# Adaptive Clustering of Users in Power Domain NOMA
## 6 Results and Discussions
### Comparison with Conventional Approach  
The conventional method [35] for clustering users in a NOMA network consists of sorting users based on their channel gain values and grouping them in pairs following the near-far policy, i.e., grouping the user with the highest channel gain value with the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Time** & **2-UEs** & **3-UEs** \\ \hline
18.0 & \(6.53\times 10^{8}\) Bits/s & - \\
27.0 & \(6.52\times 10^{8}\) Bits/s & - \\
37.0 & \(6.63\times 10^{8}\) Bits/s & - \\
48.0 & \(6.72\times 10^{8}\) Bits/s & - \\
59.0 & \(6.62\times 10^{8}\) Bits/s & - \\
103.0 & \(6.38\times 10^{8}\) Bits/s & - \\
115.0 & \(6.23\times 10^{8}\) Bits/s & - \\
208.0 & \(6.49\times 10^{8}\) Bits/s & - \\
220.0 & \(6.53\times 10^{8}\) Bits/s & - \\
309.0 & \(6.31\times 10^{8}\) Bits/s & - \\
321.0 & \(6.30\times 10^{8}\) Bits/s & - \\
404.0 & \(6.38\times 10^{8}\) Bits/s & - \\
412.0 & \(6.33\times 10^{8}\) Bits/s & - \\
493.0 & \(6.41\times 10^{8}\) Bits/s & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average data rate and cluster formation in time with an 80 Hz doppler frequency.  
Figure 7: Behavior over time of the proposed model and OMA system.  
one with the lowest channel gain value and, subsequently, performing the same procedure repeatedly for the other ordered users, until all users are grouped. This technique has some disadvantages, such as the re-run of the sorting and clustering algorithm for every sample that arrives and the prior need to define a fixed number of clusters in the system, apart from leading to a limited number of two users per cluster.  
Such disadvantages of the conventional method can be circumvented with the proposed method in this article. The clustering algorithm works online, always adapting to new samples and is able to form a cluster of two or three users without the need for information on how many clusters should be formed. In addition, the proposed method is able to adapt the clusters as a function of the variation of the channel of each user in time without the need to re-run the algorithm.  
Furthermore, as the proposed method is based on an algorithm used in data streams, it leads to the best configuration for that user that has recently arrived in the network, analyzing the scenario at that instant. For example, the first user that arrives at the network cell waits for the arrival of the next user to perform NOMA, or its signal is transmitted by OMA. After the arrival of a second user, the best possible configuration is to group the two users in the network cell if the SIC detector and outlier restrictions are satisfied. When a third user arrives in the network cell, there are two options, either a cluster of three users is formed, or the signal of this third user is transmitted by OMA until a fourth user arrives that can be grouped with this user. As the proposed method groups according to the state that the network is in after the arrival of a user, the conventional method and the proposed method generate different clusters of users.  
However, compared to OMA, the proposed method needs to track the channel state information of each system's user over time and has greater computational complexity.