Traffic-Aware Predictive Energy Optimization for Control- and User-Plane Separation Systems

To support the explosive growth of wireless traffic, the emerging control-and user-plane separation (CUPS) paradigm allows control base stations (CBS) to provide control plane (CP) coverage, while traffic base stations (TBSs) catering to varying mobile traffic and diverse quality of service (QoS) requirements. However, learning-based traffic prediction has yet to utilize the potential advantages provided by CUPS to facilitate energy saving. In this paper, we introduce a novel base station (BS) sleep scheme for CUPS that employs a learning-based approach to determine the TBS’s active/deactive states. First, traffic demands of TBSs are predicted by a novel data-driven learning approach. This approach leverages the receptive fields to explore the spatial characteristics of mobile traffic, and then employs Bidirectional Long Short-Term Memory (Bi-LSTM) networks to extract context feature of mobile traffic. Based on the traffic forecasting, we formulate a long-term network energy efficiency maximization problem that optimizes the active/deactive states of TBSs. Moreover, we introduce a service penalty term into CP to mitigate potential network coverage vulnerabilities of TBS. Then, an improved twin delayed deep deterministic policy gradient (TD3)-based algorithm is employed to solve the above non-deterministic polynomial-time hard (NP-hard) problem. Extensive experiments using a real-world dataset demonstrate that the proposed scheme significantly outperforms the existing methods in terms of traffic forecasting accuracy. Additionally, the proposed energy efficiency maximization algorithm achieves superior performance than other benchmark schemes.