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

• DOI: 10.23919/wiopt58741.2023.10349874
• Publication Date: 2023-12-22T19:18:56Z
• Authors: Chaoqun You; Xingqiu He; Jingyi Xu; Peng Yang; Tony Q. S. Quek
• Keywords: Resource Management

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 network, service-oriented RAN naturally consumes lot of energy, leading tradeoff between QoS guarantees and for network scheduler decide. In this paper, we propose sustainable (SSO) networks jointly optimize workload distribution allocation. The target minimize long-term average consumption using meta reinforcement learning (MRL) method. To be specific, each type treated independent optimization problem, where solved convex allocation solve Q-learning policy. Numerical results show that SSO effectively reduces system while satifying requirements, compared with benchmarks.