiers (QFIs) mark individual QoS flows within a PDU session, ensuring differentiated treatment according to latency and reliability requirements.
  • Figure 2: Slice-level RB utilization and delay--utilization trade-off across deployment options. We report, for each deployment option, the mean 95th percentile of slice utilization and the average normalized delay bound across flows.
  • Figure 3: Execution time of the network slice planner as a function of the number of flows $|\mathcal{F}|$ and production lines.