TY - GEN
T1 - Self-Adaptive and Robust 6G Network Architecture Integrating Native GPTs
AU - Yang, Zheng
AU - Zhang, Yuting
AU - Zeng, Jie
AU - Zhu, Chao
AU - Bu, Xiangyuan
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - 6G
KW - Endogenous Intelligence
KW - GPTs
KW - Network Architecture
KW - Network Security
UR - http://www.scopus.com/inward/record.url?scp=85198853868&partnerID=8YFLogxK
U2 - 10.1109/WCNC57260.2024.10571163
DO - 10.1109/WCNC57260.2024.10571163
M3 - Conference contribution
AN - SCOPUS:85198853868
T3 - IEEE Wireless Communications and Networking Conference, WCNC
BT - 2024 IEEE Wireless Communications and Networking Conference, WCNC 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 25th IEEE Wireless Communications and Networking Conference, WCNC 2024
Y2 - 21 April 2024 through 24 April 2024
ER -