论文标题
基于DNN的定位来自通道估计：特征设计和实验结果
DNN-based Localization from Channel Estimates: Feature Design and Experimental Results
论文作者
论文摘要
我们考虑在通道状态信息（CSI）基于大规模MIMO细胞系统的基于通道状态信息（CSI）的定位中使用深神经网络（DNN）。我们讨论了实际CSI估算中可能存在的实际障碍，并基于使所考虑障碍的功能不变的目的引入了基于CSI的DNN应用程序的特征设计方法。我们通过将其应用于在户外校园环境中测量的地理标记的CSI构成的数据集，并训练DNN以根据CSI估算UE的位置，从而证明了这种方法的效率。我们对该学习方法的几个方面进行了实验评估，包括本地化准确性，泛化能力和数据衰老。
We consider the use of deep neural networks (DNNs) in the context of channel state information (CSI)-based localization for Massive MIMO cellular systems. We discuss the practical impairments that are likely to be present in practical CSI estimates, and introduce a principled approach to feature design for CSI-based DNN applications based on the objective of making the features invariant to the considered impairments. We demonstrate the efficiency of this approach by applying it to a dataset constituted of geo-tagged CSI measured in an outdoors campus environment, and training a DNN to estimate the position of the UE on the basis of the CSI. We provide an experimental evaluation of several aspects of that learning approach, including localization accuracy, generalization capability, and data aging.
