NDR: Noise and Dimensionality Reduction of CSI for Indoor Positioning Using Deep Learning

Abstract

Due to the emerging demand for Internet of Things (IoT) applications, indoor positioning has become an invaluable task. We propose NDR, a novel lightweight deep learning solution to the indoor positioning problem. NDR is based on Noise and Dimensionality Reduction of Channel State Information (CSI) of a Multiple-Input Multiple-Output (MIMO) antenna. Based on preliminary data analysis, the magnitude of the CSI is selected as the input feature for a Multilayer Perceptron (MLP) neural network. Polynomial regression is then applied to batches of data points to filter noise and reduce input dimensionality by a factor of 14. The MLP's hyperparameters are empirically tuned to achieve the highest accuracy. NDR is compared with a state-of-the-art method presented by the authors who designed the MIMO antenna used to generate the dataset. NDR yields a mean error 8 times less than that of its counterpart. We conclude that the arithmetic mean and standard deviation misrepresent the results since the errors follow a log- normal distribution. The mean of the log error distribution of our method translates to a mean error as low as 1.5 cm.