Sustainable Service-Oriented RAN Slicing for AI-Native 6G Networks

Energy saving plays an important role in designing AI-native 6G networks. Radio Access Network (RAN) slicing is a fundamental tool to save energy through resource multiplexing. However, as the AI services required by users become more heterogenous than ever in 6G network, service-oriented RAN slicing naturally consumes a lot of energy, leading to a tradeoff between QoS guarantees and energy saving for the network scheduler to decide. In this paper, we propose sustainable service-oriented (SSO) RAN slicing scheduler for 6G networks to jointly optimize workload distribution and resource allocation. The target is to minimize the long-term average energy consumption using the meta reinforcement learning (MRL) method. To be specific, each type of services is treated as an independent optimization problem, where the workload distribution is solved by convex optimization and the resource allocation is solve by Q-learning policy. Numerical results show that SSO effectively reduces the system energy consumption while satifying QoS requirements, as compared with benchmarks.