Energy-Efficient Ultra-Dense Network Using LSTM-based Deep Neural Networks

Abstract

As a means to achieve thousand-fold throughput improvements of future wireless communications, ultra-dense network (UDN) where a large number of small cells are densely deployed on top of the macro cells has received great deal of attention in recent years. While UDN offers number of benefits, intensive deployment of small cells may pose a serious concern in the energy consumption. Over the years, to reduce the energy consumption of UDN, an approach that turns off the lightly loaded base stations (BSs) has been proposed. However, determining the proper on/off modes of BSs is a challenging problem due to the huge computational overhead and inefficiency caused by the delayed decision. An aim of this paper is to propose a deep neural network (DNN)-based framework to achieve reduction of energy consumption in UDN. By exploiting the long short-term memory (LSTM) to extract the temporally correlated features from the channel information and the feedforward network to make BS on/off mode decision, we can control the on/off modes of BSs, thereby achieving a considerable reduction of the cumulative energy consumption. From the extensive simulations, we demonstrate that the proposed technique is effective in reducing the energy consumption of UDN.