Radio Resource Allocation Method for Network Slicing using Deep Reinforcement Learning
Yu Abiko, Takato Saito, Daizo Ikeda, K. Ohta, Tadanori Mizuno, Hiroshi Mineno
Abstract
The 5th generation of mobile communications is expected to provide a network suitable for any service requirements using a technology termed network slicing. In network slicing, network resources are divided and are allocated to slices for services. Radio access network slicing is essential for end-to-end network slicing. The state of the radio access network changes from moment to moment and automatic control of network slicing is necessary to respond to service requirements in real time. Automatic control is applied using deep reinforcement learning that can learn optimal control by repeating trial and error. In this paper, we propose a method to allocate radio resources that satisfy the service requirements regardless of the number of slices using deep reinforcement learning. The proposed method flexibly controls multiple slices by observing the state of one slice and calling a model that controls one slice multiple times. The proposed method was evaluated by simulation using a scenario in which the number of slices changes with time. The results of the evaluation show that variations in slice numbers achieve almost completely satisfying of slice requirements without being affected by other slices.