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

Adaptive Dynamic Programming for Energy-Efficient Base Station Cell Switching