## 4 Result and Discussion
### Comparison of LSGB and BRFFNN Based on UAV Movement Pattern
#### 4.8.2 Random UAV Movement  
In this scenario, the UAV follows a random movement pattern, and the same QoS metrics (PDR, throughput, and delay) were evaluated. The bandwidth allocation results using the BRFFNN and LSGB models are shown in Figure 12. For random UAV movement, the average PDR, as seen in Figure 12(a), is 95.13% using the LSGB model, and 95.15% using the BRFFNN model. Figure 12(b) presents the average throughput, where the BRFFNN achieves 3.841 million packets per second (pps), compared to 3.272 million pps using LSGB for bandwidth estimation. Additionally, Figure 12(c) illustrates the data transmission delay, showing that the BRFFNN achieves a lower delay of 0.0465 ms compared to 0.0542 ms for LSGB. These results indicate that the BRFFNN consistently outperforms LSGB, achieving a higher PDR, increased throughput, and lower latency. This performance advantage is observed in both circular and random UAV movement patterns, demonstrating the suitability of the BRFFNN for bandwidth allocation in UAV-GS communication under 5G conditions.  
The simulation results reveal that there are differences in the performance of data transmission between the UAV-GS when using the LSGB and BRFFNN models for bandwidth estimation. BRFFNNs are effective at learning complex, non-linear interactions between features, especially in mixed data types such as categorical and numerical. They can work effectively with categorical data through one-hot encoding or embeddings, making them adaptable to both large and small categories of data. Least squares gradient boosting is also effective for estimating bandwidth allocation; however, it has a slightly inferior performance compared to the BRFFNN. Ongoing research and refinement using different ML-based bandwidth allocation methods are essential to enhance their accuracy and reliability, ultimately advancing the QoS of UAV communication systems.  
Although the quantitative results show that the BRFFNN consistently performs better than LSGB in terms of throughput, delay, and the PDR, additional investigation identifies a number of contributing factors. In order to improve generalization and avoid overfitting, the BRFFNN first employs Bayesian regularization during training, particularly in the noisy, unpredictable input conditions that are common in UAV communication environments.  
Figure 11: Result for (**a**) PDR, (**b**) throughput, and (**c**) delay for circular UAV movement.  
As a result, the model becomes more dependable and stable over several simulation runs. On the other hand, LSGB may need extensive hyperparameter tuning and is more likely to overfit in compact or moderately noisy datasets, despite its effectiveness in capturing non-linear relationships. Furthermore, on resource-constrained UAV platforms, the BRFFNN exhibits a lower computational cost during inference, which makes it more appropriate for real-time bandwidth prediction. In comparison to the trained BRFFNN's single, compact structure, the ensemble nature of LSGB, which is based on hundreds of low decision trees, increases memory usage and inference time from a complexity standpoint. These features account for the BRFFNN's demonstrated superiority in accuracy, operational robustness, and deployment efficiency.