Efficient Non-Line-of-Sight Identification in Localization Using a Bank of Neural Networks

Non-line-of-sight (NLOS) error is one of the dominant sources of error in localization applications. Existing algorithms rely on solving a set of highly nonlinear equations to compensate for this error, which is intractable in practice. In this paper, we propose an efficient NLOS identification algorithm based on supervised machine learning. This approach enables us to improve localization accuracy by taking advantage of the NLOS measurements if the location of the reflector is known. Hence, our approach can be employed in combination with 5G intelligent reflecting surface systems to provide location-based wireless services. We also analytically derive the Cramer-Rao lower bound for the localization problem at hand. Finally, we investigate the performance of our proposed NLOS identification algorithm under different simulation setups.