  The fifth-generation (5G) wireless communication is useful for positioning
due to its large bandwidth and low cost. However, the presence of obstacles
that block the line-of-sight (LOS) path between devices would affect
localization accuracy severely. In this paper, we propose an online learning
approach to mitigate ranging error directly in non-line-of-sight (NLOS)
channels. The distribution of NLOS ranging error is learned from received raw
signals, where a network with neural processes regressor (NPR) is utilized to
learn the environment and range-related information precisely. The network can
be implemented for online learning free from retraining the network, which is
computationally efficient. Simulation results show that the proposed approach
outperforms conventional techniques in terms of NLOS ranging error mitigation.
