Abstract: In existing Wi-Fi sensing methods,the channel state information(CSI) under high packet rates is typically required to guarantee the sensing performance,which imposes significant burdens on communication resources and energy consumption.In the scenario of low packet rate,the loss of high-frequency dynamic information severely degrades the sensing performance.Interpolation-based recovery methods often fail to reconstruct critical features.To address these challenges,this paper proposes ReGAN,a generalized CSI reconstruction framework tailored for low-packet-rate conditions.ReGAN integrates an irregular masking strategy with a context-aggregated generative adversarial network(GAN),and employs a four-term composite loss to jointly optimize reconstruction quality at multiple levels,including pixel,stripe,spectral,and semantic.Experimental results de-monstrate that,in CSI reconstruction tasks under low packet rate conditions,the action classification accuracy of ReGAN in downstream sensing tasks is only 2～3 percentage lower than that of the original high packet rate data,while the testing accuracy of multiple shallow statistical models exceeds 83%,indicating its strong capability in structural restoration and cross model generalization under low packet rate sensing scenarios.ReGAN effectively guarantees the sensing performance under limited transmission rates without increasing communication overhead,offering a practical and efficient solution for large-scale deployment of Wi-Fi activity recognition in edge scenarios.

Key words: Channel state information(CSI), Image inpainting, Generative adversarial network(GAN), Activity recognition