Heuristic-Assisted MADRL-Based Resource Allocation Scheme for QoS-Security Tradeoff in RAN Slicing With User Mobility

In the context of 5G and beyond 5G, radio access network (RAN) slicing emerges to enable differentiated services via the instantiation of virtualized logical networks. Despite its promising potential, the resource optimization of RAN slicing confronts significant challenges stemming from the scarcity of spectrum resources, the intricacies of tradeoff between slice service quality and slice security, the mobility of users, and the complexity of wireless interference in multi-cell environments. To address these challenges, we propose a heuristic-assisted multi-agent deep reinforcement learning-based resource allocation scheme for RAN slicing. This scheme aims to augment inter-slice resource isolation for security (quantified as isolation rate) while efficiently accommodating diverse requirements across slices (quantified as satisfaction rate). Through extensive numerical results, we exhibit that our proposed scheme adeptly adapts to multiple user mobility patterns, achieving superior performances in terms of satisfaction rate and isolation rate.