# Bluetooth Fingerprint Identification Under Domain Shift Through Transient Phase Derivative

arXiv: https://arxiv.org/abs/2510.09940 · HTML: https://mlsanity.com/paper/2510.09940/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2025.

## Built on by (most cited first, 1 of 1)

- [Characterizing and Mitigating the Effects of Device Temperature on RF Fingerprinting Accuracy](https://mlsanity.com/paper/2607.25070/) (2026)

## Abstract (arXiv)

Deep learning-based radio frequency fingerprinting (RFFP) has become an enabling physical-layer security technology, allowing device identification and authentication through received RF signals. This technology, however, faces significant challenges when it comes to adapting to domain variations, such as time, location, environment, receiver and channel. For Bluetooth Low Energy (BLE) devices, addressing these challenges is particularly crucial due to the BLE protocol's frequency-hopping nature. In this work, and for the first time, we investigated the frequency hopping effect on RFFP of BLE devices, and proposed a novel, low-cost, domain-adaptive feature extraction method. Our approach improves the classification accuracy by up to 58% across environments and up to 80% across receivers compared to existing benchmarks.