标题： FAST-LoRa：一种用于评估LoRaWAN网络和传输参数策略的高效仿真框架 显示英文标题

标题： FAST-LoRa: An Efficient Simulation Framework for Evaluating LoRaWAN Networks and Transmission Parameter Strategies

摘要： 物联网（IoT）已经改变了许多行业，基于LoRa（长距离）技术的LoRaWAN（长距离广域网）已成为实现广域网中可扩展、低成本和节能通信的关键解决方案。 仿真工具对于优化传输参数以及因此提高LoRaWAN网络的能量效率和性能至关重要。 虽然现有的仿真框架通过包含多层通信协议来准确复制现实场景，但它们通常意味着显著的计算开销和仿真时间。 为了解决这个问题，本文介绍了FAST-LoRa，一种新的仿真框架，旨在实现对LoRaWAN网络的快速和高效评估以及传输参数的选择。 FAST-LoRa通过依赖分析模型而无需复杂的包级仿真，并使用高效的矩阵运算实现网关接收来简化计算。 FAST-LoRa并不是旨在取代离散事件仿真器，而是作为在具有稳定流量模式和上行链路导向通信的场景中评估传输参数策略的轻量级且准确的近似工具。 在我们的评估中，我们使用多个具有不同终端设备和网关数量的网络配置，将FAST-LoRa与一个成熟的仿真器进行比较。 结果表明，即使在具有干扰和多网关接收的复杂场景中，FAST-LoRa在估计关键网络指标方面也达到了相似的准确性，对于数据包传递率（PDR）的平均绝对误差（MAE）为0.940$\times 10^{-2}$，对于能量效率（EE）为0.040 比特/毫焦耳，同时将计算时间减少了多达三个数量级。 显示英文摘要

摘要： 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 $\times 10^{-2}$ 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.