## 3 Experiments  
### Experimental Setup  
To validate the efficacy of the proposed method, experiments are conducted on synthetic dataset. In-depth comparative experiments are conducted based on the fundamental RIMformer architecture, and a comprehensive ablation study is carried out to analyze the role of each lightweight design element and the impact of KD. We compare our method across models of varying scales and reported performance metrics. Simulation datasets are constructed based on the existing FMCW mmWave radar systems and possible interference scenarios. The specific parameters are shown in Table 1.  
The primary metrics used for the evaluation of interference mitigation performance are the signal-to-interference plus noise ratio (SINR) and the peak signal-to-interference plus noise ratio (PSINR). The SINR measures the power ratio of the reconstructed target signal to the sum of interference and noise, which provides a metric to evaluate the quality of the reconstructed target signal in the presence of interference and noise. The SINR can be calculated as follows:  
\[\text{SINR}=10\log_{10}\biggl{(}\frac{s^{2}}{(s-\hat{s})^{2}}\biggr{)}, \tag{13}\]  
where \(s\) and \(\hat{s}\) are the reference signal and the reconstructed target, respectively.  
The PSINR is defined as the ratio of the mean power across the target peaks and the mean power of interference and noise in the frequency domain, which can be calculated as follows:  
\[\text{PSINR}=10\log_{10}\biggl{(}\frac{\frac{1}{\mathcal{R}_{T}}\sum_{i\in \mathcal{T}}|s[i]|^{2}}{\frac{1}{\mathcal{R}_{N}}\sum_{i\in\mathcal{N}}|s[i]|^ {2}}\biggr{)}, \tag{14}\]  
where \(i\) is the index of the signals \(s\); \(\mathcal{T}\) is the set of target peaks; \(\mathcal{N}\) is the set of background noise and noise caused by interference; and \(\mathcal{\#}\) denotes the cardinality of a set.  
For the fairness of the experiments, the same hyperparameters are used for all training processes. Specifically, models are trained using the Adam optimizer, with 600 epochs of cosine learning rate decay and 50 epochs of linear warm-up. The base learning rate is set to \(1\times 10^{-4}\) and the batch size is 32. Additionally, a KD weight of 0.5 is employed to calculate the final loss. Both interference mitigation performance and inference latency are tested across multiple hardware platforms, including a server CPU (AMD EPYC 7F72), a GPU (Nvidia RTX 4090), and a low-power edge computing platform (Jetson Orin NX 8 GB).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Parameter** & **Value** & **Parameter** & **Value** \\ \hline \multirow{4}{*}{Target Radar} & StartFreq & \multirow{2}{*}{77.0} & \multirow{2}{*}{Samples} & \multirow{2}{*}{1024} \\  & (GHz) & & & \\  & Duration (\(\mu\)s) & 20 & Chirps & 128 \\  & Slope & 30 & SNR (dB) & (\(-\)30, 20) \\  & (MHz/\(\mu\)s) & & & \\ \hline \multirow{2}{*}{Target} & Number & (2, 5) & Speed (m/s) & (\(-\)35, 35) \\  & Range (m) & (0, 40) & Amplitude * & (0.4, 3) \\ \hline \multirow{2}{*}{Interfering Radar} & Number & (3,7) & Slope & \multirow{2}{*}{(\(-\)72.5, 72.5)} \\  & StartFreq & & & \\  & (GHz) & & & \\ \hline \hline \end{tabular}  
* The amplitudes are relative values.  
\end{table}
Table 1: Parameter configuration for dataset construction.