Novel LSTM-Based Approaches for Enhancing Outdoor Localization Accuracy in 4G Networks

Abstract

Fingerprint localization using neural networks is emerging as the state-of-the-art technique for outdoor localization using mobile network features. In this paper, we introduce two sequence-based frameworks showing major accuracy enhancements in large-scale outdoor environments compared to the state of the art in this domain. The first uses a uni-directional LSTM network called SeqOutLoc, and the second uses a bi-directional LSTM network called BiOutLoc. We also introduce the AngleNoiseSynth augmenter to expand the dataset, taking into account the angle of user movement and system noise. For SeqOutLoc, we show how adding sequence information enhances the accuracy of localization in a large-scale outdoor urban area of 45 km2 by 25% compared to previous work, while using 35% fewer network parameters. The second model, BiOutLoc, enhances the localization accuracy with fewer network parameters by utilizing both past and future information, which is useful in near-real-time localization. To the best of our knowledge, our work is the first to use a Bi-LSTM model in outdoor fingerprint-based localization. BiOutLoc achieves a median localization accuracy of 9.4 meters, surpassing other deep learning-based localization systems by 31%, while reducing the number of parameters by 67%. Finally, we use transfer learning to fine-tune the parameters of BiOutLoc trained in a certain area using the data from a new area. This results in an 18% enhancement in accuracy and a 71% reduction in training time compared to training the model using only the data of the new area.