arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新，休息一下吧，周末愉快~~
arXiv 2203.08264cs.ITcs.LGeess.SPmath.IT
收藏

神经RF SLAM用于信道状态信息的无监督定位和映射

Neural RF SLAM for unsupervised positioning and mapping with channel state information

Qualcomm Technologies Netherlands B.V.（高通技术荷兰有限公司）；Qualcomm Technologies, Inc.（高通技术公司）；University of Amsterdam（阿姆斯特丹大学）；Qualcomm AI Research（高通人工智能研究院）

Shreya Kadambi, Arash Behboodi, Joseph B. Soriaga, Max Welling, Roohollah Amiri, Srinivas Yerramalli, Taesang Yoo

备注：
Accepted at IEEE International Conference on Communications 2022. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work
阅读 PDF
arXiv 页面 ↗HTML 原文 ↗

摘要：

我们提出了一个神经网络架构，用于联合学习用户的位置和环境映射到等距，在一个无监督的方式，从信道状态信息（CSI）值没有位置信息。该模型是基于一个编码器-解码器架构。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点参数化环境来模拟传播的物理过程。它旨在从编码器输出和虚拟锚点位置重建使用超分辨率方法从CSI提取的飞行时间（ToF）的集合。神经网络任务被设置为预测，并相应地被端到端地训练。所提出的模型学习可解释的潜在，即，用户的位置，通过只强制一个基于物理的解码器。结果表明，所提出的模型实现了亚米级精度的合成光线追踪为基础的数据集与单锚SISO设置，同时恢复环境地图高达4cm的中位数误差在2D环境和15 cm在3D环境

英文摘要：

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

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

↑