## 6 Simulations
### Simulation Settings  
3 GPP Technical Report 38.901 [47] has outlined an indoor factory with dense clutter and a high base station height (InF-DH) scenario with its lower probability of LOS conditions. We evaluate the performance of our proposed AMT method based on the InF-DH scenario specified by 3 GPP. The indoor factory scenario is a highly intricate industrial environment characterized by obstacles. As shown in Figure 4, 18 BSs are deployed on the ceiling of the factory with a length of 120 m and a width of 60 m. The clutters in the scene can be considered as machinery, assembly lines, and storage shelves, and UEs are uniformly distributed in the area. We use an open-source indoor measurement dataset WLI_3_1015_Inf_DH662_FR1_drop1_cir_rsrp_toa_tdoa from [59], the full parameter settings of the dataset simulation can be found in the introduction_positioning file, and we list partial parameter setting of system-level simulation in Table 1. The dataset generated by the system-level simulator is generated according to the 5G-NR standard of 3 GPP [47]. In the system-level simulation, the clutter's density, height, and diameter are 60%, 6 m, and 2 m, respectively, which are used to generate a heavy NLOS indoor scenario. The BS antenna configuration is denoted by (\(M^{a}\), \(N^{a}\), \(P\), \(M^{a}_{S}\), \(N^{a}_{S}\)), and \(M^{a}\) and \(N^{a}\) indicate that a single panel array antenna has an \(M^{a}\times N^{a}\) uniform planar array, where \(N^{a}\) is the antenna in each row and \(M^{a}\) is the number of rows in the vertical dimension. \(P\) represents the number of polarization dimensions,  
Figure 4: Indoor positioning scenario. and \(N_{g}^{a}\) illustrate that the antenna system consists of \(M_{g}^{a}\) vertical panels and \(N_{g}^{a}\) horizontal panels, and in the parameter setting, we only use one antenna in the receiver/UE side. Spatial consistency is used to keep the various channel generation steps spatially consistent for a drop-based simulation, and this process is not used in the simulator. The UE is moving in a random direction with a fixed speed of 3 km/h, which means the orientation of the antenna is random. From the settings of the system-level simulation, we can infer that there may exist some errors in AOA because of the randomness of UEs' orientation. These errors will directly affect the phase of CIR by introducing errors in field patterns, which will influence the measurement of AOA. However, because the useful angle information for the position estimation contained in CIR exits in the angle difference between antennas and we only use one antenna for receiving signals, it will not result in many errors in our positioning scheme. However, when facing the effects of antenna orientation, improvements in the estimation method of AoA can help increase the estimation accuracy of AoA [60], and using machine learning methods to assist position calculation using AoA estimation can also help enhance the positioning accuracy [61]. Also, the number of antennas is proven to affect the positioning performance by improving the resolution in AoA estimation [62]; therefore, in practical situations, the user devices with more antennas will also benefit the positioning performance. We sample CIR data by truncating the first 256 time-domain points based on the first Tx antenna element and the first Rx antenna element from CIR, and the corresponding multipath characteristic of CIR is represented in Figure 5. In the dataset, the CIR received by each UE is represented as a three-dimensional vector of \"\(18\times 256\times 2\)\", which represents the UE received CIRs from 18 BSs, and each CIR contains 256 complex-valued sampling points. The dataset consists of 80,000 CIRs corresponding to true UE coordinates. A total of 78,400 data samples are used as the training set while 1600 are used for a test set.