用深度学习提升6G工厂室内定位精度，90%情况下误差仅9厘米。
On the Application of Deep Learning for Precise Indoor Positioning in 6G
- 基于多基站的信道冲激响应和信号强度训练神经网络定位模型。
- 18个基站测量下，90%分位定位误差达9厘米。
- 模型对部分基站失效或标签误差有较强鲁棒性，适合工业场景。
由于信号存在非视距（NLoS）特性，室内环境中的精准定位面临挑战。本文探索了人工智能/机器学习技术在工厂室内（InF）场景下的定位精度提升应用。提出的神经网络模型LocNet，利用来自多个发射接收点（TRPs）的信道冲激响应（CIR）和参考信号接收功率（RSRP）进行训练。仿真结果表明，当使用18个TRP的测量数据时，LocNet在90%分位上实现了9厘米的定位精度。此外，我们证明该模型在部分TRP随机失效时仍具有良好的泛化能力。最后，我们分析了模型对训练中真实标签误差的鲁棒性。
原文摘要 · Abstract (English)
Accurate localization in indoor environments is a challenge due to the Non Line of Sight (NLoS) nature of the signaling. In this paper, we explore the use of AI/ML techniques for positioning accuracy enhancement in Indoor Factory (InF) scenarios. The proposed neural network, which we term LocNet, is trained on measurements such as Channel Impulse Response (CIR) and Reference Signal Received Power (RSRP) from multiple Transmit Receive Points (TRPs). Simulation results show that when using measurements from 18 TRPs, LocNet achieves a 9 cm positioning accuracy at the 90th percentile. Additionally, we demonstrate that the same model generalizes effectively even when measurements from some TRPs randomly become unavailable. Lastly, we provide insights on the robustness of the trained model to the errors in ground truth labels used for training.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。