Localization in Wireless Communications Using Deep Learning with Feature Reduction Techniques

In wireless communication, localization plays a key role in different applications such as asset tracking, navigation and emergency response. This paper explores a deep learning framework with feature reduction techniques to improve localization accuracy. We employ a dataset of received wireless signals at different base stations and perform feature reduction using three methods: Principal Component Analysis (PCA), Random Forest Feature Importance (RF) and Recursive Feature Elimination (RFE). Next using the processed features, a deep neural network (DNN) is utilized to predict the localization coordinates. By lowering computational complexity and preventing overfitting our findings show that feature reduction can greatly enhance the DNN's performance comparable with no feature reduction. Further, the REF was found to be the most effective feature-reduction technique for training of DNN model in terms of localization accuracy. Extensive experiments demonstrate that feature reduction and deep learning can be combined to achieve accurate localization in wireless communication systems.