ID 原文 译文
15145 首先,在楼宇内按需部署纵向定位设备; First of all, this article on-demand deploys vertical positioning equipment within the building.
15146 然后,建立室内各层楼梯口的LoRa信号指纹库,划定对应不同楼层信号强度特征的判定阈值; And then, it sets up the indoor stairs LoRa signals in each layer fingerprint and draws corresponding to the different characteristics of signal strength floor for determining threshold.
15147 最后结合LoRa-RSSI动态识别和楼层判定方法,捕捉室内动态下无线信号强度的实时变化,实现上下楼活动动态识别,提高楼层判定的准确率。 Final-ly, by combining with LoRa-RSSI dynamic identification and determination methods, it captures real-time change of indoor wireless signal strength in dynamic condition, achieves dynamic identification of up and down the stairs, and enhances the accuracy in floor to judge.
15148 实验结果表明,方法识别判定准确率达97.9%,实现简单,不受外界环境影响,可快速按需部署。 The experimental results show that the recognition accuracy of this method reaches 97. 9 %, and it is simple to implement and less affected by the external environment, which could be deployed quickly on demand.
15149 结合室内二维定位技术,提供了一种新的室内人、物三维定位方法和思路。 Combined with the indoor two-dimen-sional positioning technology, this method provides a new method and idea for indoor three-dimensional positioning of people and objects.
15150 超大规模多输入多输出(Multiple Input Multiple Output,MIMO)技术在未来的移动通信中具有巨大的潜力。 Extremely massive multiple input multiple output(MIMO) has shown considerable potential in future mobile communications.
15151 然而,采用超大型的天线阵列会出现空间非平稳性信道特征,如果为基站的每根天线都配备一个高精度量化器,系统功耗将大幅度增加,从而阻碍超大规模MIMO系统的广泛应用。 However, the use of extremely massive aperture arrays will lead to spatial non-stationary channel conditions, and each antenna of the base station is equipped with a high-precision quantizer, the power consumption of the system will be greatly increased, which will hinder the widespread application of ultra-large-scale MIMO systems.
15152 因此,假设基站的每根天线都配备有一对1-比特模数转换器(Analog-to-Digital Converters,ADC),利用子阵列与用户之间的映射关系来描述非平稳信道特征。 Therefore, this article assumes that each antenna of the base station is equipped with a pair of 1-bit analog-to-digital converters( ADC), and uses the mapping relationship between the sub-array and the user to describe the non-stationary channel characteristics.
15153 借助深度神经网络(Deep Neural Network,DNN)强大的泛化能力,设计一种新的生成式监督DNN模型,该模型可以使用合理数量的导频进行训练。 Based on the powerful generalization ability of neural network( DNN), this paper designs a new generative supervised DNN model that can be trained with a reasonable number of pilots.
15154 仿真结果表明,所提出的网络可以使用较少的导频得到较好的估计性能,在性能与计算复杂度之间取得良好的平衡。 The simulation results show that the proposed network can achieve better estimation performance with less pilots and achieve a good balance between performance and complexity.