Characterizing and Mitigating the Effects of Device Temperature on RF Fingerprinting Accuracy

arXiv:2607.25070 · eess.SP, cs.CR, cs.LG · Submitted 2026-07-27 · Read on arXiv

Haytham Albousayri, Bechir Hamdaoui

eess.SP, cs.CR, cs.LG

Submitted: 2026-07-27

Comments: 6 pages, 11 figures. Accepted at 17th International Conference on Network of the Future (NoF2026)

License: http://creativecommons.org/licenses/by/4.0/

The gist: Radio Frequency Fingerprinting (RFFP) has emerged as a promising approach for device authentication by exploiting hardware-specific impairments embedded in transmitted signals.

Terminology

Abstract

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 (BLE) dataset collected across multiple devices and environmental conditions. Experimental results demonstrate that temperature-aware modeling consistently outperforms other temperature mitigation baselines, achieving significant improvements in classification accuracy, particularly under unseen temperature and environmental conditions.

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