# LoRa, Zigbee and 5G Propagation and Transmission Performance in an Indoor Environment at 868 MHz
## 5 Measurement Results and Discussion
### Power Measurements and Path Loss Modeling
#### 5.1.5 Discussion of Path Loss Models  
In the following discussion, the slope parameter of the path loss model of each technology will be analyzed. Table 1 shows the \(n\) value of the model from other works carried out in different environments under LOS and NLOS conditions. Table 3 groups the parameters of the models designed in this work. For free space propagation, the slope value of the CI model is equal to 2, and when the slope value is less than 2 it could be attributed to a waveguide effect [37; 41] due to the physical form of the corridor [16]. This only occurs in the CI model since it is the one that contemplates the physical behavior of the propagation by means of the \(n\) parameter; however, physical behavior is absent in the FI model [63]. As mentioned in Section 4, the CI model is not suitable for NLOS conditions because it does not achieve the best minimum error fit [57]. Nevertheless, it is suitable for some NLOS conditions, which can be verified by observing that the NLOS-1 slope values in the CI and FI models of the narrowband transmission are very similar, with an \(n\) of 1.97 and \(\beta\) of 1.94, respectively, because both models have the same NLOS behavior in this environment [37; 59]. However, the NLOS-2 slope values in the CI and FI models are not similar, \(n\) of 3.03 and \(\beta\) of 7.26, respectively, because the CI model adjusts with respect to \(L_{0}\) in the NLOS-1 zone, while the FI model makes use of the free parameter \(\alpha\) for the precise adjustment in the NLOS-2 zone, being independent of the free space propagation behavior in the NLOS-1 zone [57], thus achieving a precise fit, but detached from the physical behavior. Therefore, the FI model does not allow insightful information to be extracted immediately from the alpha and beta parameters [59]. For LoRa, Zigbee and 5G transmissions, the CI model shows a slope value between 1.91 and 1.98 in the NLOS-1 zone, which is near to those expected in indoor LOS conditions (see Table 1). On the other hand, for the NLOS-2 zone, the slope value of the CI model is between 3 and 3.15 for NB, LoRa and 5G transmissions, also very close to the values of other studies (see Table 1), where, fundamentally, the values of \(n\) for the LOS zone are around 2 and the values of \(n\) for the NLOS zone are around 3 for an indoor environment that is mainly diaphanous and somewhat narrow, like the corridor studied in this work. Therefore, it can be observed that the NLOS-1 zone behaves very similarly to a LOS condition. The CI model is widely used by other researchers because the use of \(d_{0}=1\) m provides an easy comparison of various measurement campaigns in different environments [59]. The worst fit (\(\sigma\)) of the CI model with respect to the FI model may be due to the fact that the power measurements taken in LoRa, Zigbee and 5G transmissions make use of their own power indicators, RSSI and SS-RSRP, which operate in their own range and have no direct relationship with the \(L_{0}\) factor resulting from the calculation of free space losses for a distance of one meter. Therefore, the fit of the CI model will be partially erroneous in these cases, although a large difference has not been found in this work. From the data in Tables 1 and 3, we can outline the following conclusions:  
* The results of the CI and FI models in the NLOS-1 zone exhibit behavior very similar to the results of other studies under LOS conditions. * The CI model provides a correct fit for LOS measurements but is not suitable for NLOS measurements. This model considers the physical behavior of the propagation using the \(n\) parameter since it adjusts the path loss to a reference distance \(d_{0}\). Because it is fitted by a reference parameter, it is possible to compare the results of different measurement campaigns immediately. * The FI model provides the best fit for both LOS and NLOS situations because the fit is made by two free parameters, \(\alpha\) and \(\beta\), although it is difficult to compare results and draw immediate conclusions.