Hybrid Linear-Convolutional Neural Network for Timing Error Mitigation Towards 6G Deterministic Communications
We propose a timing error mitigation approach using hybrid linear-convolutional neural network towards <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$6 G$</tex> deterministic communications. Experiments demonstrate that the proposed method achieves a relative timing error of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pm 100 \mu s$</tex> in wireless networks.
Authors
- Xiangqun Lu
- Guochu Shou (ORCID: https://orcid.org/0000-0002-8271-0246)
- Yihong Hu (ORCID: https://orcid.org/0000-0001-5766-595X)
- Haotian Li (ORCID: https://orcid.org/0000-0002-5720-8078)
- Hongxing Li (ORCID: https://orcid.org/0000-0002-3286-1409)
- Lei Yang (ORCID: https://orcid.org/0000-0002-9039-8520)
- Qiang Gao (ORCID: https://orcid.org/0000-0001-5326-9626)
- Zhigang Guo (ORCID: https://orcid.org/0009-0000-7052-879X)
Institutions
- Beijing University of Posts and Telecommunications (CN)
Publication Details
- Published
- 2025-06-29
- DOI
- https://doi.org/10.23919/oecc/psc62146.2025.11110483
- Primary Topic
- Wireless Body Area Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Key Research and Development Program of China