## 4 Experimental Results and Analysis
### Neural Architecture Search Performance Analysis  
The NTU-Fi dataset demonstrates remarkable search stability and performance, as shown in Figure 1. Starting from 75.00%, accuracy peaks at 88.30% in iteration 4, with a moving average ranging from 71.99% to 77.48%. The trend line shows steady improvement from 66.11% to 85.06%. Particularly notable is the rapid early improvement from iterations 1 to 4 (75.00% to 88.30%), followed by consistent performance above 75%. The sorted accuracies reveal that 80% of discovered architectures achieve above 69.14%, indicating robust exploration.  
As shown in Figure 2, the NTU-Fi-HumanID search exhibits high volatility but strong recovery capabilities. The trajectory shows dramatic fluctuations: from 55.45% initially to a peak of 70.00% at iteration 2, followed by a significant drop to 17.27% at iteration 8. The moving average reveals three distinct phases: initial improvement (58.79%), stability around 63-67%, and final adjustment (44.85%). Despite volatility, the trend line maintains positive progression from 37.87% to 75.95%.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Phase** & **Optimizer** & **Learning Rate** & **Batch Size** & **Epochs** \\ \hline NAS Search & [PERSON] & \(10^{-3}\) & 64/256 * & 20–30 ** \\ Model Training & [PERSON] & \(10^{-3}\) & 64/256 * & 100 *** \\ Pruning Fine-tuning & [PERSON] & \(10^{-4}\) & 64/256 * & 5 \\ \hline \hline \end{tabular}  
* Training/Validation batch sizes, *** With early stopping (patience = 5), *** With early stopping (patience = 10).  
\end{table}
Table 1: Common training settings.  
The UT-HAR dataset demonstrates interesting recovery patterns as shown in Figure 3. Starting at 48.12%, it experiences multiple fluctuations, including a significant drop to 29.40% at iteration 6, followed by strong recovery to 56.91% at iteration 7. The moving average ranges from 40.91% to 52.72%, while the trend line shows consistent improvement from 36.56% to 60.48%. The distribution of accuracies shows that 70% of architectures achieve above 47.86%.  
As shown in Figure 4, the Widar dataset exhibits the most systematic improvement pattern. From an initial 42.79%, accuracy steadily improves to 50.42%, with the moving  
Figure 1: NAS progression for NTU-Fi dataset showing iteration-wise accuracy (**left**) and architecture performance ranking with ascending values (**right**).  
Figure 3: NAS progression for UT-HAR dataset showing iteration-wise accuracy (**left**) and architecture performance ranking with ascending values (**right**).  
Figure 2: NAS progression for NTU-Fi-HumanID showing iteration-wise accuracy (**left**) and architecture performance ranking with ascending values (**right**).  
average showing clear upward progression from 38.68% to 49.07%. The trend line demonstrates consistent improvement from 33.55% to 51.60%. The sorted accuracies show linear improvement, suggesting effective exploration-exploitation balance.  
The Neural Architecture Search performance analysis (Table 2) shows optimization outcomes across different WiFi sensing datasets. Through iterative optimization, NTU-Fi achieved the highest accuracy (88.30%) with stable performance (std: 6.28%), while NTU-Fi-HumanID showed the most significant improvement (52.73%). The Widar and UT datasets demonstrated balanced optimization with improvements of 18.68% and 28.02% respectively, validating the effectiveness of our search strategy across diverse WiFi sensing applications.