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.

中文翻译：

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使用基于 LSTM 的深度神经网络的节能超密集网络

作为实现未来无线通信千倍吞吐量提升的手段，在宏小区之上密集部署大量小小区的超密集网络（UDN）近年来受到了极大的关注。虽然 UDN 提供了许多好处，但小基站的密集部署可能会严重影响能源消耗。多年来，为了降低 UDN 的能耗，已经提出了一种关闭轻载基站 (BS) 的方法。然而，由于延迟决策导致巨大的计算开销和低效率，确定 BS 的适当开/关模式是一个具有挑战性的问题。本文的一个目的是提出一种基于深度神经网络 (DNN) 的框架，以实现 UDN 能耗的降低。通过利用长短期记忆（LSTM）从信道信息和前馈网络中提取时间相关特征来进行基站开/关模式决策，我们可以控制基站的开/关模式，从而实现可观的减少累计能耗。从广泛的模拟中，我们证明了所提出的技术在降低 UDN 的能耗方面是有效的。