# Learning Approach for Angle Estimation Based on Characteristics of Phase Drift
## 4 Performance Evaluation
### Analysis of Experiment Results
#### 4.3.2 Computation Cost Evaluations
The SGRU model's simpler architecture may have led to a weaker generalization of these complex indoor conditions, resulting in inconsistent phase drift estimations and larger errors in certain angles. However, no external Wi-Fi or Bluetooth interference was present during the experiment, confirming that the error variations are primarily due to the indoor environment and model-specific processing differences. Figure 14 displays the LightGRU layer model's error range by angle, which demonstrates a more consistent error pattern across angles, similar to the GRU layer model in Figure 12. The standard deviation remains low for most angles, indicating reliable predictions. The variance is also minimal, suggesting that the LightGRU layer model performs well in maintaining consistent accuracy across different angles. Figure 15a compares the loss of three models (LightGRU, SGRU, and GRU) to evaluate their prediction accuracy. The y-axis represents the loss value (mean squared error), while the x-axis lists the three models being compared. Lower loss indicates better prediction performance, and the GRU layer model exhibits the lowest loss of 0.0052, indicating the best performance in terms of minimizing prediction error. The LightGRU layer model shows a slightly higher loss at 0.0128, indicating good but slightly inferior performance compared to the GRU layer model. The SGRU layer model displays the highest loss at 0.1413, suggesting significant performance degradation, likely due to its structural adjustments or simplifications. Figure 15b compares the inference time per sample for the three models. providing insights into computational efficiency. The GRU layer model exhibits the shortest inference time at 0.076387 s per sample, while the SGRU and LightGRU models show slightly longer inference times of 0.089537 s and 0.096357 s, respectively. Although the models have similar parameter counts (GRU: 250,689, LightGRU: 250,689, and SGRU: 250,241), their inference efficiency varies. This suggests that despite having comparable model complexity in terms of parameter count, architectural differences influence inference time. The GRU layer model is the most efficient in terms of inference time, while the  
Figure 14: Error analysis by angle for the LightGRU layer model. Figure 13: Error analysis by angle for the SGRU layer model. for indoor localization systems, particularly those utilizing AoA-based techniques, computational efficiency is a key factor in ensuring real-time processing. Since embedded systems often operate under strict resource constraints, models with lower inference times are more suitable for deployment. Despite having the same overall architecture, the GRU layer model demonstrates the shortest inference time, making it a strong candidate for resource-constrained environments. The LightGRU layer model offers a balance between performance and efficiency, exhibiting a slightly higher inference time while still maintaining competitive prediction accuracy. The SGRU layer model, despite its structural modifications, shows relatively higher inference time and greater variability in predictions, suggesting that additional considerations are needed when applying it to real-time indoor localization tasks on embedded systems.