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

• URL: http://arxiv.org/abs/2607.25070v1
• Date: Mon, 27 Jul 2026 21:00:24 GMT
• Title: Characterizing and Mitigating the Effects of Device Temperature on RF Fingerprinting Accuracy
• Abstract summary: Radio Frequency Fingerprinting (RFFP) has emerged as a promising approach for device authentication by exploiting hardware-specific impairments.<n>RFFP sensitivity to temperature can significantly alter device signatures and degrade classification performance.<n>We propose a novel temperature-aware RFFP framework that explicitly incorporates device temperature information into the learning process.
• Score: 3.065243326333468
• License: http://creativecommons.org/licenses/by/4.0/
• 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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