TY - GEN
T1 - MADRL-Enhanced Secure RAN Slicing in 5G and Beyond Multi-Cell Uplink Communication Systems
AU - Sun, Yuanyuan
AU - Shi, Zhenjiang
AU - Wang, Jiadai
AU - Liu, Jiajia
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 5G and beyond are required to support a variety of emerging services that impose different requirements on the network. Radio Access Network (RAN) slicing is a promising candidate technology, and resource allocation issues related to it have received extensive attention. However, most existing literature on RAN slicing resource allocation either ignores actual network interference or focuses primarily on the QoS of slices but not slicing security. Towards this end, we propose a QoS and security-oriented slicing resource allocation scheme in a multi-cell and multi-slice scenario, where actual link interference is carefully considered. We then formulate a problem of maximizing security (i.e., isolation rate) subject to QoS (i.e., satisfaction rate) constraint. Finally, a multi-agent deep reinforcement learning-based solution is designed to solve this problem, and extensive numerical results demonstrate the superior performance of the proposed scheme.
AB - 5G and beyond are required to support a variety of emerging services that impose different requirements on the network. Radio Access Network (RAN) slicing is a promising candidate technology, and resource allocation issues related to it have received extensive attention. However, most existing literature on RAN slicing resource allocation either ignores actual network interference or focuses primarily on the QoS of slices but not slicing security. Towards this end, we propose a QoS and security-oriented slicing resource allocation scheme in a multi-cell and multi-slice scenario, where actual link interference is carefully considered. We then formulate a problem of maximizing security (i.e., isolation rate) subject to QoS (i.e., satisfaction rate) constraint. Finally, a multi-agent deep reinforcement learning-based solution is designed to solve this problem, and extensive numerical results demonstrate the superior performance of the proposed scheme.
KW - multi-agent deep reinforcement learning
KW - Radio Access Network (RAN) slicing
KW - RAN slicing resource allocation
KW - RAN slicing security
UR - http://www.scopus.com/inward/record.url?scp=85187358717&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM54140.2023.10437722
DO - 10.1109/GLOBECOM54140.2023.10437722
M3 - 会议稿件
AN - SCOPUS:85187358717
T3 - Proceedings - IEEE Global Communications Conference, GLOBECOM
SP - 2439
EP - 2444
BT - GLOBECOM 2023 - 2023 IEEE Global Communications Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE Global Communications Conference, GLOBECOM 2023
Y2 - 4 December 2023 through 8 December 2023
ER -