MA-GAN: How Self-Supervised Learning Transforms Electronic Warfare Without Prior Signal Knowledge

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The electromagnetic spectrum has become a crowded and contested domain. Electronic countermeasures have moved far beyond simple noise generation. They now require sophisticated intelligence to disrupt signals effectively. A new framework called MA-GAN offers a compelling solution for this challenge. It generates high-fidelity jamming waveforms without needing prior knowledge of the target signals. This approach addresses critical limitations found in traditional methods. It is particularly useful in non-cooperative environments where modulation types and symbol rates are unknown or change rapidly.

Traditional jamming techniques often rely on extensive prior information about the target communication link. Suppressive jamming consumes significant energy by flooding the channel with noise. Deceptive jamming requires structural similarity to target signals. Achieving this similarity is difficult without detailed parameter knowledge. Recent deep learning approaches have attempted to automate waveform generation. Conditional Generative Adversarial Networks show promise but suffer from poor generalization. These models struggle with mode collapse when handling multiple modulation types. They also fail to adapt to unseen signals or varying symbol rates. This limits their practical utility in dynamic open-spectrum scenarios.

MA-GAN overcomes these challenges by abandoning label-dependent conditioning. It employs a masked self-supervised learning strategy inspired by Masked Autoencoders and SimMIM from computer vision. The core of the system is a MWM-encoder that processes one-dimensional I/Q time-series data. By randomly masking segments of the input signal, the encoder learns to reconstruct missing portions using local-window attention mechanisms. This process helps the model learn robust hierarchical feature representations. These features capture both coarse-scale global statistics like spectral envelopes and fine-scale local details such as instantaneous constellation structures.

During the adversarial training phase, MA-GAN utilizes multiscale feature fusion to guide the generator. The pre-trained encoder extracts features at four different scales from the target signal. These features are then fused and injected into the generator alongside random noise. This ensures the generated jamming waveform matches the target across multiple dimensions. A multi-head discriminator evaluates the authenticity of the generated signal at each scale. This provides granular feedback to improve quality. The framework also incorporates explicit constraints for spectral consistency and phase continuity. These constraints prevent unnatural phase discontinuities that could reveal the signal as artificial to advanced receivers.

Experimental results demonstrate that MA-GAN significantly outperforms existing methods. It shows superior performance compared to WGAN-GP, DC-GAN, and TFC-GAN. Metrics such as Power Spectral Density correlation and Autocorrelation Function similarity highlight its effectiveness. Crucially, MA-GAN exhibits strong generalization capabilities. It maintains effective jamming performance against both known and previously unseen modulation types. This holds true under various channel conditions including AWGN, Rayleigh fading, and Rician fading. At high Jamming-to-Signal Ratios, its Bit Error Rate impact approaches that of an ideal Signal-Like jammer. This capability marks a significant step forward in adaptive electronic warfare. It enables real-time autonomous jamming in complex unpredictable electromagnetic environments.