Communication signal generation is fundamental for designing and optimizing intelligent communication systems under few-shot conditions. Existing methods perform well in static environments but struggle to capture rapidly evolving spatio-temporal features in dynamic scenarios, leading to reduced consistency and accuracy. To address this, this study proposes a Transformer-based diffusion model, named TransDiffusion, for high-dynamic communication signal generation. TransDiffusion integrates Transformer architecture into the diffusion framework and enhances long-range dependency modeling through embedded multi-level spatio-temporal attention. The noise prediction network is also optimized for time-varying environmental characteristics. Experiments on a custom high-dynamic simulation dataset that emulates multi-target motion and RF signatures in urban traffic show that TransDiffusion significantly outperforms the RF-Diffusion baseline: MME, MSE, and MAE are reduced by 85.13%, 40.92%, and 30.62%, while STFT-sim and Spectral-sim increase by 154.30% and 5.28%, respectively. This demonstrates the effectiveness of the proposed method in reconstructing high-fidelity communication signals in dynamic scenarios.