# Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning
## 5 Experiments and Results
### Case Study 2: Experiments with AIDA, Using Erceg-SUI Propagation Model  
In this section, we describe the additional experiments carried out to evaluate the AIDA method using a general path loss model, which does not assess the particularities of the terrain profile between specific smart meters and candidate positions. For this, the method AIDA was adapted to use the Erceg-SUI propagation model (SUI model), implemented as presented by the authors in [6,7] (IEEE 802.16.3c-01/29r4, section Suburban Path Loss model). The Erceg-SUI model corresponds to a path loss model computed by considering a general classification of the terrain in which the communication devices are installed. The Erceg-SUI model considers the existence of the following terrain types: Category A--hilly/moderate-to-heavy tree density; Category B--hilly/light tree density or flat/moderate-to-heavy tree density; Category C--flat/light tree density. Originally, the model was specified based on data collection in 95 regions of the United States considering a transmission frequency of 1.9 GHz. Thus, for its use in our experiments, frequency and receiver antenna height correction factors were used as recommended in [7]. According to [6,7], the equation for calculating the Erceg-SUI model Path Loss is expressed as (20):  
\[PL_{SUI}=A+10\gamma\log_{10}(d/d_{0_{SUI}})+s;\ \ \ d\geq d_{0_{SUI}} \tag{20}\]  
where \(A\) is the decibel path loss at distance \(d_{0_{SUI}}\) and \(s\) is the shadow fading variation about the linear relationship. The authors in [6] call \(A\) the intercept value, and they choose a value for \(d_{0_{SUI}}\) of 100 m. In addition, \(A=20\ \log_{10}(4\ \pi\ d_{0_{SUI}}/\lambda)\), where \(\lambda\) is the wavelength in m, \(\gamma\) is the path-loss exponent with \(\gamma=(a-bh_{b}+c/h_{b})+x\sigma_{\gamma}\) for \(h_{b}\) between 10 m and 80 m (\(h_{b}\) is the height of the base station in m), and \(a\), \(b\), and \(c\) are constants dependent on the terrain category, as shown in Table 7. The shadowing effect (\(s\)) follows a lognormal distribution, and it is calculated using (21):  
\[s=10\ x\ \sigma_{\gamma}\ \log_{10}(d/d_{0_{SUI}})+y\ \mu_{\sigma}+y\ z\ \sigma_{\sigma} \tag{21}\]  
where \(x\), \(y\), and \(z\) are independent zero-mean Gaussian variables of unit standard deviation, \(N[0,1]\), as defined in [6]. The frequency and receiver antenna height correction factors are calculated by Equations (22)-(24). Equation (22) defines the frequency correction factor, where \(f\) is the frequency in MHz. \[\Delta PL_{f}=6\ \log_{10}(f/2000) \tag{22}\]The \(\Delta PL_{h}\) (in dB) is the receiver antenna height correction term given by Equations (23) and (24), where \(h\) is the height of the antenna, between 2 m and 10 m.  
\[\Delta PL_{h}=-10.8\ \log_{10}(h/2);\quad\text{ for Categories A and B} \tag{23}\]  
\[\Delta PL_{h}=-20\ \log_{10}(h/2);\quad\text{ for Category C} \tag{24}\]  
The path loss with the application of correction factors is calculated as follows (25):  
\[PL_{modified}=PL_{SUI}+\Delta PL_{f}+\Delta PL_{h} \tag{25}\]  
For case study 2 experiments, the AIDA method was adapted to use the modified path loss presented by the Erceg-SUI model instead of the Delta-Bullington model. Experiments were carried out using the top-down approach of the AIDA method, considering the four regions previously used and performing an experiment for each region (A, B, C, D) and each type of terrain (category A, category B, and category C) proposed by the SUI-model. The results presented in Table 8 show that, in most cases, using the AIDA method, it was possible to obtain a better average LRP value than that obtained with the SUI-model (Figure 8). By evaluating the terrain in a more detailed way, it can even be said that the particularities of the terrain between the devices are more faithfully evaluated, leading to an LRP calculation that tends to be more accurate. An exception is Region B, which presented better results for the Erceg-SUI path loss model.