## 3 Experiments
### Ablation Study of Lightweight Designs  
In this section, an ablation study is described to evaluate the impact of the different number of layers and lightweight modules, including the linear attention (LA) mechanism and deep separable convolution (DSC) blocks, on the performance of the network. All models are trained directly according to the parameters in Section 3.1 without distillation. In order to evaluate the size and computational complexity of the networks, the number of parameters, floating point operations (FLOPs), and the inference memory overhead are comprehensively compared in Table 2. In addition, the single inference elapsed time of different models is obtained on the three different platforms. To visualize the differences in interference suppression performance between the different models, the results are compared by using a set of range-Doppler (RD) maps of the output results, as shown in Figure 7.  
The efficacy of the introduced linear attention mechanism and depthwise separable convolutions is assessed for the lightweight design structure. The linear attention mechanism is implemented to replace the traditional attention mechanism, reducing the computational load from quadratic to linear complexity with respect to the sequence length. Depthwise separable convolutions are used to reduce the number of parameters and the computational cost of convolutional layers, with a slight reduction in the model's ability to capture spatial features. The network with the linear attention mechanism and deep separable convolution has a significantly reduced number of parameters and inference time compared to the baseline RIMformer while maintaining its performance. The original RIMformer consisted of 362.99 M FLOPs, with an SINR of 11.68 dB, a PSINR of 21.83 dB, and an inference time on the Jetson platform of 177.24 ms. After introducing the linear attention mechanism, the FLOPs are reduced by approximately 48. 8%, and the inference time is decreased by 40.7%. However, the PSINR remains at 21.50 dB. In addition, the impact of the linear attention mechanism on memory overhead is significant, as it saves more than 1 GB of memory space. The depthwise separable convolution leads to a 12.6% reduction in FLOPs, with PSINR remaining stable at 21.31 dB. The combined application of both techniques results in a 61.4% reduction in parameters and a 45.7% reduction in inference time, with the PSINR improving to 21.41 dB. These results show that the linear attention mechanism and the depthwise separable convolution dramatically reduce the computational complexity of the network and decrease the computation time. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Layer**} & \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Parameter** **(K)**} & \multirow{2}{*}{**FLOPs** **(M)**} & \multirow{2}{*}{**Memory** **(MB)**} & \multirow{2}{*}{**PSINR** **(dB)**} & \multirow{2}{*}{**SINR** **(dB)**} & **GPU** & **CPU** & **Jetson** \\  & & & & & & & & **Time** & **Time** & **Time** \\  & & & & & & & & **(ms)** & **(ms)** \\ \hline \multirow{4}{*}{6} & Baseline & 124.94 & 362.99 & 1562.11 & 21.83 & 11.68 & 17.61 & 484.65 & 177.24 \\  & DSC & 113.71 & 317.29 & 1559.22 & 21.31 & 11.03 & 17.29 & 476.29 & 168.62 \\  & LA & 123.50 & 185.98 & 182.95 & 21.50 & 11.54 & 26.08 & 84.46 & 105.04 \\  & DSC + & 112.26 & 140.29 & 180.07 & 21.41 & 11.37 & 25.83 & 82.17 & 96.31 \\ \hline \multirow{4}{*}{2} & - & 41.67 & 121.08 & 505.67 & 19.15 & 9.58 & 6.03 & 154.70 & 59.90 \\  & DSC & 37.93 & 105.85 & 503.71 & 18.59 & 9.43 & 5.88 & 151.62 & 59.45 \\  & LA & 41.19 & 62.08 & 46.95 & 18.93 & 8.95 & 8.68 & 28.34 & 34.18 \\ \cline{1-1}  & DSC + & 37.45 & 46.85 & 44.99 & 18.73 & 8.38 & 8.50 & 26.78 & 33.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Main results of ablation experiments. Models with different number of layers and different lightweight designs are combined to compare their interference mitigation performance and efficiency.