MADRL-Enhanced Secure RAN Slicing in 5G and Beyond Multi-Cell Uplink Communication Systems

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.