# Generalization Investigation for Artificial Intelligence-Based Positioning in IIoT
## 5 Performance Evaluation and Discussion
### Discussion and Summary
For example, when the AI model is trained with the data set with mixed network synchronization error setting and tested with data set of 30 ns standard deviation, then the positioning error @90% for clutter parameter {40%, 2 m, 2 m} is reduced from around 3 m to less than 2 m, as shown in Figure 28. Thus, in the realistic work, solutions to reduce the network synchronization error could help to guarantee the performance. In addition, another approach is to use dedicated models for dedicated network synchronization error settings. For example, as shown in Figure 29, in the network with a synchronization error of 50 ns standard deviation, when the AI model is also trained with data set with synchronization error of 50 ns standard deviation, then better performance is also achieved. In this approach, the network should monitor the distribution of the network synchronization error, and once the distribution changes, then a model switch is needed. The details of the model switch could be one part of our future work. \begin{table}
\begin{tabular}{l l l} \hline  & **Clutter Parameter** & **Positioning Error @90\% CDF (m)** \\ \hline \multirow{4}{*}{Impact from different clutter parameters from training} & Training: {0.6,6,2} & \multirow{2}{*}{7.0914} \\  & Test: {0.4,2,2} & \\ \cline{2-3}  & Training {0.4,2,2} & \\  & Test{0.6,6,2} & \\ \hline \multirow{2}{*}{Impact from network synchronization error} & Training/Test: {0.6,6,2} & \multirow{2}{*}{12.4486} \\  & Training/Test: {0.4,2,2} & \multirow{2}{*}{14.5779} \\ \hline \multirow{2}{*}{Impact from receiving error} & Training/Test: {0.6,6,2} & \multirow{2}{*}{1.7885} \\  & Training/Test: {0.4,2,2} & \multirow{2}{*}{2.5998} \\ \hline \end{tabular}
\end{table} 
Table 2: Positioning results summary of different affecting factors for direct AI-based positioning. \begin{table}
\begin{tabular}{l l l} \hline  & **Clutter parameter** & **Positioning Error @90\% CDF (m)** \\ \hline \multirow{4}{*}{Impact from different clutter parameters from training} & Training: {0.6,6,2} & \multirow{2}{*}{7.1173} \\  & Test: {0.4,2,2} & \\ \cline{2-3}  & Training {0.4,2,2} & \\ \cline{2-3}  & Test{0.6,6,2} & \\ \hline \multirow{2}{*}{Impact from network synchronization error} & Training/Test: {0.6,6,2} & \multirow{2}{*}{12.7748} \\  & Training/Test: {0.4,2,2} & \multirow{2}{*}{15.4699} \\ \hline \multirow{2}{*}{Impact from receiving timing error} & Training/Test: {0.6,6,2} & \multirow{2}{*}{1.3461} \\  & Training/Test: {0.4,2,2} & \multirow{2}{*}{2.2409} \\ \hline \end{tabular}
\end{table} 
Table 3: Positioning results summary of different affecting factors for AI-based ToA prediction. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Simulation Cases** & \multicolumn{3}{c}{**Positioning Error @90\% CDF (m)**} \\ \hline  & **Clutter Parameter of Test Data Set** & **Mixed Parameter** & **Fine-Tuning** \\ \hline \multirow{3}{*}{Different clutter parameters} & \{0.6,6,2\} & 0.5419 & 1.4315 \\  & \{0.4,2,2\} & 0.7684 & 0.7185 \\ \cline{1-1}  & \{0.6,6,2\} & 1.2736 & 4.7145 \\ \cline{1-1}  & \{0.4,2,2\} & 2.3022 & 8.0254 \\ \cline{1-1}  & \{0.6,6,2\} & 0.8074 & 1.6094 \\ \cline{1-1}  & \{0.4,2,2\} & 1.2604 & 2.1858 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results summary of mixed data sets and fine-tuning for direct AI-based positioning. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Simulation Cases** & \multicolumn{3}{c}{**Positioning Error @90\% CDF (m)**} \\ \hline  & **Clutter Parameter of Test Data Set** & **Mixed Parameter** & **Fine-Tuning** \\ \hline \multirow{3}{*}{Different clutter parameters} & \{0.6,6,2\} & 0.6867 & 1.5325 \\  & \{0.4,2,2\} & 0.7974 & 0.8494 \\ \cline{1-1}  & \{0.6,6,2\} & 1.3461 & 4.4925 \\ \cline{1-1}  & \{0.4,2,2\} & 2.2409 & 7.506 \\ \cline{1-1}  & \{0.6,6,2\} & 0.9341 & 1.6034 \\ \cline{1-1}  & \{0.4,2,2\} & 1.2952 & 2.1152 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results summary of mixed data sets and fine-tuning for AI-based ToA prediction. Figure 28: Performance comparison between different network synchronization errors.