Energy saving in wireless networks is growing in importance due to increasing demand for evolving new-gen cellular networks, environmental and regulatory concerns, and potential energy crises arising from geopolitical tensions. In this work, we propose an approximate dynamic programming (ADP)-based method coupled with online optimization to switch on/off the cells of base stations to reduce network power consumption while maintaining adequate Quality of Service (QoS) metrics. We use a multilayer perceptron (MLP) given each state-action pair to predict the power consumption to approximate the value function in ADP for selecting the action with optimal expected power saved. To save the largest possible power consumption without deteriorating QoS, we include another MLP to predict QoS and a long short-term memory (LSTM) for predicting handovers, incorporated into an online optimization algorithm producing an adaptive QoS threshold for filtering cell switching actions based on the overall QoS history. The performance of the method is evaluated using a practical network simulator with various real-world scenarios with dynamic traffic patterns.

翻译：无线网络节能问题日益重要，这源于新一代蜂窝网络不断增长的需求、环境与监管方面的考量，以及地缘政治紧张局势可能引发的能源危机。本文提出一种基于近似动态规划（ADP）并结合在线优化的方法，用于控制基站小区的开关状态，以降低网络功耗，同时维持合理的服务质量（QoS）指标。我们采用多层感知机（MLP）对每个状态-动作对进行功耗预测，从而在ADP中近似价值函数，以选择具有最优预期节能量的动作。为在不降低QoS的前提下实现最大限度的节能，我们引入另一个MLP用于预测QoS，并采用长短期记忆网络（LSTM）预测切换事件，将其整合进在线优化算法中，基于整体QoS历史数据生成自适应QoS阈值，以筛选基站小区切换动作。通过基于实际网络仿真器的实验，在多种真实场景和动态流量模式下对所提方法的性能进行了评估。