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.