## 4 Simulation Results
### Throughput Evaluation  
We compare the throughput performance of the proposed algorithm with the outcomes of other existing methods. Figures 4 and 5 show the throughput evaluation and its cumulative distributive function, respectively.  
Figure 4 shows that the throughput performance gradually decreases as the distance increases. This result also indicates that the proposed algorithm outperforms other methods. When the UE distance is 104 meters, the proposed algorithm shows 2%, 13%, 13%, and 21% average throughput improvements as compared to coalitional game theory, capacitated affinity propagation, affinity propagation, and novel static clustering, respectively. In addition, when the UE distance is 496 m, the proposed algorithm shows 30%, 20%, 207%, and 304% average throughput improvements compared to coalitional game theory, capacitated affinity propagation, affinity propagation, and novel static clustering, respectively. In addition, Figure 5 indicates that the proposed algorithm provides higher average throughput performance in all percentiles. The average UE throughput percentage of less than 1.5 Mbps for the proposed algorithm, coalitional game theory, capacitated affinity propagation, affinity propagation, and novel static clustering are 16%, 26%, 33%, 43%, and 56%, respectively. The average UE throughput percentage of less than 2.5 Mbps for the proposed algorithm, coalitional game theory, capacitated affinity propagation, affinity propagation, and novel static clustering are 64%, 72%, 82%, 83%, and 90%. The average UE throughput percentage of less than 3.5 Mbps for the proposed algorithm, coalitional game theory, capacitated affinity propagation, affinity propagation, and novel static clustering are 95%, 96%, 99%, 98%, and 100%.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Number of cells & 7 cells \\ Number of transmitter antennas at each cell & 2 antennas \\ Number of receiver antennas at each user mobile & 1 antenna \\ Type of CoMP & Joint Processing \\ Number of users at each cell \({}^{*}\) & 100 users \\ Cell radius \({}^{*}\) & 500 m \\ BS Transmit Power & 40 dBm \\ Subcarrier spacing & 15 kHz \\ Number of subcarriers & 1200 \\ System bandwidth & 18 MHz \\ Noise & \(-174\) dBm\(/\)Hz \\ Wireless channel modeling & Short-scale fading (Rayleigh) \\ Pathloss exponent & 3.7 \\ Tranmission time interval & 1 ms \\ \hline \hline \end{tabular}  
* These parameters are not applied in the network scalability simulation.  
\end{table}
Table 1: Simulation Parameter.  
These results show that the proposed algorithm has higher throughput performance at every UE distance point. The proposed algorithm also maintains high performance in vulnerable areas, such as edge users. This is possible because the proposed algorithm attempts to reconsider all possible cooperating base stations by maximizing sum-capacity in order to determine the appropriate formation by the means of multi-layer assessment. This multi-layer message passing scheme increases the possibility of the best solution being achieved and prevents greedy choices in forming the cell clustering. The proposed algorithm also set outs proper cell clustering during the global optimality of the solution. Accordingly, the fixed point will get improved performance after the proposed algorithm reaches its convergence point.  
Figure 4: Average UE throughput versus the UE distance.  
Figure 5: Cumulative distribution function of the average UE throughput.