Meta-Scheduling Framework With Cooperative Learning Toward Beyond 5G

In this paper, we propose a novel meta-scheduling framework with cooperative learning that fully exploits a functional split structure of the base station (BS) consisting of a central unit (CU) and digital units (DUs). To this end, we first design a meta-scheduling policy structure to find a scheduling strategy that can be applied to any BS regardless of BS-specific characteristics such as the number of users, various quality-of-service requirements, and the number of resource blocks. In the proposed framework, the CU only needs to manage objective-specific meta-scheduling policies in a centralized manner while each DU performs scheduling in a decentralized manner by simply using the policy at the CU without any responsibility to manage or train policies. Besides, the policies at the CU can be cooperatively trained using the experiences obtained from all DUs. Therefore, the proposed framework not only provides computational efficiency but also supports network scalability. We show via experiments that the proposed meta-scheduling framework achieves competitive performances compared with the near-optimal conventional schedulers tailored to each BS even though it manages and exploits only one meta-scheduling policy for each objective. Furthermore, our meta-scheduling framework effectively adapts to the change of BS-specific characteristics.