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

DOI

10.2991/978-94-6463-986-5_94How to use a DOI?

Keywords

Transformer; signal; multimodal noise cross-domain attribution analysis

Abstract

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.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jiaxuan Xu
PY  - 2026
DA  - 2026/02/18
TI  - Designing Cross-Domain Attribution Analysis into A Transformer for Enhancing 5G Under SNR Conditions
BT  - Proceedings of the 2025 International Conference on Electronics, Electrical and Grid Technology (ICEEGT 2025)
PB  - Atlantis Press
SP  - 917
EP  - 928
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-986-5_94
DO  - 10.2991/978-94-6463-986-5_94
ID  - Xu2026
ER  -