CSI Based Indoor Localization Using Ensemble Neural Networks

Indoor localization has attracted much attention due to its many possible applications e.g. autonomous driving, Internet-Of-Things (IOT), and routing, etc. Received Signal Strength Indicator (RSSI) has been used extensively to achieve localization. However, due to its temporal instability, the focus has shifted towards the use of Channel State Information (CSI) aka channel response. In this paper, we propose a deep learning solution for the indoor localization problem using the CSI of an \(8 \times 2\) Multiple Input Multiple Output (MIMO) antenna. The variation of the magnitude component of the CSI is chosen as the input for a Multi-Layer Perceptron (MLP) neural network. Data augmentation is used to improve the learning process. Finally, various MLP neural networks are constructed using different portions of the training set and different hyperparameters. An ensemble neural network technique is then used to process the predictions of the MLPs in order to enhance the position estimation. Our method is compared with two other deep learning solutions: one that uses the Convolutional Neural Network (CNN) technique, and the other that uses MLP. The proposed method yields higher accuracy than its counterparts, achieving a Mean Square Error of 3.1 cm.