A Self-Attention Mechanism Based NLOS Discrimination Method for B5G Network

With the rapid development of mobile communication technology, ranging and angling methods based on LTE and 5G signals have been widely used in location-based services (LBSs). However, mobile communication signals also undergo different degrees of occlusion and refraction during propagation because of the influence of buildings and human activities. The conventional ranging and angling methods are unable to extract high-precision navigation observations from these non-line-of-sight (NLOS) signals. Regarding the NLOS signals that seriously affect the performance of wireless positioning systems, this paper proposes a deep learning method based on the self-attention mechanism by distinguishing the difference characteristics in the autocorrelation functions of line-of-sight (LOS) and NLOS signals. It provides a basis for developing a stable LBS by accurately determining the state of received signals in complex indoor and outdoor environments. The simulation and field tests were carried out separately to evaluate the performance of the method developed in this paper under different scenarios, which achieved a discrimination accuracy better than 99.9% for NLOS signals.