MA-GAN: Jamming Waveform Generation Based on Masked Self-Supervised Learning and Multiscale Feature Fusion

In open, noncooperative electromagnetic environments, prior information about target signals, such as their modulation types and symbol rates, is often difficult to obtain. Consequently, both conventional methods and explicitly label-dependent jamming waveform generation methods face substantial limitations in practical applications. To address this issue, we propose a Multiscale Aggregation Generative Adversarial Network with Masked Pre-training (MA-GAN), a communication jamming waveform generation method based on masked self-supervised learning and multiscale feature fusion. We construct a masked waveform modeling pre-training framework for In-phase/Quadrature (I/Q) time-series signals, through which the Masked Waveform Modeling encoder (MWM-encoder) learns hierarchical and multiscale representations from unlabeled data. The generator is guided by multiscale statistical features and combined with a multi-head multiscale discrimination mechanism and physical constraints to improve the structural consistency between the generated jamming waveforms and the target signals. Experimental results show that, under Additive White Gaussian Noise (AWGN), Rayleigh fading and Rician fading channels and across different Signal-to-Noise Ratio (SNR) and Jamming-to-Signal power Ratio (JSR) conditions, MA-GAN achieves effective jamming performance for five modulation types observed during training and five unseen modulation types, with performance approaching that of the Signal-Like method, which assumes complete prior information. These results indicate that MA-GAN can generate communication jamming waveforms with a certain degree of cross-modulation and robustness to the tested channel conditions without relying on explicit prior information.