Multi-Domain Computation-Aware Resource Slicing and Orchestration for 6G Programmable Converged Wireless-Optical Networks

Six-generation mobile systems aim to support stringent end-to-end service-level agreements for diverse user applications simultaneously. This paper introduces novel multi-domain computation-aware resource slicing orchestration that jointly manages in-network communications, computation, and caching storage resources for multi-domain networking. It automatically programs wireless access, edge cloud, and regional/central cloud infrastructure to enable wireless-optical network virtualization. Specifically, a mobile virtual network operator's long-term profit maximization problem and two subproblems are formulated to slice wired and wireless infrastructure resources and assign user requests and contents to slices. Accordingly, a reinforcement learning-based slicing with greedy pre-caching is proposed, which automatically allocates in-network resources for dynamic wireless connectivity and supports real-time inferring with minimal user request signaling. Numerical results show that our solutions provide superior performance from both user and infrastructure perspectives, with 20% improved operator profits, 17% enhanced service provisioning rates, and 80% reduced delay when simultaneously serving augmented reality and large language model's quality demands. This innovation exploits a generalized rein-forcement learning approach to minimize the signaling overheads and computation complexity while agilely adapting to practical converged networks, thus benchmarking AI-driven multi-domain network slicing development.