MF-LSTM-based adaptive energy-saving system for base stations
基于MF-LSTM的基站自适应节能方案

摘要

随着5G基站大规模部署，能耗问题日益突出。传统节能方案存在业务流量时间波动性强，预测精度不足和异构基站场景下节能策略泛化能力差的问题，难以适应动态业务需求，易导致网络服务质量下降。为此，本文提出基于多特征长短期记忆神经网络（MF-LSTM）的基站自适应节能方案，利用MF-LSTM融合多维特征，提升预测准确性；通过K-means聚类算法划分基站能耗模式，动态匹配最优节能策略。实验表明，MF-LSTM可解释92%以上的流量波动，基站自适应节能策略可降低11.71%的基站能耗，为绿色通信提供更有效的方法。

Abstract

With the large-scale deployment of 5G base stations, energy consumption issues have become increasingly prominent. Traditional energy-saving solutions suffer from strong temporal fluctuations in service traffic, insufficient prediction accuracy, and poor generalization of energy-saving strategies in heterogeneous base station scenarios, making them difficult to adapt to dynamic service demands and prone to degrading network service quality. To address this, this paper proposes an adaptive base station energy-saving system based on Multi-Feature Long Short-Term Memory neural networks (MF-LSTM) and K-means clustering. The MF-LSTM integrates multi-dimensional features integrates temporal features (weekday/hour) and traffic data to enhance prediction accuracy, while K-means clustering categorizes base station energy consumption patterns to dynamically match optimal energy-saving strategies. Experimental results demonstrate that the MF-LSTM explains over 92% of traffic fluctuations, and the adaptive energy-saving strategy achieves an 11.71% reduction in base station energy consumption, providing a more effective approach for green communication.