Self-Adaptive and Robust 6G Network Architecture Integrating Native GPTs

Zheng Yang, Yuting Zhang, Jie Zeng, Chao Zhu, Xiangyuan Bu

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

The emergence of generative pre-trained transform-ers (GPTs) will thoroughly change the application of sixth generation mobile communications (6G) networks. Therefore, it is necessary to design new network architectures to support ubiq-uitous deployment and real-time applications of GPTs. Aiming to integrate GPTs and the 6G network, this paper investigates the typical application scenarios of 6G+GPTs and summarizes the requirements of network key performance indicators (KPIs). Then, to address the complex and dynamically changing commu-nication environment, a self-adaptive 6G network architecture is proposed based on autonomous learning and self-optimization. Additionally, a novel mechanism based on attack samples is studied to improve the security of applying GPTs in 6G networks. Finally, we demonstrate that the proposed network architecture and security mechanism can satisfy the KPIs and improve robustness effectively. Overall, this paper provides a theoretical basis for the support of native GPTs with a novel 6G network architecture.

源语言英语
主期刊名2024 IEEE Wireless Communications and Networking Conference, WCNC 2024 - Proceedings
出版商Institute of Electrical and Electronics Engineers Inc.
ISBN(电子版)9798350303582
DOI
出版状态已出版 - 2024
活动25th IEEE Wireless Communications and Networking Conference, WCNC 2024 - Dubai, 阿拉伯联合酋长国
期限: 21 4月 202424 4月 2024

出版系列

姓名IEEE Wireless Communications and Networking Conference, WCNC
ISSN(印刷版)1525-3511

会议

会议25th IEEE Wireless Communications and Networking Conference, WCNC 2024
国家/地区阿拉伯联合酋长国
Dubai
时期21/04/2424/04/24

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