# Tethered Balloon Cluster Deployments and Optimization for Emergency Communication Networks
## 5 Simulation Validations and Discussion
### Simulation Results
Figures 5-7 demonstrate the relationship between total throughput, average delay, packet loss rate, and number of balloons when the communication system adopts MOPSO and SA, respectively, under five distribution scenarios for users. The experimental results show that MOPSO achieves a 37.09% improvement in total throughput, a 34.86% decrease in average delay, and a 13.69% reduction in packet loss rate compared to SA. From a physical point of view, these improvements can be attributed to better deployment locations causing better SINR management and more efficient utilization of radio resources. MOPSO always outperforms SA, especially when the number of balloons is high. The performance advantage of MOPSO is mainly attributed to its ability to optimize the deployment locations of balloons. Through a more reasonable deployment scheme, MOPSO can effectively avoid the overlapping of balloon coverage and mutual interference, shorten the data transmission  
Figure 3: The convergence curves for MOPSO and SA. It can be observed from Figures 4(a)-4(a) that MOPSO can effectively optimize balloon locations and power allocation to increase the network capacity while keeping the latency and packet loss rate low. This is achieved through optimal spatial diversity exploitation and the efficient management of the radio frequency spectrum. MOPSO tries to find a better combination of locations in each iteration to maximize the coverage and the signal quality and thus improve throughput. However, when the numbers of balloons are 4, 7, and 9, there is a significant drop in throughput. That is attributed to the fact that it may fall into a local optimal solution, resulting in little difference in the location optimization effect for the increased numbers of balloons. From an electromagnetic wave propagation perspective, as the number of balloons increases, the interference in the network increases accordingly, which partially cancels out the performance improvement brought by MOPSO, resulting in a fluctuating increase in throughput. This phenomenon can be explained by the increased probability of co-channel interference and multipath fading effects in denser networks. The adaptive power control mechanism plays a moderating role in this process by dynamically adjusting the transmit power in an attempt to find a balance between coverage and interference control. This power adaptation directly affects the received  
Figure 6: Comparison of MOPSO and SA on average delay under different user distribution scenarios. Figure 7: Comparison of MOPSO and SA on packet loss rate under different user distribution scenarios. signal strength and interference levels at user terminals, creating a complex electromagnetic environment that requires careful management. Network performance fluctuations can be regarded as a dynamic outcome reached by these multi-party games. In contrast, as shown in Figures 5b-7b, the overall performance of SA is not as good as that of MOPSO, which is mainly due to the lack of reasonable planning of balloon locations, resulting in overlapping coverage and causing signal interference and resource wastage. This leads to destructive interference patterns and increased inter-cell interference, degrading the physical layer performance. As the number of balloons increases, this overlap and interference problem becomes more serious, which in turn leads to poor overall network performance.