부산대학교 도서관

The increasing network traffic and dynamic user connections have posed challenges for cellular operators in reducing operating costs while ensuring the quality of service (QoS) for users. Cloud radio access network (C-RAN) addresses these issues by separating baseband units (BBUs) and remote radio heads (RRHs), creating a centralized BBU pool. To optimize C-RAN performance, the key is to dynamically assigning RRHs to BBUs, which is challenging due to cost and QoS constraints. In this paper, we propose a data-driven RRH-BBU mapping scheme (KC-A3C) with deep reinforcement learning (DRL) to improve the performance of large-scale C-RANs. First, we analyze a dataset from a cellular operator containing approximately 26,652 active base stations and use the features of the dataset to construct an RRH popularity metric to cluster RRHs. Second, we model the RRH-BBU mapping as a Markov decision process and use the synchronous Advantage Actor-Critic (A3C) algorithm to find the optimal mapping scheme with the highest long-term gain in a dynamic environment, considering resource utilization, RRH migration, and BBU load balancing. Evaluations using real-world datasets show that our proposed scheme outperforms baseline methods.