FAST-LoRa: An Efficient Simulation Framework for Evaluating LoRaWAN Networks and Transmission Parameter Strategies
Abstract
The Internet of Things (IoT) has transformed many industries, and LoRaWAN (Long Range Wide Area Network), built on LoRa (Long Range) technology, has become a crucial solution for enabling scalable, low-cost, and energy-efficient communication in wide-area networks. Simulation tools are essential for optimizing the transmission parameters and, therefore, the energy efficiency and performance of LoRaWAN networks. While existing simulation frameworks accurately replicate real-world scenarios by including multiple layers of communication protocols, they often imply significant computational overhead and simulation times. To address this issue, this paper introduces FAST-LoRa, a novel simulation framework designed to enable fast and efficient evaluation of LoRaWAN networks and selection of transmission parameters. FAST-LoRa streamlines computation by relying on analytical models without complex packet-level simulations and implementing gateway reception using efficient matrix operations. Rather than aiming to replace discrete-event simulators, FAST-LoRa is intended as a lightweight and accurate approximation tool for evaluating transmission parameter strategies in scenarios with stable traffic patterns and uplink-focused communications. In our evaluation, we compare FAST-LoRa with a well-established simulator using multiple network configurations with varying numbers of end devices and gateways. The results show that FAST-LoRa achieves similar accuracy in estimating key network metrics, even in complex scenarios with interference and multi-gateway reception, with a Mean Absolute Error (MAE) of 0.940 for the Packet Delivery Ratio (PDR) and 0.040 bits/mJ for Energy Efficiency (EE), while significantly reducing computational time by up to three orders of magnitude.
Keywords
Cite
@article{arxiv.2507.23342,
title = {FAST-LoRa: An Efficient Simulation Framework for Evaluating LoRaWAN Networks and Transmission Parameter Strategies},
author = {Laura Acosta García and Juan Aznar Poveda and Fabian Margreiter and Antonio-Javier García Sánchez and Joan García Haro and Thomas Fahringer and José Lorente López and José-Víctor Rodríguez},
journal= {arXiv preprint arXiv:2507.23342},
year = {2026}
}