Adaptive Dynamic Programming for Energy-Efficient Base Station Cell Switching
This work addresses energy efficiency in wireless networks for operators and environmental concerns, but it is incremental as it builds on existing dynamic programming and neural network techniques.
The paper tackles the problem of reducing energy consumption in cellular networks by proposing an approximate dynamic programming method with online optimization to switch base station cells on/off, achieving power savings while maintaining Quality of Service (QoS) metrics, as evaluated in real-world scenarios.
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.