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LR-FHSS-Sim: A Discrete-Event Simulator for LR-FHSS Networks

Jean Michel de Souza Sant Ana, Arliones Hoeller, Hirley Alves, Richard Demo Souza

TL;DR

LR-FHSS-Sim addresses the need for flexible, reusable modeling of LR-FHSS IoT networks in non-terrestrial contexts. It presents a Python-based discrete-event simulator built on SimPy with a modular core and extension-friendly architecture, modeling parameters such as header duration $t_{header}=233.472$ ms, fragment duration $t_f=102.4$ ms, and the fragmentation count $f=ig\lceil\dfrac{b+3}{6~CR}\big\rceil$ with $CR\in\{1/3,2/3\}$ over a $488$ Hz physical-channel grid. The paper's contributions include an open-source implementation, a reusable LR-FHSS simulation framework, and two extensions (Traffic Modeling and ACRDA) to explore different strategies and interference-management techniques. Findings show that long-run throughput and average per-device success can be similar across traffic patterns, but variance and ACRDA performance depend on burstiness, illustrating the need for flexible simulators to study dynamic networks.

Abstract

This work presents the LR-FHSS-Sim, a free and open-source discrete-event simulator for LR-FHSS networks. We highlight the importance of network modeling for IoT coverage, especially when it is needed to capture dynamic network behaviors. Written in Python, we present the LR-FHSS-Sim main structure, procedures, and extensions. We discuss the importance of a modular code, which facilitates the creation of algorithmic strategies and signal-processing techniques for LR-FHSS networks. Moreover, we showcase how to achieve results when considering different packet generation traffic patterns and with a previously published extension. Finally, we discuss our thoughts on future implementations and what can be achieved with them.

LR-FHSS-Sim: A Discrete-Event Simulator for LR-FHSS Networks

TL;DR

LR-FHSS-Sim addresses the need for flexible, reusable modeling of LR-FHSS IoT networks in non-terrestrial contexts. It presents a Python-based discrete-event simulator built on SimPy with a modular core and extension-friendly architecture, modeling parameters such as header duration ms, fragment duration ms, and the fragmentation count with over a Hz physical-channel grid. The paper's contributions include an open-source implementation, a reusable LR-FHSS simulation framework, and two extensions (Traffic Modeling and ACRDA) to explore different strategies and interference-management techniques. Findings show that long-run throughput and average per-device success can be similar across traffic patterns, but variance and ACRDA performance depend on burstiness, illustrating the need for flexible simulators to study dynamic networks.

Abstract

This work presents the LR-FHSS-Sim, a free and open-source discrete-event simulator for LR-FHSS networks. We highlight the importance of network modeling for IoT coverage, especially when it is needed to capture dynamic network behaviors. Written in Python, we present the LR-FHSS-Sim main structure, procedures, and extensions. We discuss the importance of a modular code, which facilitates the creation of algorithmic strategies and signal-processing techniques for LR-FHSS networks. Moreover, we showcase how to achieve results when considering different packet generation traffic patterns and with a previously published extension. Finally, we discuss our thoughts on future implementations and what can be achieved with them.
Paper Structure (10 sections, 2 equations, 4 figures, 1 table)

This paper contains 10 sections, 2 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Contributions
  3. Organization
  4. LR-FHSS-Sim
  5. Architecture
  6. Extensions
  7. Traffic Modeling
  8. Asynchronous Contention Resolution Diversity Aloha (ACRDA)
  9. Results
  10. Conclusions

Figures (4)

  • Figure 1: Simplified UMLFowler:2003 diagram of the LR-FHSS-Sim package, showing the Core and Exensions components and their respective classes.
  • Figure 2: Network average success (red left) and throughput (blue right) for regular LR-FHSS network for different number of end devices $N$ with different traffic models.
  • Figure 3: Cumulative distribution function of the end device success probability for different traffic models and number of end devices with 5 hours of simulation time.
  • Figure 4: Network average success for regular LR-FHSS network and ACRDA-based LR-FHSS Santana:IoTJ:2024 for different number of end devices $N$ with different traffic models.