# Dynamic Divide Grouping Non-Orthogonal Multiple Access in Terrestrial-Satellite Integrated Network
## 6 Simulation and Analysis
### Simulation Results and Analysis  
In Figure 6, we investigate the variation of the system capacity with different \(\gamma_{S}\) in three cases. We can observe that if less BS users are in the network, the decreasing speed of the capacity of BSs will be larger than the increasing speed of the capacity of the satellite at the first, and then the increasing speed will exceed the decreasing speed as the \(\gamma_{S}\) continues to increase. If there are more BS users, either by increasing the number of BSs, as illustrated in Figure 6 where we increase \(I\) from 5 to 30, the decreasing speed of the BSs will always be less than the increasing speed of the satellite. Thus, with the help of the satellite, the \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Orbital altitude & 1000 km \\ Number of beams & 5 \\ BS number of beam coverage & 10 \\ Number of BS subchannels & 10 \\ Number of beam subchannels & 50 \\ User terminal height & 1.5 m \\ BS height & 10 m \\ Coverage radius of BS & 5 km \\ Coverage radius of beam & 50 km \\ The maximum power of group \(P_{\text{max}}\) & 18 dBm \\ Satellite antenna gain & 25 dBi \\ BS antenna gain & 17 dBi \\ User antenna gain & 0 dBi \\ Noise power spectral density & \(-174\) dBm\(/\)HZ \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameter. network can provide service for more users simultaneously, especially for those users with bad BS channel conditions. Sacrificing part of the capacity of the users with good channel conditions, the system can provide better service for those users that have no access or bad access to the BSs by introducing the satellite to the system, and the total user number that can be served is also increased. Figure 7 compares the total throughput performance of different algorithms, it can be seen that the increase of SINR threshold improves the total throughput of all algorithms, because the higher SINR threshold increases the power allocated by the user terminal, thus improving the total throughput. Compared with CC-NOMA and DC-NOMA, the proposed DD-NOMA has the best throughput performance. When the SINR threshold is 10 dB, the total throughput of DD-NOMA is approximately 28% and 34% higher than that of CC-NOMA and DC-NOMA. The reason is that DD-NOMA couples users with large channel differences into a NOMA group, and uses the joint iterative algorithm to get the optimal resource allocation, better system capacity can be obtained. In contrast, CR-OMA can not achieve signal superposition on the same time-frequency resource, and its throughput performance is lower than other NOMA algorithms. In CC-NOMA, authors proposed a channel correlation coefficient algorithm to pair users with the largest difference in current channel conditions. However, the algorithm is local optimal, it can hardly guarantee the channel difference of remaining users. As a result, the throughput of remaining users would be reduced. Due to the lack of stable pairing guarantee mechanism in DC-NOMA algorithm, the total throughput is lower than the proposed DD-NOMA. To verify the performance of the proposed DD-NOMA algorithm when the channel gain distribution is uneven, the definition of \(\eta\)=\(\frac{d_{min}}{R}\), which can reflect the distribution uniformity of far and near users. Figures 8 and 9 compare the average throughput of BS remote users and satellite remote users of the four algorithms under this parameter. It can be seen that the throughput of remote users decreases with the increase of \(\eta\). The reason is that the larger the \(\eta\), the more remote users are distributed at the cell edge, which reduces the channel gain of remote users and leads to the decrease of throughput. CR-OMA algorithm can not guarantee the throughput of remote users because it can provide resources for remote users while ensuring the demand of primary users.