Deep Learning-based Symbolic Indoor Positioning using the Serving eNodeB
This addresses indoor positioning for residential users, but appears incremental as it builds on existing symbolic positioning methods with cellular signal enhancements.
The paper tackles indoor positioning in residential apartments using cellular signals from a serving eNodeB, eliminating the need for specialized infrastructure, and reports that the method outperforms conventional techniques in various performance metrics.
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.