Single-anchor two-way localization bounds for 5G mmWave systems

Recently, millimeter-wave (mmWave) 5G localization has been shown to be to provide centimeter-level accuracy, lending itself to many location-aware applications, e.g., connected autonomous vehicles (CAVs). One assumption usually made in the investigation of localization methods is that the user equipment (UE), i.e., a CAV, and the base station (BS) are time synchronized. In this paper, we remove this assumption and investigate two two-way localization protocols: (i) a round-trip localization protocol (RLP), whereby the BS and UE exchange signals in two rounds of transmission and then localization is achieved using the signal received in the second round; (ii) a collaborative localization protocol (CLP), whereby localization is achieved using the signals received in the two rounds. We derive the position and orientation error bounds applying beamforming at both ends and compare them to the traditional one-way localization. Our results show that mmWave localization is mainly limited by the angular rather than the temporal estimation and that CLP significantly outperforms RLP. Our simulations also show that it is more beneficial to have more antennas at the BS than at the UE

Distributed Two-Way Localization Bounds for 5G mmWave Systems

Recently, localization for 5G millimeter-wave communication systems has been shown to provide high-accuracy performance, with error being in the order of tens of centimeters. However, most of the literature assumes a high level of synchronization, which is not always the case practically. To address this matter, we investigate a distributed two-way localization protocol (DLP) that relieves the need for tight timing synchronization. We derive the position and orientation bounds, when localization is initiated and carried out by the base station. Our simulation results show that the performance of DLP is identical to that of the synchronized one-way localization. We thus conclude that the considered 5G localization is limited by the estimation of the angles rather than the delay. The results also imply that orientation estimation is more challenging than position estimation