Deep Learning-Based Indoor Localization Using Received Signal Strength and Channel State Information

Indoor localization has received wide attention recently due to the potential use of wide range of intelligent services. This paper presents a deep learning-based approach for indoor localization by utilizing transmission channel quality metrics, including received signal strength (RSS) and channel state information (CSI). We partition a rectangular room plane into two-dimensional blocks. Each block is regarded as a class, and we formulate the localization as a classification problem. Using RSS and CSI, we develop four deep neural networks implemented with multi-layer perceptron (MLP) and one-dimensional convolutional neural network (1D-CNN) to estimate the location of a subject in a room. The experimental results indicate that the 1D-CNN using CSI information achieves excellent localization performance with much lower network complexity.