标题： 无线定位与信道绘图的孪生神经网络 显示英文标题

标题： Siamese Neural Networks for Wireless Positioning and Channel Charting

摘要： 近年来，神经网络被提出用于无线系统中用户设备（UE）的定位和信道测绘。 这两种方法都处理在多天线基站获取的信道状态信息（CSI），以学习将CSI映射到位置信息的函数。 基于深度神经网络的CSI定位需要一个包含CSI及其相关位置信息的数据集。 信道测绘（CC）仅需使用CSI信息即可提取相对位置信息。 由于CC建立在降维的基础上，因此可以使用自动编码器来实现。 本文提出了一种基于孪生网络的统一架构，可用于监督式UE定位和无监督信道测绘。 此外，我们的框架还支持半监督定位，在训练过程中仅有少量的位置信息可用。 我们通过仿真表明，与现有的定位和CC方法相比，孪生网络能够使用单一、统一的神经网络架构实现相似甚至更好的性能。 显示英文摘要

摘要： Neural networks have been proposed recently for positioning and channel charting of user equipments (UEs) in wireless systems. Both of these approaches process channel state information (CSI) that is acquired at a multi-antenna base-station in order to learn a function that maps CSI to location information. CSI-based positioning using deep neural networks requires a dataset that contains both CSI and associated location information. Channel charting (CC) only requires CSI information to extract relative position information. Since CC builds on dimensionality reduction, it can be implemented using autoencoders. In this paper, we propose a unified architecture based on Siamese networks that can be used for supervised UE positioning and unsupervised channel charting. In addition, our framework enables semisupervised positioning, where only a small set of location information is available during training. We use simulations to demonstrate that Siamese networks achieve similar or better performance than existing positioning and CC approaches with a single, unified neural network architecture.