# Methodology for Simulating 5G and GNSS High-Accuracy Positioning
## 1 Introduction  
Precise and reliable localization is a topic of high interest for autonomous and unmanned vehicles [1], such as self-driving cars and drones. Indeed, the automotive industry demands positioning accuracies at the cm-level, in order to enable vehicular use cases based on automated driving and road safety [2]. Current localization technologies used for these critical applications are based on Global Navigation Satellite Systems (GNSS) for absolute positioning, and the combination of radars, cameras and inertial sensors for relative positioning. Nonetheless, the high implementation cost of these on-board sensors may prevent their adoption in certain applications. Thus, wireless networks dedicated for vehicular-to-everything (V2X) communications can also be exploited for positioning purposes. This is the case of fifth generation (5G) cellular networks, whose disruptive technologies are expected to enable high-accuracy localization. The combination of GNSS and cellular networks has attracted special attention along the different network generations [3]. Cellular systems are typically considered to complement the lack of GNSS visibility in urban environments, as discussed in [4]. Most of these hybrid algorithms are basedon the extended Kalman filter (EKF) or particle filter, as in [5; 6]. These navigation solutions are necessary to filter noisy cellular observables over time. Furthermore, the cellular propagation channels are dominated by non-line-of-sight (NLoS) conditions and dense multipath. As studied in [7], the position errors are above 63 m on the 95% of the cases with 20-MHz Long Term Evolution (LTE) signals from field measurements. Therefore, the 5G new radio (NR) features [3; 8], such as wideband signals, massive antenna arrays, millimetre wave (mmWave) transmissions, ultra-dense networks and device-to-device (D2D) communications, are expected to significantly enhance the hybrid positioning performance with GNSS. These NR features introduces high-accuracy ranging and angle measurements with a high network density, which are envisaged to achieve high-accuracy positioning. In addition, the 5G centimetre-wave (cmWave) transmissions with extended bandwidths are also of interest in macro-cell deployments. In this sense, the 3 GPP standard has just approved a new study item on 5G NR positioning [9]. However, there is a limited literature on the integration of GNSS and 5G technologies, such as in [10]. The objective of this work is to present a reproducible methodology for the representative simulation of 5G and GNSS technologies in realistic scenarios. This methodology helps to characterize the effects of 5G cmWave and mmWave observables on GNSS positioning. However, to the best of the authors' knowledge, current models are either too complex, such as those based on ray-tracing models as in [6], or too generic, such as in the LTE standard in [11]. Therefore, a novel methodology is proposed to couple the network density with the GNSS visibility masks. This methodology is then used to assess the achievable positioning performance of cmWave and mmWave technologies, with insights on the current limitations and promising features of 5G. The standardization of 5G positioning in [12] is expected to support dedicated network resources, i.e., positioning pilot signals, over short periods of time, in order to fulfil the stringent latency requirements (i.e., a position fix in less than 500 ms). This leads to the study of snapshot localization algorithms instead of navigation algorithms, where the former only uses one observable per GNSS satellite or 5G base station (BS) for positioning. This snapshot approach is especially suitable for standard protocols with periodical or on-demand positioning occassions, and to reduce the power consumption. The observables are only time-of-arrival (ToA) measurements for GNSS and cmWave positioning, while stand-alone mmWave positioning is based on ToA, angle-of-arrival (AoA) and angle-of-departure (AoD) estimates.