## 3 Conventional Positioning Solutions in 5G Networks
### Assisted Positioning in 5G Networks  
In this section, we present the efficiency of 5G hybrid solutions. The following examples show that GNSS assisted 5G positioning is currently being extensively studied by multiple research groups. In addition, a promising solution is also reviewed, which utilizes 5G signals assisted by visible light communication.  
[PERSON] et al. [43] proposed a hybrid 5G-GNSS positioning method (see Figure 5) based on combining AOA estimates from 5G base stations and TOA measurements from GNSS satellites. In their simulations, three satellites are visible. The mathematical models for the GNSS and 5G measurements are nonlinear due to the clock bias between the UE and GNSS system time; therefore, the Taylor series least-square method is used to linearize the mathematical model and estimate the position solution iteratively. Then, moving averaging is performed for the raw position estimates to reduce the effects of noise. According to their results, the 2D position error is less than 10m in more than 95% of the cases with the hybrid 5G-GNSS solution. Under the same circumstances, this value is about 15 m for the standalone 5G AOA positioning method. Thus, by using both GNSS and 5G signals, the authors were able to reduce the positioning error by at least 5 m.  
[PERSON] et al. [44] proposed their method of hybrid GNSS and 5G positioning for specific scenarios where systems of Autonomous Vehicles (AVs) are equipped with 5G transceivers. They focused on scenarios where GNSS and 5G positioning systems individually do not work well or have too large error. For example, on urban roads, where AVs can only receive GNSS signals for short periods of time due to limited satellite visibility. The results of their simulations show that when the GNSS satellites are well-spaced and two 5G base stations are available, a sub-meter error is achievable with the hybrid solution. Moreover, the sub-meter error is also obtainable when the GNSS satellites are ill-positioned but two 5G base stations are still accessible. Based on that, the authors concluded that the positioning information of the 5G signals is the main component of this hybrid 5G-GNSS positioning solution.  
[PERSON] et al. [45] focused on hybrid 5G-GNSS D2D positioning in their study. The experiments were conducted with a dataset consisting of real GNSS trajectory data and simulated 5G D2D measurements. Moreover, the GNSS dataset contains both parts with good GNSS conditions and parts with denied GNSS. To improve the efficiency of measurements for D2D positioning in 5G networks, a novel Crossover Multiple-Way Ranging protocol (CO-MWR) was proposed. In addition, a particle filter was used to estimate the user's position. They also proposed a state dimension reduction method to prevent particle degeneracy, which would lead to performance degradation. Their results show that their solution can provide accurate estimates even in areas where GNSS is not available. Meanwhile, an RMSE of 3 m or less can be achieved for the entire data set.  
In their study, [PERSON] et al. [46] presented a multi-layer network architecture that integrates visible light communication (VLC) and visible light positioning (VLP) in 5G networks. Their simulations compared VLC assisted 5G and WiFi-based positioning solutions in an indoor environment (one room) with 25 VLC AP and 4 RF AP evenly distributed. According to their results, the mean positioning error was 7.19 cm in case of the VLC-5G-based positioning, in contrast to the WiFi fingerprint algorithm, whose mean error was 68.76 cm. Aside from the mentioned sub-meter positioning error, some features of the VLP technology are worth mentioning: due to the already existing light infrastructures, deployment costs are low, and they can be used in electromagnetic-interference-sensitive scenarios, and communication security is ensured since visible light signals cannot penetrate walls.