Localization in 5G and beyond using RIS
High-accuracy localization in the absence of the global positioning system (GPS) has attracted attention in recent years. A prime example of use cases that require a reliable positioning system is the set of indoor applications based on autonomous vehicles (AVs). With the advancements in 5G and beyond radio access networks (RAN), new positioning signals can be leveraged to provide these applications with accurate location estimates. However, these signals suffer from significant path losses indoors. Furthermore, for accurate localization, existing frameworks have stringent synchronization requirements which can be challenging to fulfill. In this paper, we propose OFDM RA: Optimal Femtocells Deployment for Accurate Indoor Positioning of RIS-Mounted AVs, a novel positioning framework that is robust against multipath and does not require strict synchronization between anchor-anchor or anchor-target entities. The first design objective of OFDM-RA is the mitigation of ranging errors by leveraging a compact reconfigurable intelligent surface (RIS) mounted on top of AVs acting as a programmable mirror in a 5G network. The second design objective is to achieve optimal anchor placement in a three-dimensional indoor space to lower the geometric dilution of precision (GDOP), which can minimize the effect of geometric-induced errors in the final position estimate. Through experimental verification, we show that the overall localization error is affected by GDOP as a combination of the X −Y plane and Z-axis estimations. By optimizing anchor placement, we show OFDM-RA can improve Z-axis accuracy by seven-fold compared to state-of-the-art with a sub-1 m three-dimensional accuracy for more than 95% of cases.