## 3 Simulation Results  
In this section, simulation results from different communication scenarios and energy modeling approaches that have been discussed in the previous section will be described. Key simulation settings are shown in Table 2.  
In the first step, our simulation analyzed how D2D communications assists the outage users communicate to the BS or a small cell via other users located within the coverage area of the macro-cell BS for the scenario in Figure 1. There are two issues in this scenario, the first one is the security of data transmissions with multi-hop communications between MTC devices and the second one is the increased energy consumption. Regardless of the security issue, which is not in the scope of this paper, we considered an energy efficient scenario where only one extra hop is allowed to extend the coverage to the users in outage. For the proposed model, the users in outage discover nearby MTC devices within the coverage area of the BS or small cells and measure the required energy to transmit the data packets to those devices. Then the network will choose the most energy efficient communication link to one of the nearby devices for data transmission. Table 3 shows the baseline coverage analysis for both LTE and NB-IoT devices. Table 3 depicts that NB-IoT with an extra 20 dB of permitted MCL can reduce the percentage of users in outage to half of that for devices using LTE communication technology in the same size macrocell. As can be seen in Figure 4, the total percentage of outage users for NB-IoT is around 30% without deploying small cells or D2D. By increasing the number of MTC devices within the macro-cell coverage region, the proportion of outage users can be improved to 27% and to 10% with 100 and 1000 D2D enabled users, respectively. The result shows that D2D communication can also be considered an effective solution to improve coverage if the issues associated with energy consumption and security are resolved for MTC devices. \begin{table}
\begin{tabular}{l l} \hline
**Parameters** & **Values** \\ \hline NB-IoT Macro-cell Radius & 6 km \\ \hline Path-loss model Base Station-IoT Device distance: & Equations (3) and (4) \\ d(km) & \\ \hline Log-normal fading standard deviation & from shadow-fading map \\ \hline Femto eNB EIRP & 20 dBm \\ \hline Pico eNB EIRP & 35 dBm \\ \hline Maximum Transmit Power of MTC device & 23 dBm \\ \hline Maximum Transmit Power of Main BS & 46 dBm \\ \hline Number of Small Cells & Up to 200 \\ \hline Number of MTC Devices & 300 and up to 1000 for the D2D scenario \\ \hline \(f_{c}\) for NB-IoT & 900 MHz \\ \hline \end{tabular}
\end{table} 
Table 2: Simulation Key parameters. \begin{table}
\begin{tabular}{c c} \hline
**Communication Technology** & **Percentage of Users in Outage** \\ \hline LTE & 71 \\ \hline NB-IoT & 28.6 \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of the number of outage users in NB-IoT and LTE (4G) technologies. An interesting point is about how results differ from [3]. First of all, the number of outage users does not decrease significantly for a small number of 20 femto cells. Still, for both path-loss models in Figure 2, around 25% of users will remain in outage. On the other hand, by increasing the number of femto cells to 200 femto cells, we can improve the coverage and the number of outage users will be reduced significantly when compared to the simple model in [3]. The results in Figure 5 and Table 4 show that only 7.3% and 6.8% of users will remain in the outage for two path-loss models, respectively. The results for deploying pico-cells are different. In contrast to [3] which has less than 15% of users in outage for deploying only 20 pico-cells, the results in Figure 6 and Table 4 do not show significant outage reduction with this number of pico-cells. This is regardless of their being inside the buildings or outside the building for a realistic shadow-fading map.