A Channel Robust RFF Extraction Method Based on 802.11N HT-MF Preamble

In the research of Wi-Fi device identification, Radio Frequency Fingerprint (RFF) identification leverages the inherent hardware characteristics of devices to establish a unique identity at the physical layer. However, RFF identification is vulnerable to variations in the wireless channel. Although several studies have proposed channel-robust RFF extraction methods based on Wi-Fi signal preambles, these approaches rely on the legacy preamble of the 802.11 protocol, which results in relatively limited feature dimensions. This paper introduces the High Throughput Mixed Format (HT-MF) preamble under the 802.11n protocol framework, removing channel effects by calculating the ratio of three independent symbols in the frequency domain and subsequently extracting 24-dimensional RF fingerprint features. Maximum relevance minimum redundancy (MRMR) feature selection algorithm is then applied to remove redundant features and retain the most discriminative ones for the final RFF. This paper analytically shows that the three symbols in the HTMF preamble exhibit similar channel response characteristics within the channel's coherence time. We conducted extensive experiments on eight Wi-Fi devices across multiple channel environments, including four static and one dynamic scenario. Compared to methods without channel effect mitigation, our approach significantly improves channel robustness, achieving a 45.2% increase in average recognition accuracy. Additionally, when compared to other channel mitigation methods, our approach shows superior performance, with a maximum recognition accuracy improvement of 20.2%.