Transmitter Identification via Volterra Series-Based Radio Frequency Fingerprint

Radio Frequency Fingerprint (RFF), a physical-layer authentication technique that requires no cryptographic pairing and exhibits inherent resistance to cloning or spoofing, has emerged as a promising solution for device authentication in the Internet of Things (IoT) and unmanned systems. However, practical RFF authentication depends on extracting device-specific hardware signatures from received waveforms in a physically explainable manner. Existing deep-learning-based methods often provide limited interpretability, whereas feature-specific methods usually capture only a small subset of handcrafted impairments. This motivates a more general, hardware-oriented RFF representation. In this paper, we construct such a representation by using the received signal together with its corresponding ideal reference waveform to estimate the transmitter’s linear and nonlinear behavioral features. The estimated behavior features are then used as the radio frequency fingerprint for device classification and identification. Under a fixed receiver and a standard preprocessing pipeline (timing alignment, CFO compensation, and amplitude normalization), we represent this transmitter-dependent behavior with a Volterra series: the zeroth-order term accounts for the constant offset, the first-order kernel describes the equivalent linear response with memory, and the higher-order kernels describe nonlinear interactions among delayed signal samples. To obtain a compact representation, we approximate the kernels using wavelet bases and estimate the corresponding coefficients via regularized least-squares fitting. The magnitudes of the fitted coefficients are then classified using a lightweight convolutional neural network. Experiments on a public LoRa dataset achieve 99.49% mean accuracy in the static-channel setting and above 90% mean accuracy under combined multipath and Doppler impairments. Compared with recent RFF methods, the proposed approach provides competitive accuracy and substantially lower complexity than most deep-learning baselines, while offering a more physically interpretable representation of transmitter behavior. We further validate dataset-level generalization on a self-constructed 16-device dataset. The implementation is publicly available at: https://github.com/thomas-smith123/RFFI.