# Signal Detection Based on Separable CNN for OTFS Communication Systems
## 6 Experimental Result  
The dataset used in this paper is generated using MATLAB R2022b [24], consisting of 100,000 samples divided into training, validation, and testing at a ratio of 6:3:1. The simulation parameter settings are shown in Table 1. The subcarrier spacing is set to 15 kHz within a 10 MHz bandwidth. To evaluate the BER of the signal detection against 2D-CNN, a range of SNRs between 0 dB and 20 dB is used with a step size of 2.5 dB. The SeCNN-OTFS is trained with a batch size of 2048, where each sample within a batch corresponds to a unique DD domain channel realization. These realizations are independently generated using random path gains and Doppler-delay indices, ensuring high diversity in the training dataset and allowing the network to generalize well across a wide range of channel conditions. The learning rate is set to 0.01 for a total of 2000 training iterations. A stochastic gradient descent (SGD) optimizer is employed to speed up the convergence. The simulation environment is built on Pycharm Community Edition (python 3.6, Tensorflow 2.15.0). The parameter configuration of kernel sizes and network layers is summarized in Table 2. All separable convolutional layers use a kernel size of \(3\times 3\), and the number of filters gradually decreases in the last three layers, with the final layer containing only one filter to match the output dimensions. To ensure a fair comparison, we construct CNN-OTFS with the same architecture as SeCNN-OTFS, replacing the separable convolutional layers with standard convolutional layers. Similarly, 2D-CNN [24] is included as a baseline model. Also, two classical linear estimation methods of LS and minimum mean square error (MMSE) are introduced as baseline references. The total number of parameters in 2D-CNN, CNN-OTFS, and SeCNN-OTFS are 81,085, 99,501, and 15,511, respectively. Overall, SeCNN-OTFS significantly reduces both computational and parameter cost compared to CNN-based baselines, thereby improving model efficiency and deployment feasibility. The BER performance of different methods under various signal-to-noise ratio (SNR) conditions is shown in Figure 4. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Carrier frequency & 4 GHz \\ Subcarrier spacing & 15 KHz \\ OTFS frame size \((N,M)\) & (8, 8) \\ Configuration \((N_{R}\times N_{T})\) & \(1\times 1\) \\ Modulation scheme & 64-QAM \\ Number of path & P = 4 \\ Maximum speed & 500 Kmph \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameter settings. Simulation results indicate that the LS method, due to its lack of consideration for noise statistics, yields the highest BER across all SNR ranges. The MMSE offers a significant improvement over LS in the low and medium SNR regions, but its performance saturates at high SNRs, where the BER cannot be further reduced. In contrast, all three deep learning-based models outperform the classical methods across the full SNR ranges. Notably, 2D-CNN and SeCNN-OTFS share roughly the same BER at high SNRs (over 12.5 dB), while 2D-CNN outperforms SeCNN-OTFS at low SNRs. This is because at high SNR, the parameter efficiency of SeCNN-OTFS is sufficient to learn channel features with less noise, achieving a performance comparable to 2D-CNN. Although SeCNN-OTFS exhibits a slightly inferior BER performance compared to CNN-OTFS across all SNR levels, it achieves a substantial reduction in model complexity. Specifically, the number of parameters in SeCNN-OTFS is only approximately 15% that of CNN-OTFS, and 19% that of 2D-CNN, demonstrating a favorable balance between performance and computational efficiency, as shown in Table 3. Nevertheless, CNN-OTFS may be preferred in scenarios where higher accuracy is required and computational resources permit. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline K1 & K2 & K3 & K4 & K5 & K6 & K7 \\ \hline
3 & 3 & 3 & 3 & 3 & 3 & 3 \\ \hline C1 & C2 & C3 & C4 & C5 & C6 & C7 \\ \hline
32 & 64 & 32 & 32 & 16 & 4 & 1 \\ \hline \hline \end{tabular}
\end{table}