ID 原文 译文
25785 小小区密集部署的异构网络是 5G 系统提升网络容量与数据速率的最有效的关键技术之一。 Dense heterogeneous networks ( DenseHetNets) is one of the most promising technologies to enhance the 5G network capacity．
25786 随着小小区的大量部署，小小区之间的同频干扰以及小小区基站( Small Cell Base Stations，SeNBs) 的能耗问题愈发显著。 However, with a large number of small cells, the problem of co-channel interference and energy efficiency of small cell base stations ( SeNBs) becomes more and more severe．
25787 为提高SeNBs 能效并降低小小区间的同频干扰，提出一种基于干扰贡献比( Interference Contribution Ｒatio，ICＲ) 的小小区开关算法，可以仅通过较少的用户设备( User Equipment，UE) 信息反馈量与测量过程实现有效的小小区开关操作。 In order to improve the power efficiency and alleviate the co-channel interference, small cells need to be switched on or off accordingly. This paper proposes a SeNB switching on/off algorithm based on the interference contribution rates of the small cells that involve less signaling information in the procedure of switching on/off operation and require less computational complexity under the dense HetNet environment．
25788 仿真表明，所提算法在保持较低的小小区业务损失量的前提下，能有效地降低小小区之间的同频干扰，提高网络总速率与SeNBs 能效。 Simulation results show that the algorithm proposed in this paper can effectively reduce the co-channel interference of smallcells and improve the total network rate, at the same time, maintain a lower loss rate of load in small cells．
25789 由于室内多径信号丰富且包含了室内几何信息，可以利用室内多径信号对目标进行定位。 Multipath signals can be used to realize localization since they are abundant and contain geometry information of indoor environments．
25790 基于此，本文提出了一种多径辅助的目标定位算法。 Based on this, this paper proposes a multipath-assisted target localization algorithm．
25791 首先，利用多径信号的差分飞行时间( Time of Flight，TOF) 构建关于目标以及散射体位置的适应度函数; Firstly, the fitness function about the target and scatterer locations is constructed with Time of Flight ( TOF) differences．
25792 然后，提出了基于粒子群优化( Particle Swarm Optimization，PSO) 的目标及散射体位置联合搜索算法，其中利用目标及散射体到达角( Angle of Arrival，AOA) 确定搜索范围; Then, the locations of the target and scatterers are searched jointly by Particle Swarm Optimization ( PSO) and Angle of Arrivals ( AOAs) that determines searching ranges．
25793 其次，选取搜索到的散射体位置联合差分 TOF 求解目标位置; Secondly, the estimated locations of scatterers and TOF differences are used to estimate the target location．
25794 最后，利用仿射传播聚类( Affinity Propagation Clustering，APC) 对所有散射体估计到的目标位置进行聚类，提出聚类准则消除大的定位误差点。 Finally, all target locations are clustered by using Affinity Propagation Clustering ( APC) ，and a clustering criterion is proposed to eliminate big localization errors.