A High-Order Modulation Signal Classification Method Based on a Fourier Analysis Network Integrated with an Attention Mechanism

In modern wireless communication and electromagnetic control, automatic modulation classification (AMC) of orthogonal frequency division multiplexing (OFDM) signals plays an important role. However, under Doppler frequency shift and complex multipath channel conditions, extracting discriminative features from high-order modulation signals and ensuring model interpretability remain challenging. To address these issues, this paper proposes a Fourier attention network (FAttNet), which combines an attention mechanism with a Fourier analysis network (FAN). Specifically, the method directly converts the input signal to the frequency domain using the FAN, thereby obtaining frequency features that reflect the periodic variations in amplitude and phase. A built-in attention mechanism then automatically calculates the weights for each frequency band, focusing on the most discriminative components. This approach improves both classification accuracy and model interpretability. Experimental validation was conducted via high-order modulation simulation using an RF testbed. The results show that under three different Doppler frequency shifts and complex multipath channel conditions, with a signal-to-noise ratio of 10 dB, the classification accuracy can reach 89.1%, 90.4% and 90%, all of which are superior to the current mainstream methods. The proposed approach offers practical value for dynamic spectrum access and signal security detection, and it makes important theoretical contributions to the application of deep learning in complex electromagnetic signal recognition.