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.

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

Yuxin Zhao, Ericsson AB; Jung-Fu (Thomas) Cheng, Ericsson Research; Atieh Rajabi Khamesi, Ericsson