# A Neural Network Propagation Model for LoRaWAN and Critical Analysis with Real-World Measurements
## 6 Conclusions  
The propagation of LoRaWAN was analyzed in an outdoor-indoor scenario and compared with commonly used propagation models. The performances of these models were briefly compared, and their advantages and shortfalls were discussed. An adjustment was made to the COST231 model, which made it more applicable to outdoor-indoor scenarios. A hybrid model was proposed, comprising an ANN and an optimized Multi-Wall Model--in this case, the adjusted COST231 model. This combination made the training process faster and easier rather than relying on an ANN only. It also diminished the number of data samples required for training the ANN. By using the ANN, the propagation estimation accuracy was improved. This improvement was achieved by first optimizing the COST231 propagation (\(F_{A231}\)). Second, the trained ANN was used to generate \(T_{E_{NN}}\), which is an estimate of the error in the optimized COST231, Figure 6a. Finally, the estimated error was added to the optimized propagation results (\(R\bar{S}S\!I=T_{E_{NN}}+F_{A231}\)), as shown in Figure 5, resulting in a more accurate prediction (\(R\bar{S}S\!I\)) of the practical measurements, Figure 6b. This hybrid model reduced the initial MSE of the COST213 from 21 to 11.23. * _[PERSON] et al. (2017)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Evaluation of LoRa LPWAN technology for indoor remote health and wellbeing monitoring. _Int. J. Wirel. Inform. Netw._**2017**, _24_, 153-165. [CrossRef]
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