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A Calibrated and Automated Simulator for Innovations in 5G

Conrado Boeira, Antor Hasan, Khaleda Papry, Yue Ju, Zhongwen Zhu, Israat Haque

TL;DR

This work delivers a calibrated, end-to-end, open-source 5G simulator (Simu5G) aligned to 3GPP guidelines for urban and rural eMBB deployments, complemented by a YAML-based automation API that simplifies configuration. By generating realistic KPI data, the authors enable data-driven research, demonstrated through a machine learning case study for anomaly detection in 5G RAN using Simu5G data. The combination of calibration, automation, and practical ML use cases lowers barriers to experimentation, accelerates innovation, and supports reproducible evaluation. The approach holds practical impact for researchers and operators seeking realistic, data-rich simulators with minimal setup overhead.

Abstract

The rise of 5G deployments has created the environment for many emerging technologies to flourish. Self-driving vehicles, Augmented and Virtual Reality, and remote operations are examples of applications that leverage 5G networks' support for extremely low latency, high bandwidth, and increased throughput. However, the complex architecture of 5G hinders innovation due to the lack of accessibility to testbeds or realistic simulators with adequate 5G functionalities. Also, configuring and managing simulators are complex and time consuming. Finally, the lack of adequate representative data hinders the data-driven designs in 5G campaigns. Thus, we calibrated a system-level open-source simulator, Simu5G, following 3GPP guidelines to enable faster innovation in the 5G domain. Furthermore, we developed an API for automatic simulator configuration without knowing the underlying architectural details. Finally, we demonstrate the usage of the calibrated and automated simulator by developing an ML-based anomaly detection in a 5G Radio Access Network (RAN).

A Calibrated and Automated Simulator for Innovations in 5G

TL;DR

This work delivers a calibrated, end-to-end, open-source 5G simulator (Simu5G) aligned to 3GPP guidelines for urban and rural eMBB deployments, complemented by a YAML-based automation API that simplifies configuration. By generating realistic KPI data, the authors enable data-driven research, demonstrated through a machine learning case study for anomaly detection in 5G RAN using Simu5G data. The combination of calibration, automation, and practical ML use cases lowers barriers to experimentation, accelerates innovation, and supports reproducible evaluation. The approach holds practical impact for researchers and operators seeking realistic, data-rich simulators with minimal setup overhead.

Abstract

The rise of 5G deployments has created the environment for many emerging technologies to flourish. Self-driving vehicles, Augmented and Virtual Reality, and remote operations are examples of applications that leverage 5G networks' support for extremely low latency, high bandwidth, and increased throughput. However, the complex architecture of 5G hinders innovation due to the lack of accessibility to testbeds or realistic simulators with adequate 5G functionalities. Also, configuring and managing simulators are complex and time consuming. Finally, the lack of adequate representative data hinders the data-driven designs in 5G campaigns. Thus, we calibrated a system-level open-source simulator, Simu5G, following 3GPP guidelines to enable faster innovation in the 5G domain. Furthermore, we developed an API for automatic simulator configuration without knowing the underlying architectural details. Finally, we demonstrate the usage of the calibrated and automated simulator by developing an ML-based anomaly detection in a 5G Radio Access Network (RAN).
Paper Structure (17 sections, 3 equations, 6 figures, 5 tables)

This paper contains 17 sections, 3 equations, 6 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Related Work
  4. Calibration
  5. Methodology
  6. Implementation
  7. Evaluation Results
  8. Urban Deployment
  9. Rural Deployment
  10. Automating Configuration
  11. Automation Workflow
  12. Implementation and Results
  13. A case study: ML-based anomaly detection
  14. Dataset Description and Pre-processing
  15. Model Construction
  16. ...and 2 more sections

Figures (6)

  • Figure 1: An example cellular network deployment with hexagonal grid and tri-sector antennas.
  • Figure 2: Simu5G results compared to 3GPP submissions with max UE distance of 50m for the Urban eMBB.
  • Figure 3: Simu5G results compared to 3GPP submissions for the Rural eMBB.
  • Figure 4: Workflow of the proposed automation API.
  • Figure 5: An example of using Simu5G configuration automation to generate X2 connections.
  • ...and 1 more figures