论文标题
使用服务eNodeB的深度学习符号室内定位
Deep Learning-based Symbolic Indoor Positioning using the Serving eNodeB
论文作者
论文摘要
本文提出了一种专为住宅公寓设计的新型室内定位方法。提出的方法利用了从服务eNodeB发出的细胞信号，从而消除了对专门定位基础架构的需求。此外，它利用脱氧自动编码器来减轻细胞信号损失的影响。我们使用从八个符号空间的代表性公寓内收集的实际数据评估了提出的方法。实验结果验证了所提出的方法在各种性能指标中的传统象征性室内定位技术优于常规的象征性室内定位技术。为了促进可重复性并培养新的研究工作，我们制作了与这项公共工作相关的所有数据和代码。
This paper presents a novel indoor positioning method designed for residential apartments. The proposed method makes use of cellular signals emitting from a serving eNodeB which eliminates the need for specialized positioning infrastructure. Additionally, it utilizes Denoising Autoencoders to mitigate the effects of cellular signal loss. We evaluated the proposed method using real-world data collected from two different smartphones inside a representative apartment of eight symbolic spaces. Experimental results verify that the proposed method outperforms conventional symbolic indoor positioning techniques in various performance metrics. To promote reproducibility and foster new research efforts, we made all the data and codes associated with this work publicly available.
