A neural network propagation model for LoRaWAN and critical analysis with real-world measurements

Among the many technologies competing for the Internet of Things (IoT), one of the most
promising and fast-growing technologies in this landscape is the Low-PowerWide-Area Network
(LPWAN). Coverage of LoRa, one of the main IoT LPWAN technologies, has previously been
studied for outdoor environments. However, this article focuses on end-to-end propagation in
an outdoor–indoor scenario. This article will investigate how the reported and documented outdoor
metrics are interpreted for an indoor environment. Furthermore, to facilitate network planning
and coverage prediction, a novel hybrid propagation estimation method has been developed and
examined. This hybrid model is comprised of an artificial neural network (ANN) and an optimized
Multi-Wall Model (MWM). Subsequently, real-world measurements were collected and compared
against different propagation models. For benchmarking, log-distance and COST231 models were
used due to their simplicity. It was observed and concluded that: (a) the propagation of the LoRa
Wide-Area Network (LoRaWAN) is limited to a much shorter range in this investigated environment
compared with outdoor reports; (b) log-distance and COST231 models do not yield an accurate
estimate of propagation characteristics for outdoor–indoor scenarios; (c) this lack of accuracy can be
addressed by adjusting the COST231 model, to account for the outdoor propagation; (d) a feedforward
neural network combined with a COST231 model improves the accuracy of the predictions. This
work demonstrates practical results and provides an insight into the LoRaWAN’s propagation in
similar scenarios. This could facilitate network planning for outdoor–indoor environments.