Energy-Efficient Edge-Domain Automation and Service Provision in 6G Networks by Deploying Offline Discovered Assignment Skills

Although the next generations of wireless networks are anticipated to be enabled by Artificial Intelligence (AI), the development of practical, scalable, and efficient system models and network orchestration strategies remains a significant open challenge that requires thorough investigation. In this research work, we study a system model including two parts of AI-driven management and orchestration and AI-enabled infrastructures consisting of multiple automated domains. While the network orchestration manages the autonomous domains, the intra-domain resource allocation and network slicing are also automated and AI-enabled. Consequently, we propose an efficient and scalable strategy for automating network edge domains. This approach reduces the complexity and, thus, increases the scalability and energy efficiency by implementing one Double Deep Q-Learningbased controller in each domain that can perform network slicing for multiple service types. One agent provides dynamic network services by utilizing unsupervised discovered assignment skills in an offline phase. We implement the domain controller with multiple algorithms to find the best performance and efficiency. Finally, we compare the reliability of the algorithms, and we select the algorithm that offers the best trade-off between complexity and performance.