AI-enabled wireless networks have emerged as vital components of future-generation wireless communication systems. Among various wireless architectures, Bluetooth Low Energy (BLE) networks have attracted significant research interest due to their widespread adoption and low cost. In these networks, deep learning (DL) models can be integrated directly into the physical-layer infrastructure to perform diverse tasks, including environment sensing, localization, radio frequency fingerprinting (RFFP), and signal denoising. While highly effective under their original training configurations, these DL models often fail significantly when deployed in unseen environments---such as different rooms, varying temperature conditions, or altered carrier frequencies. Addressing this limitation requires rigorous benchmarking of models and algorithms against unseen configurations to evaluate their generalization and robustness. To bridge this gap, this paper makes two key contributions: i) we introduce a comprehensive, large-scale wireless BLE dataset consisting of raw IQ transmissions from 31 distinct commercial devices captured across diverse days, locations, environments, temperatures, receiver hardware, and frequency channels; ii) we present a case study evaluating state-of-the-art techniques for domain-robust RFFP to advance real-world AI reliability.

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