An artificial neural network based cell switch-off algorithm in cellular system
An artificial neural network based cell switch-off algorithm in cellular system
复制标题
蜂窝系统中基于人工神经网络的小区关闭算法
DOI：
10.1109/compcomm.2016.7924940
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Jia
中科院分区：
文献类型：
--
作者：
Ruhong Zeng;Shixiang Zhu;Hongwen Yang;Jia
随着无线设备使用的增加，蜂窝网络的功耗和能量效率近年来在无线网络中受到了广泛关注。节省能量的一种方式是在轻业务条件下关闭一些未充分利用的小小区。在现有的研究中，网络中可以关闭的基站（BS）的数量作为优化目标。然而，在许多情况下，如果能量效率随着更少的电池关闭而增加，则是可接受的。在本文中，我们以能量效率为优化目标，并假设每个用户设备（UE）有一个最小的速率要求和带宽是均匀共享的UE在一个小区。在这种假设下，优化小区关闭以获得最大能量效率的问题仍然是NP困难的。我们提出了一种基于人工神经网络的小小区关闭算法，然后在全向小区部署的翻转方法，提供了一个很好的结果与线性复杂度在线系统和服务质量（QoS）的UE可以得到满足。通过系统级仿真的数值结果。
With the increasing usage of wireless devices, the power consumption and energy efficiency of cellular networks have been receiving much attention in wireless network in recent years. One way of saving energy is to switch off some underutilized small cells in light traffic conditions. In existing researches, the number of the base stations (BSs) that can be switched off in a network is take as the the optimization object. However, under many circumstances, it would be acceptable if the energy efficiency increases with less cells switched off. In this paper, we take the energy efficiency as the optimization object and suppose that each user equipment (UE) has a minimum rate requirement and the bandwith is uniformly shared by UEs in a cell. Under this assumption, the problem of optimizing cell switch-off for maximum energy efficiency is still NP-hard. We present an artificial neural network based small cell switch-off algorithm followed by flip-over method in omnidirectional cell deployment which offers a good result with linear complexity in on-line system and the Quality of Service (QoS) of the UEs can be met. Numerical results are presented through system level simulations.