Generalization aspect of accurate machine learning models for CSI-based localization

Localization is the process of determining the position of an entity in a given coordinate system. Due to its wide range of applications (e.g. autonomous driving, Internet-of-Things), it has gained much focus from the industry and academia. Channel State Information (CSI) has overtaken Received Signal Strength Indicator (RSSI) to achieve localization given its temporal stability and rich information. In this paper, we extend our previous work by combining classical and deep learning methods in an attempt to improve the localization accuracy using CSI. We then test the generalization aspect of both approaches in different environments by splitting the training and test sets such that their intersection is reduced when compared with uniform random splitting. The deep learning approach is a Multi Layer Perceptron Neural Network (MLP NN) and the classical machine learning method is based on K-nearest neighbors (KNN). The estimation results of both approaches outperform state-of-the-art performance on the same dataset. We illustrate that while the accuracy of both approaches deteriorates when tested for generalization, deep learning exhibits a higher potential to perform better beyond the training set. This conclusion supports recent state-of-the-art attempts to understand the behaviour of deep learning models.