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This paper investigates the impact of different channel models on LoRaWAN gateway placement by formulating an optimization problem that contrasts stochastic, empirical, and ray-tracing-based models. They developed a framework integrating ray tracing simulators with a discrete-event network simulator to generate LoRaWAN wireless data metrics. Results demonstrate that optimized gateway placement is highly sensitive to the chosen channel model, highlighting a trade-off between computational cost and solution fidelity.
Optimized LoRaWAN gateway placement hinges on the channel model used, with ray tracing offering higher fidelity but at a significant computational cost.
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.
