Designing Cross-Domain Attribution Analysis into A Transformer for Enhancing 5G Under SNR Conditions

The Transformer framework has recently been proven to be feasible for noise analysis in low Signal-to-Noise Ratio (SNR) environments.The signal is heavily overwhelmed by noise (the lower the Signal-to-Noise Ratio, the closer or even greater the noise energy becomes compared to the signal energy), making it difficult for traditional methods to distinguish between "useful signal features" and "noise features"; if multiple modalities are superimposed, noise can further confuse features through "cross-modal coupling".Optimizing the Transformer's multimodal noise cross-domain attribution analysis capability provides clear assistance for low SNR noise analysis.It is one of the key technical paths to address the challenges of "difficult source tracing, inaccurate quantification, and coarse denoising" in low SNR multimodal noise.This work presents four parts of the optimizations for multimodal noise cross-domain attribution analysis based on the basic algorithm, which can help with cross-domain attribution analysis and further enhance the capability of solving complex signals.