DSP: A Deep Neural Network Approach for Serving Cell Positioning in Mobile Networks

Positioning remains a crucial aspect with wide-ranging applications, despite the availability of various solutions and extensive research. The lack of transparency surrounding the infrastructure topology and precise locations of base stations operated by telecommunication companies further complicates the positioning process. Moreover, as wireless networks continue to evolve with advancements like 5G and 6G, accurate positioning becomes increasingly important, leveraging vast amounts of data and artificial intelligence techniques. This paper proposes a Deep Neural Network (DNN) based approach called DSP for accurately determining the position of telecommunication operator serving cells in outdoor spaces. The proposed method incorporates fingerprint data collection, machine learning algorithms, and big data analysis to enhance accuracy and expand the range of evaluatable parameters. The data was collected and labeled using the drive test method. By employing deep learning techniques, the proposed method successfully predicts the location of the base station connected to the user device. Through effective data preprocessing and optimized hyperparameters, the average distance error was reduced to 20 meters.