## 4 Discussion  
Performance Across Different Input SNRs: As shown in Figure 15, the effects of the noise level in the interfered signal on the interference mitigation performance are investigated. The results show that the network maintains high performance even though the input SNR varies. This ability is crucial for practical applications where the environmental conditions can cause significant fluctuations in signal quality. The proposed method consistently outperforms the other three methods at different input SNRs. When the input SNR is a relatively high value, the proposed lightweight  
Figure 14: The time-domain waveforms and range profiles in measured experiments. (**a**) The time-domain waveforms without and with interference in scenario 1. (**b**) The time-domain waveforms without and with interference in scenario 2. (**c**) The range profiles without and with interference in scenario 1. (**d**) The range profiles without and with interference in scenario 2. (**e**) Range profiles comparison after interference suppression in scenario 1. (**f**) Range profiles comparison after interference suppression in scenario 2. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**PSINR (dB)** & **Input** & **WD** & **ANC** & **BiGRU** & **Baseline Model** & **Lightweight Model** \\ \hline Scenario 1 & 14.38 & 10.98 & 15.14 & 14.25 & 16.07 & 15.43 \\ Scenario 2 & 0.42 & 8.82 & 8.919 & 13.63 & 15.51 & 15.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Quantitative results of different interference suppression methods in measured experiments. RIMformer exhibits excellent interference suppression performance. However, when the SNR is extremely low, the correlations among the distinct distance elements of IF signal becomes weak, and the network captures limited interference features. Therefore, the interference cannot be completely suppressed. Robustness Under Various Radar and Interference Conditions: The results of the measured experiments indicate that the lightweight model effectively translates from simulated to real-world scenarios and achieves good performance metrics. To further validate the robustness of the lightweight model, the dataset is reconstructed to cover a wider range of radar parameter configurations and more challenging interference conditions. Compared to the parameter configurations in Section 3, the start frequency, duration, and slope of the victim radar are adjusted from single fixed values of 77 GHz, 20 \(\upmu\)s, and 30 MHz/\(\upmu\)s to commonly used ranges of (76.5 GHz, 77.5 GHz), (10 \(\upmu\)s, 40 \(\upmu\)s), and (10 MHz/\(\upmu\)s, 60 MHz/\(\upmu\)s), respectively. Additionally, the number of interfering radars is extended from a range of (3, 7) to (1, 10). The lightweight RIMformer and other methods are applied to suppress interference in the reconstructed dataset. The quantitative results in Table 6 demonstrate the lightweight model's suitability for a broader range of radar parameter configurations and robustness under more challenging interference conditions. Potential Limitations: Although the proposed lightweight design and two-stage KD in the RIMformer network offer substantial improvements in model size and inference time, several avenues remain open for future research, including the following: (1) In extremely low SNR conditions, the interference mitigation performance of the network is less pronounced. This suggests that the network might struggle with input signals that are heavily corrupted by noise and require additional pre-processing steps to enhance the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Input** & **WD** & **ANC** & **BiGRU** &
\begin{tabular}{c} **Lightweight** \\ **RIMformer** \\ \end{tabular} \\ \hline PSINR (dB) & 2.29 & 9.37 & 8.21 & 16.64 & 20.22 \\ SINR (dB) & \(-\)21.4 & \(-\)12.07 & \(-\)17.25 & 5.25 & 8.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of results for WD, ANC, and proposed distilled lightweight RIMformer with different deployment modes.