# Evaluation of 5G Positioning Performance Based on UTDoA, AoA and Base-Station Selective Exclusion
## 1 Introduction  
The demand for precise and reliable localization is growing rapidly, being a topic of high interest especially for autonomous and unmanned vehicles [1]. The increasing number of such applications in urban environments requires positioning accuracies at the cm level. Initially, a Global Navigation Satellite System (GNSS) has been the main technology used for positioning purposes as it offers high position and timing accuracy, free access and global coverage. However, GNSS vulnerabilities [2], such as jamming and spoofing, can compromise the reliability and accuracy of the position computation. Moreover, GNSS suffers a severe performance degradation in harsh environments, such as deep urban canyons. Alternative solutions to compensate GNSS limitations and vulnerabilities have been the subject of extensive research, as in [3, 4, 5]. To this end, fourth-generation (4G) and current fifth-generation (5G) cellular systems have become an aiding source to provide alternative positioning technologies in the absence of GNSS signals in such harsh environments [6]. The Third-Generation Partnership Project (3 GPP) has been one of the main motivators setting standard requirements for cellular positioning. The 3 GPP 5G New Radio (NR) technology [7] is uniquely positioned to provide added value in terms of enhanced location capabilities. Current localization technologies used to meet these requirements rely ontiming-based techniques, i.e., Time of Arrival (ToA), Time Difference of Arrival (TDoA), or Round-Trip Time (RTT) [8], angle-based techniques, i.e., Angle of Arrival (AoA), or their combinations [9; 10; 11]. All these technologies are expected to play a significant role in achieving accurate user positioning as they possess desirable attributes, namely large bandwidth, massive antenna arrays, centimeter-wave (cmWave) and millimeter-wave (mmWave) transmissions, among others. However, every category has its own limitations. ToA- and TDoA-based localization methods need an accurate time synchronization, while the AoA method can perform well only when the target is not far away from the sensors. When in harsh working conditions of urban scenarios, these methods working as stand-alone technologies do not achieve the positioning accuracy considered as baseline by the 5G Release 16 [7], i.e., a horizontal positioning error below 10 m for 80% of user equipments (UEs). The most common way to minimize such degradation is the use of hybrid techniques. In this context, the combination of timing-based and angle-based methods is of high interest in order to take advantage of both technologies and provide significant improvement in terms of accuracy. Unfortunately, because there is a predominance of Non-Line-of-Sight (NLoS) conditions in 5G urban transmissions, in the presence of network synchronization error, its achievable positioning capabilities are mainly limited by dense multipath. Some contributions in positioning tend to ignore the presence of such propagation obstacles and consider instead the so-called achievable positioning performance [12]. Some other contributions incorporate the presence of multipath and NLoS, but no mechanisms are implemented to distinguish between Line-of-Sight (LoS)/NLoS situations [13]. In general, most of the algorithms for range-based target localization in NLoS environments [14] require sophisticated mathematical tools, which add computational complexity and therefore increase their execution time. In this work, we present a realistic deep urban scenario in the presence of NLoS with a tight network synchronization, using uplink signal of 5G NR operating in the cmWave band. The aim of this work is to assess the achievable positioning performance of hybrid Uplink TDoA (UTDoA) and AoA by taking the advantage of using antenna arrays. This technology helps to characterize the effects of 5G cmWave observables in 5G positioning and add value to the use of advance antennas in a relevant application, namely positioning.