Guolin Sun
University of Electronic Science and Technology of China, China
Title: Resource slicing and customization in 5G RAN with dueling deep Q-Network
Biography
Biography: Guolin Sun
Abstract
The emerging future generation 5G technology is expected to support service-oriented virtualized networks where different network applications provide unique services. 5G networks have the potential to allow completely different slices to co-exist in a substrate network and satisfy the differentiated requirements of various users. In networks with heterogeneous traffics, operators are required to provide services in isolation since each operator has its own defined performance requirements. However, achieving an efficient resource provisioning mechanism for such traffics is very challenging. We proposes a coarse resource provisioning scheme and a dynamic resource slicing refinement scheme based on dueling deep reinforcement learning for virtualized radio access network. Then, a shape-based resource allocation algorithm is proposed to customize the diverse requirements of users to improve user satisfaction and resource utilization. The results reveal that the proposed algorithm balances satisfaction and resource utilization with 80% of the available resources. The algorithm also provides performance isolation such that, a sudden change in user population in one slice does not affect the others.