# Analysis of Power Allocation for NOMA-Based D2D Communications Using GADIA
## 6 Results and Discussion
### D2D System Model  
In this scenario, we consider a D2D system model containing a cellular system where the number of assigned users follow a Poison Point Process (PPP) and the user's spatial position is randomly distributed following a uniform distribution. We assume the D2D users are also distributed following a Poisson point process. In this part, the downlink data of the base station received by the UEs receiving end is optimized in combination with D2D. For our study, we consider a square cell, where the BS is omnidirectional and placed at the cell centre. The research is focused on the downlink scenario. A crucial part of the downlink is power control. Furthermore, we reflect shadow fading, represented as a Gaussian random variable \(X\) with zero mean and standard deviation in dB. Recalling the GADIA algorithm, we create two different scenarios where GADIA allocates the D2D users in rural, and urban environment. For the convenience of the reader, the system parameters are summarized in Table 3 unless otherwise stated. The GADIA algorithm is applied to a single random user deployed in the square unit. It then conducts a continuous iterative analysis of its performance, focusing on the interference of its behaviour. After several iterations, the GADIA algorithm is applied to different random users to obtain the average value of its performance. This process is iterated until the number of users is exhausted. The BS is illustrated as a red point in the square cell centre, BS users as blue squares, and D2D connections as green triangles. Figures 13 and 14, respectively, illustrate distribution of UEs within a range of 1 km using GADIA for rural and urban scenario under consideration. After the frequency allocation using GADIA, D2D users will stay within their frequency or change into the BS users. That shows how users are stationed in this simulation model for the two studied environments. Moreover, D2D users (represented with triangles) are colored with green (if they remain to  
Figure 12: Data rate comparison of NOMA vs. OFDMA in rural areas. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Urban** & **Rural** \\ \hline BS Sensitivity (dBm) & \(-100\) & \(-100\) \\ BS Coverage Area & \(1\) km\({}^{2}\) & \(1\) km\({}^{2}\) \\ UEs Max Tx Power & \(30\) dBm & \(30\) dBm \\ Path Loss Exponent & \(6\) & \(2\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: System parameters. utilise the non-shared frequency band) or cyan (if they turn to the shared frequency band). The figures represent how the D2D users are allocated with one or another frequency according to the number of interference signal received from these maps. After several iterations, the average interference of the two scenes stabilized, and the mean interference received by D2D users represent in Figures 15 and 16. The two other variables have been pursued along GADIA iterations to determine the whole network's performance ultimately: mean interference received by all network users (red line) and mean received interference of cellular network users (blue line). The simulation results for the two studied environments confirm how the D2D user's performance is enhanced at the expense of a deterioration of the cellular network users performance. The average interference of D2D users in cities is \(-60\) dBm, and the moderate interference of D2D users in rural areas is \(-30\) dBm. That effect can be described by increasing the interference that  
Figure 14: Location map after GADIA for urban environment. Figure 13: Location map after GADIA for rural environment. cellular network users receive as the D2D users are allocated with the second frequency band (shared with the cellular network users). As it can be seen in Figure 17, employing GADIA controlled frequency allocation brings improvements compared to random frequency allocation or not using any kind of frequency sharing approach. It should be noted that the final simulation result strongly relies on the initial deployment of users.