Advanced Positioning in 5G and Beyond: Leveraging Deep Learning Techniques

Recent advancements in machine learning have improved device positioning in challenging environments where traditional methods often fall short. This work introduces a deep learning-based approach to predict the location of user equipment in a wireless radio network. The model leverages data samples derived from channel impulse responses as input features. Additionally, the proposed sample-based method is compared to conventional path-based data estimated through a channel estimator. Performance evaluations are conducted under different conditions to demonstrate the impact of input types, network densification, and available bandwidth in an indoor factory scenario.