论文标题
使用CNNS基于Mamimo CSI的定位：在黑匣子内窥视
MaMIMO CSI-based positioning using CNNs: Peeking inside the black box
论文作者
论文摘要
使用卷积神经网络（CNN）的基于大规模的MIMO（MAMIMO）通道状态信息（CSI）的用户定位系统显示出很大的潜力，在不引入Mamimo通信系统中的任何开销的情况下达到了很高的精度。在这项研究中，我们表明，这两个系统都可以将室内用户定位在视线和非线条件下，精度约为20毫米。但是，为了进一步开发这些定位系统，更多地了解了CNN如何渗透该位置。二手CNN是一个黑匣子，我们只能猜测它们如何定位用户。因此，本文的第二个重点是使用多个实验打开黑匣子。我们使用收集在现实生活中的64-Antenna Mamimo测试台上的开放数据集探索当前的局限性和承诺。这样，收集了系统中的额外洞察力，可以使用正确方向的CNN对基于Mamimo CSI的定位系统进行研究。
Massive MIMO (MaMIMO) Channel State Information (CSI) based user positioning systems using Convolutional Neural Networks (CNNs) show great potential, reaching a very high accuracy without introducing any overhead in the MaMIMO communication system. In this study, we show that both these systems can position indoor users in both Line-of-Sight and in non-Line-of-Sight conditions with an accuracy of around 20 mm. However, to further develop these positioning systems, more insight in how the CNN infers the position is needed. The used CNNs are a black box and we can only guess how they position the users. Therefore, the second focus of this paper is on opening the black box using several experiments. We explore the current limitations and promises using the open dataset gathered on a real-life 64-antenna MaMIMO testbed. In this way, extra insight in the system is gathered, guiding research on MaMIMO CSI-based positioning systems using CNNs in the right direction.
