Multi-Variate Time Series Prediction of Traffic and Users for Dynamic RRH-BBU Mapping in C-RAN

Cellular operators face significant challenges in cutting operating expenses while maintaining the quality of service (QoS) for users due to growing network traffic and dynamic user connections. These challenges are addressed by the cloud radio access network (C-RAN) architecture, which includes a centralized pool of baseband units (BBUs) and distributes them from remote radio heads (RRHs). The key to improving C-RAN performance is to dynamically allocate large-scale RRHs to different BBUs in real time. In this paper, we propose a user behavior-aware RRH-BBU mapping framework to improve the performance of large-scale C-RANs by predicting RRH traffic and users in advance. First, we propose a Multivariate RRH time series Prediction Model (MRPM) that captures the spatio-temporal patterns in the data to predict the traffic volume and the number of users of RRHs, which represents key indicators of RRH connection states. Second, we formulate the RRH-BBU mapping as a Markov decision process problem to optimize cost and QoS by considering BBU utilization, BBU energy consumption, RRH migration frequency, and BBU load balancing. Third, we propose a prediction-based RRH-BBU mapping scheme (PB-RBM) to find the optimal RRH-BBU mapping strategy by leveraging the prediction information of MRPM. In the PB-RBM algorithm, we employ an A3C algorithm to learn the mapping policy and group the RRHs based on a defined popularity metric to reduce the state and action space of the reinforcement learning algorithm. Finally, extensive experiments are conducted on a real-world dataset, and our algorithm is compared with several matching algorithms, such as ACKTR, heuristic, etc., to demonstrate its superiority, especially reducing 17.5% in RMSE compared to the best-performing baseline.