# Indoor Localization Using 6G Time-Domain Feature and Deep Learning
## 4 Numerical Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Noise Level** & **Algorithm** & **ResNet** & **ResNet with CLAB** &
\begin{tabular}{c} **ResNet with SLAB** \\ **CLAB and SLAB** \\ \end{tabular} \\ \hline
10\% noise & 0.0969 & 0.0752 & 0.0774 & 0.0544 \\
15\% noise & 0.1046 & 0.094 & 0.0988 & 0.0607 \\
20\% noise & 0.1298 & 0.1139 & 0.1155 & 0.0716 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: RMSE (m) performance under different noise levels. Figure 6: The RMSE versus noise levels without interference. Next, the ResNet with the SLAB model consistently achieves lower RMSE values compared to the baseline ResNet, indicating that spatial attention enhances feature extraction and improves noise robustness. For a 10% noise level, RMSE is reduced from 0.0969 m to 0.0774 m, reflecting a 20.2% improvement. For a 15% noise level, RMSE reduction is 5.5%, showing that SLAB remains effective at moderate noise levels. This indicates that SLAB is particularly effective in reducing errors under varying noise conditions, likely due to its ability to capture spatial dependencies more effectively. Lastly, the ResNet with CLAB and SLAB models achieves the lowest RMSE values under all noise conditions, verifying that the combination of spatial and channel attention mechanisms results in superior performance. Compared to ResNet, RMSE reductions of 43.9%, 42%, and 44.8% are observed at 10%, 15%, and 20% noise levels, respectively. From Table 1, it is evident that integrating both attention mechanisms, CLAB and SLAB, significantly enhances positioning accuracy. The RMSE can be reduced to 7 cm. Figure 7 illustrates the RMSE variation over epochs without additional interference sources for different models under a 15% noise level. The results indicate that all models experience an initial sharp decline in RMSE, followed by stabilization as training progresses. The ResNet model with CLAB and SLAB mechanisms achieves the lowest RMSE and demonstrates the best convergence stability, suggesting that combining both channel and spatial attention mechanisms effectively enhances feature extraction and noise resilience. In brief, it has been observed that the standalone CLAB and SLAB models show improvement over the baseline ResNet. Additionally, the hybrid model outperforms both, confirming the complementary benefits of integrating both attention mechanisms. We are currently developing tools to visualize the learned attention weights from CLAB and SLAB in different indoor scenes. Preliminary results show that CLAB tends to emphasize frequency-domain feature channels that correspond to dominant signal components. SLAB highlights regions that spatially align with the anchor point, likely correlating with areas of signal strength variation due to multipath effects. In some wireless communication systems, interference may come from multiple independent radio sources. The impact of these interference sources can be approximated as a Gaussian distribution. As a result, we use 20% Gaussian interference to evaluate our model. Figure 8 shows the RMSE performance with 20% interference under different noise levels. Figure 7: The RMSE versus epoch without interference. Table 2 presents the RMSE performance under different noise levels with an additional 20% interference. The results also indicate that both CLAB and SLAB mechanisms contribute to reducing the RMSE value. Compared to Table 1, the presence of interference significantly increases the RMSE across all models, indicating that interference degrades positioning accuracy. Nevertheless, ResNet integrated with the CLAB and SLAB model still achieves the best performance, demonstrating its robustness against interference. In the 10% noise scenario, RMSE for ResNet increases from 0.0969 m (Table 1) to 0.1546 m (Table 2) due to interference, reflecting the impact of interference. However, applying CLAB or SLAB reduces RMSE, and their combination, CLAB and SLAB, achieves the lowest RMSE at 0.0673, an improvement of 50.5% compared to the baseline ResNet.