This article presents a comprehensive analysis of state-of-the-art models and methods for wireless device identification based on their unique radio frequency characteristics (RF-fingerprinting). The fundamental principles of RF fingerprint formation, which arise from the intrinsic hardware imperfections of transmitter components, are examined in detail. The study analyzes the primary sources of signal uniqueness, including variations in oscillator parameters, power amplifier behavior, modulator nonlinearities, and antenna system characteristics. Existing RF-fingerprinting approaches are systematized according to feature extraction methodology, applied classification algorithms, and practical application domains. Special attention is devoted to signal processing techniques in the time, frequency, and time–frequency domains, including transient behavior analysis, spectral feature characterization, and wavelet transforms. The article provides a structured classification of machine learning algorithms used for device identification, ranging from traditional statistical models to advanced deep learning architectures. Hybrid approaches that combine multiple methodologies to improve iden
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