# Generalization Investigation for Artificial Intelligence-Based Positioning in IIoT
## 5 Performance Evaluation and Discussion
### Discussion and Summary  
According to the simulation results above, the following summary can be made:  
* In the IIoT scenario, the AI-based positioning methods are sensitive to the change in clutter parameters, network synchronization errors, and receiving timing errors. Tables 2 and 3 summarize the positioning accuracy at 90% CDF considering different generalization factors for direct AI-based positioning and AI-based ToA prediction, respectively. Generally, once there is a change in these typical parameters, the positioning performance would degrade greatly and the trend in the direct AI-based positioning and AI-based ToA prediction is similar. Comparing the impact of these three factors, it is observed that the impact of receiving timing errors is the smallest due to the small range of timing errors. The impact of the network synchronization error is the most significant. There are two reasons, one reason is that the error range of the network synchronization is usually large. Another reason is all BSs may experience such errors. As for the impact of clutter parameters, more performance degradation is observed when the AI model is trained with clutter parameters of {60%, 6 m, 2 m} and tested by data set with clutter parameters of {40%, 2 m, 2 m}. The possible reason is the different LOS/NLOS conditions between the scenario with a clutter of {60%, 6 m, 2 m} and the scenario with a clutter parameter of {40%, 2 m, 2 m}. The scenario with clutter parameter of {60%, 6 m, 2 m} is full of NLOS-based propagation while lack of LOS-based propagation and the scenarios with clutter parameter of {40%, 2 m, 2 m} contain more LOS-based propagation. In this case, the trained AI model by data set with a clutter parameter of {60%, 6 m, 2 m} may not be able to extract the feature of LOS-based propagation fully. Then, inferior performance will be experienced when handling the LOS-based propagation in the scenario with a clutter parameter {40%, 2 m, 2 m}. * To guarantee the generalization performance from the perspective of clutter parameter and receiving timing error, both optimizing the data set construction and using fine-tuning can be considered. Tables 4 and 5 summarize the positioning accuracy at 90% CDF for direct AI-based positioning and AI-based ToA prediction when mixed data sets and fine-tuning are used. For the impact of different clutter parameters, both optimizing the data set and fine-tuning could improve the performance greatly. Using mixed data set could provide less than 1 m positioning accuracy at 90% CDF. For the impact of receiving timing error, using mixed data set for training could improve the positioning accuracy, while it seems that fine-tuning does not help much. The possible reason is that fewer samples with receiving timing errors are used for training  
Figure 27: Performance of AI-based ToA prediction with samples with receiving timing error for fine-tuning. in the fine-tuning method. For the impact of network synchronization error, it is observed that using the mixed data set and fine-tuning could improve the positioning performance slightly but, on the hand, using these two methods still cannot meet the positioning accuracy of less than 1 m. In addition, when comparing these two generalization improvement solutions, it is observed that optimizing data set construction could achieve slightly better performance due to more samples from the target scenario. * For network synchronization error, using mixed data sets and fine-tuning does not help much in improving the generalization capability. Other solutions can be considered. According to our test, when the network synchronization error is small, the positioning error would be small.