Anti-jamming is the core issue of wireless communication viability in complex electromagnetic environments, where jamming recognition is the precondition and foundation of cognitive anti-jamming. In the current jamming recognition methods, the existing convolutional networks are limited by the small number of layers and the extracted feature information. Simultaneously, simple stacking of layers will lead to the disappearance of gradients and the decrease in correct recognition rate. Meanwhile, most of the jamming recognition methods use single-node methods, which are easily affected by the channel and have a low recognition rate under the low jamming-to-signal ratio (JSR). To solve these problems, a multi-node cooperative jamming recognition method based on deep residual networks was proposed in this paper, and two data fusion algorithms based on hard fusion and soft fusion for jamming recognition were designed. Simulation results show that the use of deep residual networks to replace the original shallow CNN network structure can gain a 6–14% improvement in the correct recognition rate of jamming signals, and the hard and soft fusion-based methods can significantly improve the correct jamming recognition rate by about 3–7% and 5–12%, respectively, under low JSR conditions compared with the existing single-node method.

Traditional anti-jamming techniques such as frequency hopping (FH) and direction-sequence spread spectrum (DSSS) are easily targeted by jammers. Inspired by the significant advantages of reconfigurable intelligent surfaces (RIS), and in order to overcome “double fading”, controllable multiple active RISs are proposed to explore anti-jamming communication in this paper. To verify the feasibility of active RIS, the anti-jamming performance of active RIS is analyzed through theoretical derivation and simulation and compared with passive RIS. Furthermore, to solve the optimization problem of active RIS, a controllable multi-active RIS assisted anti-jamming algorithm based on BCD is proposed. Theoretical analysis and simulation results show that in small-scale deployment scenarios of RIS, the anti-jamming performance of active RIS is better than that of passive RIS, and the complexity and optimization performance of the proposed algorithm are better than those of semidefinite relaxation (SDR) algorithms.

To address the difficulty in obtaining a large number of labeled jamming signals in complex electromagnetic environments, this paper proposes a small-sample communication jamming signal recognition method based on WDCGAN-SA (Wasserstein Deep Convolution Generative Adversarial Network–Self Attention) and C-ResNet (Convolution Block Attention Module–Residual Network). Firstly, leveraging the DCGAN architecture, we integrate the Wasserstein distance measurement and gradient penalty mechanism to design the jamming signal generation model WDCGAN for data augmentation. Secondly, we introduce a self-attention mechanism to make the generation model focus on global correlation features in time–frequency maps while optimizing training strategies to enhance the quality of generated samples. Finally, real samples are mixed with generated samples and fed into the classification network, incorporating cross-channel and spatial information in the classification network to improve jamming signal recognition rates. The simulation results demonstrate that under small-sample conditions with a Jamming-to-Noise Ratio (JNR) ranging from −10 dB to 10 dB, the proposed algorithm significantly outperforms GAN, WGAN and DCGAN comparative algorithms in recognizing six types of communication jamming signals.

With the advancement of wireless communication technologies, especially the emergence of mobile communication technologies such as satellite internet and sensor networks, the rapid proliferation of communication facilities has given rise to challenges such as the scarcity of spectrum bandwidth resources, heightened channel interference, and increased noise. Consequently, traditional image source coding technologies urgently require further improvements in their compression ratio and anti-interference capability. Targeting image transmission scenarios characterized by low signal-to-noise ratios and constrained channel bandwidths, this paper proposes an image semantic coding method based on the pre-trained Stable Diffusion model, producing a zero-shot universal image compressor. This compressor leverages the denoising network of the Stable Diffusion model, with feedback from channel SNR, to further enhance the adaptability of transmitted data to channel interference. Additionally, by designing quantization and entropy coding methods for feature tensors in the semantic space, the compression ratio of the image coding process is further improved. Simulation results demonstrate that the proposed method not only achieves superior compression performance but also ensures relatively high similarity between the decoded reconstructed image and the original. Notably, it delivers a significant improvement in the perceptual similarity of human visual quality. Furthermore, the method can adapt to Gaussian noise channels, Rician fading channels, and Rayleigh fading channels with low SNR, exhibiting broad application prospects in the field of wireless communication coding methods, where the electromagnetic environment is growing increasingly complex.