User Environment Detection Using Long Short-Term Memory Autoencoder

Mobile networks are rapidly expanding, and there is an increasing demand for seamless connectivity. Detecting whether a user is indoors or outdoors is pivotal in optimizing network performance and enhancing user experience. This paper proposes a semi-supervised learning method using a Long Short-Term Memory Autoencoder (LSTM-AE) that detects the user's environment. It uses mobile network radio signal data of real users. The LSTM Autoencoder learns to capture the underlying structure of the data and identify patterns that distinguish indoor from outdoor environments. Three key features are used to train the model: Reference Signal Received Power (RSRP), Channel Quality Indicator (CQI), and Timing Advance (TA). Results show that the LSTM-AE model achieves a high accuracy of 84% and an F1 score of 89%. In our approach, we achieve a substantial reduction of 34.14% in the requirement for labeled data compared to traditional methods that primarily rely on fully supervised learning. By diminishing the dependence on labour-intensive and time-consuming data labelling processes, this improvement significantly enhances the overall efficiency of the machine-learning process.