## 4 Experimental Results and Analysis
### Structured Pruning Analysis  
The NTU-Fi dataset demonstrates remarkable resilience to pruning, exhibiting distinctive behavioral patterns across different model architectures, as shown in Figure 5. Quantitative analysis reveals compelling evidence of systematic performance improvements across all model variants. The NTU-Fi dataset demonstrates remarkable resilience to pruning, exhibiting distinctive behavioral patterns across different model architectures. As shown in Figure 5, our approach combines pruning and fine-tuning to achieve the high accuracy identified during the NAS process (88.30%). Through iterative pruning and fine-tuning steps, Model 1 not only reduces its parameter count but also gradually improves its accuracy from 63.64% to 87.12%, ultimately approaching the potential performance discovered by NAS. This suggests that our method successfully maintains essential feature representations while eliminating redundant parameters. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Dataset** & **Best Acc. *** & **Mean Acc.** & **Std Acc.** & **Improvement** & **Initial** & **Final** \\ \hline NTU-Fi-HumanID & 70.00 & 56.91 & 14.64 & 52.73 & 55.45 & 58.18 \\ NTU-Fi & 88.30 & 75.59 & 6.28 & 22.87 & 75.00 & 81.91 \\ Widar & 50.42 & 42.58 & 5.86 & 18.68 & 42.79 & 50.42 \\ UT & 57.41 & 48.52 & 8.24 & 28.02 & 48.12 & 57.41 \\ \hline \hline \end{tabular}  
* Acc.: Accuracy  
\end{table} 
Table 2: Neural Architecture Search performance summary. Figure 4: NAS progression for Widar dataset showing iteration-wise accuracy (**left**) and architecture performance ranking with ascending values (**right**). For Models 2, 3, and 4, we observe similar patterns where the combination of pruning and fine-tuning leads to significant accuracy improvements while reducing model size. The final accuracies of these models (91.67%, 85.61%, and 84.85%, respectively) demonstrate that our approach successfully achieves both model compression and performance enhancement, closely aligning with or even exceeding the accuracy levels identified during the initial NAS phase. This confirms that our strategy of coupling NAS-guided architecture design with structured pruning effectively produces lightweight yet high-performing models for WiFi sensing applications. The observed patterns can be characterized by three key findings:  
1. **Pruning Resilience**: All models maintain or improve accuracy despite significant parameter reduction, with improvements ranging from 23.48 to 51.52 percentage points. 2. **Stage Efficiency**: Models exhibit different optimal pruning patterns, from gradual progression (Model 1) to dramatic early improvement (Model 2) to effective single-stage optimization (Models 3 and 4). 3. **Architecture Robustness**: The consistent improvement across diverse architectural variants suggests inherent robustness in the base architecture design, enabling effective parameter optimization without compromising model performance. These diverse yet consistently positive responses to pruning across all model variants highlight the inherent robustness of the architectures developed for the NTU-Fi dataset. The observed patterns suggest that our pruning strategy successfully identifies and preserves critical feature extractors while eliminating redundant parameters, resulting in more efficient models without compromising--and in many cases, substantially improving--recognition accuracy. Analysis of the NTU-Fi-HumanID dataset reveals a systematic and uniform pattern of accuracy improvements across all model variants under 50% pruning conditions, as shown in Figure 6. The quantitative results demonstrate remarkable consistency in performance enhancement across all architectures. Model 1's accuracy improves from 33.67% to 63.95%, suggesting effective preservation of discriminative features critical for human identification.