# A Neural Network Propagation Model for LoRaWAN and Critical Analysis with Real-World Measurements
## 1 Introduction  
The low-power wide-area network technology has several advantages, such as low price and low power consumption. LoRa and SigFox are two of the main Internet of Things (IoT) Low-Power Wide-Area Network (LPWAN) technologies [1]. LoRa has more advantages, in addition to having a low power consumption and long range of coverage. One of its features is a proprietary chirp spread spectrum (CSS) modulation, which is resistant to interference and the Doppler effect [2]. The CSS modulation also improves the sensitivity of the device and increases the overall available link budget [3]. It is also internet protocol version 6 (IPv6) compatible; hence, it is capable of providing better security, scalability, and end-to-end connectivity [2, 4]. Unlike SigFox, the LoRa alliance is not an IoT network provider; therefore, no subscription is required, and there are no uplink or downlink restrictions regarding the number of messages per day [5, 6, 7]. The aforementioned advantages made LoRa favorable for the purpose of this research. Currently, Semtech is the main manufacturer of LoRa modules. LoRa commonly refers to a physical layer using the CSS, and LoRaWAN is an open standardMedia Access Control (MAC) layer protocol developed by the LoRa Alliance, which allows end devices to gateway communication. The outdoor propagation of LoRa and LoRaWAN has been studied in [8; 9; 10; 11; 12; 13; 14]. These studies were mainly focused on the: (a) coverage range of line of sight (LoS), (b) signal strength in central business districts, and (c) impact of modulation spreading factor on its propagation. The propagation ranges in these studies are therefore reported from two to 20 km, which is much lower than 50 km [15]. There have been a few studies where point-to-point LoRa communication has been investigated in indoor environments for the purpose of network sensor implementation [16; 17]. In this study, an outdoor-indoor scenario is investigated with an end-to-end connection, to observe the structural penetration of LoraWAN and its gateway. Therefore, practical measurements were collected, analyzed, and compared against common propagation models. These models include a log-distance, COST231 Multi-Wall Model (MWM). In addition to these models, artificial neural network (ANN) models are used to estimate the propagation. Since hybrid models are based on an ANN, they are capable of learning the propagation. Learning is achieved through a training process, where the ANN is exposed to sets of input-output parameters. ANN models are more accurate and less computationally demanding compared with non-deterministic and deterministic models, respectively [18]. However, one disadvantage of the ANN models is that they require a considerable amount of data collection over a vast area for the purpose of training, validation, and testing. For instance, in [19], 600,000 indoor data samples were collected, and learning took several hours. Authors in [18; 19; 20; 21; 22; 23; 24; 25] used ANNs to predict the propagation in indoor environments. In these studies, the main ANN inputs were the: distance between transceivers, number of walls, number of doors, number of windows, frequency of transmission, antenna gains, and even transmission power. However, two of the authors [20; 22] used \"free space path attenuation\" as a single input. This is an intuitive preprocessing of two individual inputs of distance and frequency, by taking advantage of the free space path loss formula. This approach (a) reduces the number of inputs to the ANN, which should help the learning by reducing the number of parameters; (b) makes the network needless of learning the basic propagation principles, such as calculating space path loss. A similar approach can also be applied to unify all of the attenuating factors such as the number of walls, windows, and doors or transmission power and antenna gain, which can form the effective radiated power. In these studies, the ANN had the responsibility of inferring the relations between the propagation parameters.