论文标题
神经射频大满贯，用于无监督的定位和映射通道状态信息
Neural RF SLAM for unsupervised positioning and mapping with channel state information
论文作者
论文摘要
我们提出了一个神经网络体系结构，用于以无监督的方式映射到等轴测图的共同学习用户位置和环境，从没有位置信息的渠道状态信息（CSI）值。该模型基于编码器架构。编码器网络将CSI值映射到用户位置。解码器网络通过使用虚拟锚来参数环境来对传播的物理进行建模。它旨在从编码器输出和虚拟锚位置重建飞行时间（TOF）的集合，这些时间是使用超分辨率方法从CSI中提取的。神经网络任务是设置预测的，并经过端到端训练。提出的模型仅通过执行基于物理的解码器来学习一种可解释的潜在，即用户位置。结果表明，所提出的模型在基于合成射线跟踪的数据集上具有单锚SISO设置的次级准确性，而在2D环境中恢复环境映射到最高4厘米的中位数误差，而在3D环境中的映射
We present a neural network architecture for jointly learning user locations and environment mapping up to isometry, in an unsupervised way, from channel state information (CSI) values with no location information. The model is based on an encoder-decoder architecture. The encoder network maps CSI values to the user location. The decoder network models the physics of propagation by parametrizing the environment using virtual anchors. It aims at reconstructing, from the encoder output and virtual anchor location, the set of time of flights (ToFs) that are extracted from CSI using super-resolution methods. The neural network task is set prediction and is accordingly trained end-to-end. The proposed model learns an interpretable latent, i.e., user location, by just enforcing a physics-based decoder. It is shown that the proposed model achieves sub-meter accuracy on synthetic ray tracing based datasets with single anchor SISO setup while recovering the environment map up to 4cm median error in a 2D environment and 15cm in a 3D environment
