# Indoor Localization Using 6G Time-Domain Feature and Deep Learning
## 4 Numerical Results
At the 15% noise level, RMSE for the baseline ResNet rises from 0.1046 m (Table 1) to 0.1631 m (Table 2), further confirming the negative effect of interference. Even so, the CLAB and SLAB mechanisms still provide improvements by reducing RMSE to 0.0768, achieving a 54.3% improvement over the baseline ResNet. At 20% noise, RMSE degradation is most pronounced, with ResNet increasing from 0.1298 m (Table 1) to 0.1898 m (Table 2). Despite this, ResNet with CLAB and SLAB still effectively suppresses the error, achieving an RMSE of 0.0825 m, which represents a 56.5% improvement compared to ResNet. The above results demonstrate that both CLAB and SLAB effectively mitigate noise interference, and their combination provides the most significant improvement in positioning accuracy. The findings highlight the importance of integrating spatial and channel attention mechanisms in challenging environments. Figure 9 presents the RMSE versus epoch results for the same models under a 15% noise level, with an additional 20% interference source. Compared to Figure 7, the RMSE values are generally higher in the initial training phase due to the added interference, which introduces additional challenges in learning accurate feature representations. However, as training progresses, RMSE stabilizes, with the ResNet model incorporating CLAB and SLAB still achieving the best overall performance. The gap between the baseline ResNet and the models utilizing attention mechanisms widens, highlighting that attention-based \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Noise Level** & **Algorithm** & **ResNet with CLAB** & **ResNet with SLAB** &
\begin{tabular}{c} **ResNet with** \\ **CLAB and SLAB** \\ \end{tabular} \\ \hline
10\% noise & 0.1546 & 0.0916 & 0.0914 & 0.0673 \\
15\% noise & 0.1631 & 0.1047 & 0.1086 & 0.0768 \\
20\% noise & 0.1898 & 0.1124 & 0.1165 & 0.0825 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: RMSE (m) performance with 20% interference under different noise levels. Figure 8: The RMSE versus noise levels with interference. architectures are more robust against interference. Notably, the hybrid CLAB and SLAB model not only achieves lower RMSE but also maintains a more stable convergence trend, indicating its superior capacity to mitigate interference effects while preserving localization accuracy. As shown in Figures 7 and 9, models equipped with CLAB and SLAB exhibit faster and more stable convergence trends compared to the baseline ResNet. In particular, the hybrid model with both CLAB and SLAB not only achieves the lowest RMSE but also shows less fluctuation during training, indicating better learning stability and robustness to noise and interference. The loss consistently decreases within the first 50 epochs and stabilizes afterwards, suggesting that our attention-augmented architectures can converge effectively and reliably. In relation to the overall training duration for deep learning, our experiments are conducted on the same hardware configuration: a personal computer equipped with a 3.8 GHz Intel Core i7 processor, 64 GB RAM, and an NVIDIA RTX 4060 12 GB GPU. The training time for the baseline ResNet model is approximately 150 minutes. In comparison, the ResNet with CLAB requires an average of 155 min, and the model with SLAB needs the training time to approximately 160 min. The model with both CLAB and SLAB needs the training time to approximately 165 min. The computation time for training each model is shown in Table 3. Although training requires several hours, once the models are trained, they can perform inference in less than 1 s, making the proposed method suitable for real-time indoor localization applications.