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Dynamic Adaptation RFF Identification Method Leveraging Cognitive Representation Learning
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AI
DOI：10.1109/TIFS.2024.3451710.png)
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Abstract
En 中文
The evolution of wireless communication technologies has brought significant conveniences but also raised security concerns. Radio frequency fingerprint (RFF) is a potential feature, which can uniquely identify a specific emitter. The integration of Deep Learning (DL) has further enhanced the reliability of RFF identification. However, DL methods often struggle in dynamic communication environments. In this paper, we propose a dynamic adaptive RFF identification method leveraging Cognitive Representation Learning (CRL). Our proposed method is capable of recognizing and storing cognitive knowledge from historical environments. Furthermore, it dynamically adapts to current situations through its cognitive module, offering enhanced adaptability in dynamic environments. Specifically, we analyze the causes of RFF and define the RFF identification problems at first. Secondly, our cognitive module evaluates current data by examining both data distribution and feature distribution distances. Concurrently, our representation learning strategy enhances feature reuse and focuses on feature space. Finally, we implement an unsupervised ensemble module, combining unsupervised clustering with model ensemble techniques to boost performance. Simulation results validate our method's robust generalization in dynamic settings, with an improvement of 7.66% in controlled environments and 5.98% in more challenging scenarios on PA dataset. Furthermore, the high identification ratio and ablation study results underscore the efficacy and necessity of each module in our approach.
Keywords:
Feature extraction
Task analysis
Wireless communication
Transfer learning
Representation learning
Object recognition
Internet of Things
Radio frequency fingerprint
deep learning
ensemble learning
supervised contrastive learning
representation learning
physical layer security
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