## 2 Related Work  
Model compression techniques, such as pruning [11, 12], quantization [16], and knowledge distillation [9], have attracted considerable interest as effective strategies to reduce the size and computational load of deep neural networks. These techniques are especially relevant for deployment on edge devices and embedded platforms, where computational resources and energy budgets are limited. Among these methods, pruning is often the first step considered due to its intuitive goal: removing redundant or less critical param eters to streamline the inference process. However, pruning methods can face notable obstacles. For instance, when applied to small datasets or domain-specific tasks with limited training samples, aggressive pruning may exacerbate overfitting and fail to yield meaningful improvements in model efficiency [15]. This trade-off highlights the need for more nuanced strategies that balance accuracy and complexity without compromising model generalization.  
In parallel with compression methods, Neural Architecture Search (NAS) has emerged as a powerful paradigm for automating model design [14]. Instead of relying on manual trial-and-error approaches, NAS systematically explores a large space of possible architectures to identify models that meet specific accuracy, latency, or memory constraints. Bayesian Optimization, in particular, has gained popularity as an NAS technique due to its sample efficiency and capability to navigate complex, high-dimensional search spaces [11]. By iteratively refining architectural candidates based on observed performance, Bayesian Optimization-based NAS algorithms can converge toward architectures that outperform manually engineered models in various domains, including vision and language tasks [14; 19].  
Within the WiFi sensing community, deep learning has played a significant role in advancing state-of-the-art performance in human activity recognition tasks. Convolutional Neural Networks (CNNs) have been shown to effectively capture the spatiotemporal characteristics of Channel State Information (CSI) data, enabling accurate classification of user activities, gestures, and even user identification [2; 3]. Despite these advances, most current approaches rely on architectures adapted from other fields or straightforward manual design choices. While this has led to notable improvements in accuracy, it often comes at the cost of increased model complexity and greater computational demands.  
So far, relatively few efforts have sought to integrate model compression techniques and NAS into WiFi sensing model design. Much of the existing work on compression has concentrated on standard vision benchmarks, while NAS research has largely focused on broad application domains where large-scale data and extensive computational resources are readily available. Transferring these techniques directly to WiFi-based tasks is not trivial due to differences in data distributions, environmental dependencies, and domain constraints such as limited training data and specialized signal processing requirements. Moreover, the inherent complexity of CSI signals and the subtlety of human motions captured through WiFi channels necessitate architectures that are both expressive and efficient.  
This paper addresses these gaps by systematically exploring pruning-based compression and extending Bayesian Optimization-based NAS frameworks to the WiFi sensing domain. We evaluate pruning strategies on multiple CNN architectures, revealing their strengths and limitations when applied to realistic WiFi motion recognition scenarios. Building on these insights, we employ Bayesian Optimization to automatically discover streamlined network architectures tailored for WiFi-based activity recognition. By doing so, we aim to uncover architectures that not only deliver state-of-the-art accuracy and robustness but also achieve these results with fewer parameters, reduced latency, and greater suitability for deployment in resource-constrained real-world settings.