5G millimeter wave (mmWave) signals can enable accurate positioning in vehicular networks when the base station (BS) and vehicles are equipped with large antenna arrays. However, radio-based positioning suffers from multipath signals generated by different types of objects in the physical environment. Multipath can be turned into a benefit, by building up a radio map (comprising the number of objects, object type, and object state) and using this map to exploit all available signal paths for positioning. Mapping and positioning using 5G can be categorized as a simultaneous localization and mapping (SLAM) problem.
Fig. 1. 5G SLAM scenario with the propagation environment and two vehicles.
Fig. 1 shows the 5G SLAM scenario, where 5G communication links can be used to share measurements, map, or location information, leading to cooperative positioning and mapping. In 5G mmWave cooperative positioning and mapping (i.e., positioning and mapping based on measurements from 5G mmWave communication signals), there are three main tasks: (i) Vehicle positioning: determine the states (position, velocity, heading, clock bias) of the vehicles; (ii) Environment mapping: estimate the number of objects, as well as each object’s type and position; and (iii) Cooperation: in the BS fusing the collected the map information from the vehicles, and relay it to each vehicle.
Fig. 2. Mapping accuracy of 5G SLAM without cooperation (left) and with cooperation (right).
Fig. 2 reports the mapping accuracy, represented by the GOSPA results. In Fig. 2a, we see that the GOSPA per vehicle goes down as they move in the environment. The GOSPA at the BS is reduced faster, as it can benefit from the information of all vehicles. In Fig. 2b, we note that when the BS sends back the map to the vehicles over the downlink, each vehicle can benefit from the measurements of the other vehicle, so that the GOSPA is reduced faster on the vehicle maps as well. Video represents 5G SLAM.