# Connected Objects Geo-Localization Based on SS-RSRP of 5G Networks
## 1 Introduction  
Global Positioning System (GPS) has for decades been the unique practical way of global positioning. This long period of pioneering work has allowed GPS to acquire major technical improvements in terms of equipment and theory. But the most important benefit of GPS remains linked to the change of human habits by making localization an unavoidable feature in our lives and in different fields such as transports, military, health and games. In spite of this remarkable accomplishment, GPS still has limitations that slow down and keep on slowing down progress in the field of localization. These limitations are mainly related to the accuracy of the satellite signals used or their availability. Indeed, for security reasons, GPS limits the accuracy of signals used in the civil domains giving a geo-localization of the order of a few meters. The security and the strategic nature of this limitation confirms that this will not be lifted in the coming years. Location-based applications such as vehicle collision detection, real-time Assisted Living for healthcare providers or visually impaired people localization will continue to be subject of scientific papers until we have improved the accuracy to a few centimeters. However, the next generation of mobile network, 5G, promises a leap in performance technologically compared to 4G. They can offer a flow rate of 10 times higher than the current 4G. The latency is also reduced tremendously since it is divided by 10 compared to 4G and LTE-A. The 5G network ensures robust reliability and radio link availability. The network is also able to simultaneously connect a large number of objects with a better energy efficiency [1]. All those criteria make the 5G a strong candidate to assist GPS features. Localization solutions based on radio signals have evolved theoretically and fundamentally over the last few decades for the 4G and older generations. Works in 2G and 3G such as [2; 3; 4] have accuracy over 100 m. Ref. [5] use trilateration techniques in 3G and 4G networks and have achieved accuracies of 50 m. Refs. [6; 7] use delay techniques and obtain accuracies of 10 m.  
However, the next generation of mobile network, 5G, promises a leap in technology performance compared to 4G and previous generations. They can offer a flow rate of 10 times higher than the current 4G. The latency is also reduced tremendously since it is divided by 10 compared to 4G and LTE-A. The 5G network ensure robust reliability and radio link availability. The network is also capable to simultaneously connect a large number of objects with a better energy efficiency. All those criteria make the 5G a strong candidate to assist GPS features. The performance promised by the arrival of 5G is estimated in the latest 3 GPP standard update to be less than 4 m. This will unlock new opportunities for location-based applications such as vehicle collision detection or precise topographic mapping. Recent research works are trying to improve the accuracy to reach the 3 GPP objectives and specially to satisfy the requirements of new applications [8]. The works in [9; 10; 11] use mmWave and a massive number of antennas, confirming achievable positioning accuracy below one meter. In [12] authors use RSS measurements and distributed massive MIMO and show an accuracy of 35 m from a single base station. The works in [13; 14; 15] use multipath assisted techniques with advanced tracking algorithms for positioning. Other works such as [16; 17; 18; 19] propose collaborative positioning solutions and localization solutions in ultra-dense networks where the simulation of certain works shows the ability to reach accuracies of less than one meter. [PERSON] et al. [20] provided an algorithm to predict the location of a person in 5G networks using RSS measurements. For this purpose, the authors use in the first step the relative coordinates by using Isomap and in a second step these coordinates are transformed by a Procrustes analysis. The performance of their algorithm is evaluated using the Cramer-Rao lower bound.