## 4 Experimental Results and Analysis  
In order to comprehensively evaluate the performance of the proposed two-stage equalization method, this section analyzes the signal characteristics before and after equalization in detail, and compares them with the traditional LSTM-based equalizer and Volterra nonlinear equalizer to evaluate the effectiveness of the three methods in suppressing nonlinear distortion. The BER of the three methods before and after equalization is shown in Table 2. It should be noted that due to factors such as nonlinearity, the dataset after data augmentation is more complex, so the Volterra equalizer and the traditional LSTM equalizer fail to learn the channel characteristics, so the BER is about 0.5. In the following discussion, we will compare the data before and after data augmentation to illustrate that our method has certain advantages before and after data augmentation, especially the latter; that is, when the nonlinear influence is large, it can still effectively equalize the received sequence. The final Losses of the three methods after data augmentation are Volterra: 0.112; traditional LSTM: 0.064; and two-stage LSTM equalization: 0.019. The Volterra equalizer uses a second-order kernel function commonly used in nonlinear FIR modeling, and its memory length is 15. At the same time, in order to better compare the two-stage time-domain equalization strategy with the traditional LSTM time-domain equalizer, we further evaluate its performance through the BER assisted by the signal waveform diagram and the constellation diagram, thereby qualitatively and quantitatively verifying the proposed method. Since the effect of the Volterra equalizer is similar to the conventional LSTM equalization result, the following discussion only compares the conventional LSTM with the two-stage time-domain equalization strategy. At the 230 GHz band with an LOS distance of 2.1 m, the received signals acquired after frame synchronization are shown in Figure 6b. Compared to the transmitted signals in Figure 6a, it is evident that the signals have been significantly affected by phase noise and hardware-induced nonlinearities during transmission. The demodulated BER reaches as high as 0.5072, further confirming that severe channel distortions have seriously degraded signal integrity. It is worth noting that, to clearly compare between the raw received signal, the signal processed by a conventional DL-based equalizer, and the signal processed by \begin{table}
\begin{tabular}{l l} \hline
**Parameter** & **Value** \\ \hline Neural Network Configuration & \\ \hline Equalizer input size & 25 samples \\ Input/Output dimension & 2/2 (I/Q components) \\ LSTM architecture & 4 layers, 128 hidden units per layer \\ Dropout & Applied between LSTM layers \\ Loss function & Mean Square Error (MSE) \\ Optimizer & Adam \\ Initial learning rate & 0.001 (with scheduling) \\ Weight decay & \(1\times 10^{-7}\) \\ Batch size & 64 \\ Max training epochs & 100 \\ Early stopping & Enabled (patience = 10, based on validation loss) \\ Data augmentation & Low-SNR noise (0–5 dB), applied to first stage \\  & only \\ \hline Hardware Environment & \\ \hline Training platform & NVIDIA RTX 3060 GPU \\ GPU memory & 8 GB \\ Framework & PyTorch 2.1.1 \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._the proposed two-stage time-domain equalizer, only a portion of the signal waveform is displayed in the time-domain plots. To highlight the advantages of the proposed method, the received signal is first directly processed by a traditional LSTM-based equalizer with the same architecture as the equalizer used in our model, without any upsampling. After equalization, the BER before and after data enhancement are 0.0022 and 0.4965, respectively. The results after data enhancement are shown in Figure 7a,d. From the time-domain waveform in Figure 7a, it can be seen that the signal amplitude fluctuates around zero.