Extrinsic Information Aided Fingerprint Localization of Vehicles for Cell-Free Massive MIMO-OFDM System

Abstract

In smart city, traffic congestion and parking problems will be solved assisted by precise vehicle location. To address the vehicle localization problem in smart city, a novel extrinsic information aided fingerprint localization algorithm is proposed in this paper. Firstly, on the basis of massive multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM), the angle-delay domain channel matrix (ADDCM) is extracted. Then, an amplitude ratio based non-line of sight (NLoS) identification method is provided to estimate link states. For the case of LoS existence, in order to save storage space, a tuple fingerprint is proposed to record angle of LoS path. In other case, when all APs service in NLoS scenarios, to improve the localization robustness in dynamic environments, the correlated ADDCM (CADDCM) is taken as location fingerprint which can extract the constant information related to fixed scattering clusters. A greedy-based fingerprint matching scheme is used to search the nearest reference point (RP). Furthermore, the extrinsic information provided by neighbor vehicles, such as estimated location and measured distance, is utilized to improve the localization stability. Simulation results show that the extrinsic information aided algorithm could improves the localization accuracy and robustness in dynamic environments.