Joint Traffic Prediction and Base Station Sleeping for Energy Saving in Cellular Networks

Densely deployed base station (BS) network is one of the important technologies for 5G and beyond mobile communication system, which improves the system throughput by deploying a large number of BSs in the service area. However, such a mobile network has to deal with the consequent power issue since the energy consumption of the BSs generally accounts for a substantial part of the whole system. In this paper, we propose an intelligent BS sleeping scheme to reduce the system energy consumption as much as possible with reasonable signaling overhead while guaranteeing the quality of experience of users. First, we introduce a long short term memory learning method to forecast the traffic distribution in the service area, by which we can determine when the BS sleeping operation is triggered; second, we develop an efficient three-step procedure to determine which of the BSs would sleep or be wakened. Experiment results show that our proposed traffic prediction method works quite well in practical scenarios. The prediction error is not more than 10%, and the energy consumption decreases more than 40% in average for a commercial mobile network with 20 BSs.