## 1 Introduction  
WiFi sensing technology has recently gained attention as a non-invasive, privacy-preserving alternative to camera-based systems, leveraging subtle variations in Channel State Information (CSI) to recognize human activities [1]. By exploiting WiFi signals to detect, classify, and interpret various behaviors, WiFi-based sensing techniques have found utility in smart homes, healthcare, and security systems, enabling tasks like people counting, motion recognition, and gesture analysis [2, 3, 4, 5, 6]. In particular, the availability of diverse benchmark datasets has propelled progress in WiFi-based human sensing research. For example, datasets such as UT HAR and Widar, as well as recently introduced datasets like NTU Fi HAR and NTUFi HumanID, collectively offer a range of scenarios, sensing platforms, and data modalities [4]. These datasets enable the development and evaluation of deep learning models across tasks, including human activity recognition, gesture recognition, and identification. By incorporating data from multiple domains, resolutions, and experimental conditions, researchers can design and validate models that generalize better, ultimately supporting more robust and versatile WiFi sensing applications [7, 8, 9, 10]. Despite these advancements, environmental factors such as interference, user variability, and diverse deployment conditions continue to pose significant challenges. Modelstrained on one dataset or environment often fail to maintain performance when transferred to another, underscoring the need for methods that can adapt effectively. Achieving this adaptability demands architectures that are both flexible and computationally efficient, ensuring reliable performance even in dynamic, real-world contexts where training and testing conditions may differ substantially [11, 12, 13, 14]. Advancements in deep learning, including CNNs, LSTMs, and Transformer-based architectures, have significantly improved the accuracy of motion recognition using WiFi sensing [15, 16]. However, these models often suffer from large parameter counts and high computational complexity, posing challenges in resource-constrained environments such as embedded systems and mobile devices [17, 18]. To address these limitations, model compression techniques--such as pruning, quantization, and knowledge distillation--have been explored to reduce inference time and energy consumption while maintaining performance [19, 20, 21, 22]. Automated machine learning (AutoML) and Neural Architecture Search (NAS) have significantly advanced the discovery of neural architectures that achieve an optimal balance between accuracy and efficiency [23]. By systematically searching through the architectural design space, these methods enable researchers to develop models that not only excel in capturing CSI-derived features but also perform efficiently in practical, real-world applications. Many prior approaches, however, have relied on conventional, off-the-shelf network architectures [24], which often underutilize the potential for domain-specific optimization. Unlike prior applications of Bayesian Optimization-based NAS in domains such as gas/odor mixture classification, which utilize fixed Gaussian process priors for hyperparameter tuning over relatively simpler search spaces, our approach is tailored specifically for the complexities of WiFi-based activity recognition. By defining an expansive search space that includes architectural parameters such as convolutional layer configurations, LSTM and Transformer components, and optimizing for signal characteristics unique to CSI data, we address the challenges of real-world adaptability and efficiency in WiFi sensing tasks. The contributions of this study are as follows:  
* We conduct a comprehensive evaluation of the performance and limitations of pruning across four distinct CNN architectures specifically designed for WiFi-based motion recognition.