# Generalization Investigation for Artificial Intelligence-Based Positioning in IIoT
## 5 Performance Evaluation and Discussion
### Scenario 3: Non-Ideal Receiving Timing  
* Training manner 1: Training data set with ideal receiving timing and test data set with Receiving timing error  
Firstly, we test the case in which the AI/ML model is trained by a data set with ideal receiving timing and then is tested by a data set with a 10 ns receiving timing error. The results are summarized in Figures 22 and 23. It is observed that no matter in the direct AI-based positioning method or the AI-based ToA prediction method or no matter with clutter parameter {60%, 6 m, 2 m} or with clutter parameter {40%, 2 m, 2 m}, the positioning performance degrades. Compared with other scenarios, the degradation is relatively small.  
* Training manner 2: Mixed receiving timing error setting for training  
In this case, the data set for training is constructed by mixing data samples with different receiving timing error settings. In the evaluation, standard deviations of 10 ns, 20 ns, 30 ns, and 0 ns are used for the training data set generation. Figures 24 and 25 show the performance for direct AI-based positioning and AI-based ToA prediction when the test data set is generated by using 10 ns standard deviations. According to the simulation  
Figure 21: Performance of direct AI-based ToA prediction with samples with network synchronization error for fine-tuning.  
Figure 20: Performance of direct AI-based positioning with samples with network synchronization error for fine-tuning.  
results, it is observed that, compared with training manner 1, using mixed receiving timing error setting could improve the positioning performance greatly.
* Training Manner 3: Fine-Tuning  
For each clutter meter setting, the corresponding AI model is first trained by using ideal receiving timing. Then, fine-tune the AI model by using a small number of samples generated with the same receiving timing error setting as that of the test data set. Figures 26 and 27 show the performance for direct AI-based positioning and AI-based ToA prediction when the AI model is fine-tuned by using data set with a 10 ns standard deviation and using test data set with a 10 ns standard deviation. It is observed that using fine-tuning could improve positioning performance to some extent. Yet the improvement is not significant. In addition, the positioning error at 90% CDF is around 1.7 m and 2 m for clutter parameters of {60%, 6 m, 2 m} and {40%, 2 m, 2 m}, respectively. There is still some gap in the requirement of IIoT. Moreover, compared with the training manner 2, the improvement is small.  
Figure 23: Performance of AI-based ToA prediction with different receiving timing error settings between training and inference.  
Figure 22: Performance of direct AI-based positioning with different receiving timing error settings between training and inference.  
Figure 24: Performance of direct AI-based positioning with mixed receiving time errors for training.  
Figure 25: Performance of AI-based ToA prediction with mixed receiving time errors for training.  
Figure 26: Performance of direct AI-based positioning with samples with receiving timing error for fine-tuning.