User-Side Retraining-Free Learning for High-Precision 5G Positioning

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Abstract
The fifth-generation (5G) wireless communication signal is ideal for positioning by the user equipment (UE) due to its high precision, low cost, low latency, and easy integration. However, UE-side positioning faces significant challenges in complex electromagnetic environments. Obstacles that block the line-of-sight (LOS) path can severely impact localization accuracy. This paper proposes a deep learning architecture with two-stage inference to achieve high-precision positioning at the user side with multipath channels. For the first time, online learning is achieved without the need to retrain the neural network (NN), making it suitable for implementation on UE. Specifically, the variational inference theory is used to sense the environment and improve positioning accuracy using multipath information. This approach significantly reduces range error in non-line-of-sight (NLOS) channels. Furthermore, a new NN with a neural processes regressor (NPR) is proposed to learn the distribution of ranging bias directly from received signals. The proposed learning architecture and networks can be implemented without retraining, even under different circumstances and environments, making it more computationally efficient than most existing NNs and suitable for practical deployment. Simulation results demonstrate that the proposed approach outperforms conventional techniques with various channels.

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