Characterizing and Mitigating the Effects of Device Temperature on RF Fingerprinting Accuracy
Radio Frequency Fingerprinting (RFFP) has emerged as a promising approach for device authentication by exploiting hardware-specific impairments embedded in transmitted signals. Yet existing methods largely overlook a major drawback: RFFP sensitivity to temperature--a critical factor influenced by both internal and environmental conditions--which can significantly alter device signatures and degrade classification performance. In this paper, we propose a novel temperature-aware RFFP framework that explicitly incorporates device temperature information into the learning process to improve robustness and generalization. We evaluate the proposed method on a real-world Bluetooth Low Energy (BL...
| Authors | Haytham Albousayri, Bechir Hamdaoui |
|---|---|
| Primary category | eess.SP |
| Published | 27 July 2026 |
| Last updated at source | 27 July 2026 |
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