Network planning is a fundamental task in wireless communications, primarily focused on guaranteeing adequate coverage for every network device. In this context, the quality of any planning effort strongly depends on the channel model adopted in the design process of the simulations. Given this motivation, this work investigates how different channel models influence the placement of Long Range Wide Area Network (LoRaWAN) gateways (GWs), formulating an optimization problem that contrasts stochastic and empirical models with ray-tracing-based models. To this end, we developed a framework that integrates ray tracing (RT) simulators with a discrete-event network simulator. Using this framework to generate long range wide area network (LoRaWAN) wireless data metrics, we employ an optimization model that determines the optimized GW placement under different channel models and power constraints. Our results show that the optimized solution is highly sensitive to the chosen channel model, even when considering the same scenarios with different RT simulators, revealing a clear trade-off between computational cost and the fidelity of the solution to real-world conditions.

翻译：网络规划是无线通信中的基础任务，主要致力于确保每个网络设备获得足够的覆盖。在此背景下，任何规划方案的质量都高度依赖于设计仿真过程中所采用的信道模型。基于这一动机，本研究探讨了不同信道模型如何影响远距离广域网（LoRaWAN）网关（GW）的部署，构建了一个对比随机模型、经验模型与基于射线追踪模型的优化问题。为此，我们开发了一个集成射线追踪（RT）模拟器与离散事件网络模拟器的框架。利用该框架生成远距离广域网（LoRaWAN）无线数据指标，我们采用优化模型来确定在不同信道模型和功率约束下的最优网关部署。结果表明，即使在相同场景中使用不同的RT模拟器，优化解对所选信道模型也高度敏感，揭示了计算成本与解对真实世界条件保真度之间的显著权衡。