## 4 Experimental Results and Analysis
The constellation diagram in Figure 7d further shows that the model fails to learn the nonlinear distortion characteristics of the THz channel and the superimposed data enhancement processing to which the transmitted signal is subjected. Subsequently, without modifying the LSTM architecture, we upsample the transmitted and received signals and apply the first-stage (global) equalization. The resulting time-domain waveforms are shown in Figure 7b. The waveforms after equalization show improved alignment with the original transmitted sequences in terms of both trend and amplitude. BER analysis indicates that the BER decreases to 0.1816 after the first-stage equalization, demonstrating the effectiveness of upsampling and the global equalizer in enhancing signal quality. However, the downsampled constellation diagram in Figure 7e reveals that while the overall constellation shape shows improved phase and amplitude recovery, some amplitude fluctuations remain. This suggests that although the first-stage equalizer is effective in correcting coarse signal distortions, it is insufficient in compensating for subtle nonlinear noise and residual distortions. The downsampled signal is subsequently processed by the second-stage (local) equalizer. As shown in Figure 7c,\(I\), the time-domain waveform after the second-stage equalization closely matches that of the transmitted signal, indicating a significant improvement in equalization performance. A comparison of the constellation diagrams before and after the second stage (Figure 7c vs. Figure 7f) reveals that the constellation points become more concentrated after the second-stage equalizer. The BER is further reduced from 0.1816 to 0.0398, demonstrating that the second-stage equalizer successfully compensates for most of the residual distortions and achieves the desired equalization effect. These results confirm that the proposed two-stage time-domain equalization method effectively implements a coarse-to-fine signal processing strategy. In addition, compared to conventional DL-based equalizers, the proposed method shows better adaptability across a wider range of channel conditions. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **Config** & **BER (230 GHz (DA/NDA))** & **BER (310 GHz)** \\ \hline Traditional DL & (4, 128, 25) & 0.4965/0.0022 & 0.0080 \\ Two-stage DL (S1) & (4, 128, 25) & 0.1816/0.0013 & 0.0040 \\ Two-stage DL (Final) & (4, 128, 25) & 0.0398/0.0002 & 0.0020 \\ Volterra & (\(\
eg\), \(\
eg\), 25) & 0.5003/0.0110 & 0.0107 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of equalization methods at 230 GHz and 310 GHz. All LSTM models use \(H=4\) hidden layers, \(L=128\) hidden units per layer, and \(T=25\) input length. Two-stage deep learning equalizers are trained in two stages. Volterra equalizer uses a second-order kernel with a memory length of 15. Abbreviations: Config represents the network structure in the format of (\(H\), \(L\), \(T\)), where \(H\) is the number of hidden layers, \(L\) is the number of LSTM units, and \(T\) is the input sequence length. DA stands for data augmentation, and NDA stands for non-data augmentation. To further verify the robustness of the model, additional test data were collected at 310 GHz with a 1.5 m LOS channel. To prevent the two-stage model from memorizing training sequences rather than learning nonlinear channel characteristics, the new data were LDPC-encoded using an irregular parity-check matrix during transmission. The BER of the original, unprocessed signal is 0.012. After the first stage of equalization, the BER drops to 0.004, though slight distortions remain. Following the second stage of equalization, the BER is further reduced to 0.002, and the constellation points appear more concentrated, indicating improved signal quality. These results confirm that the two-stage equalizer can effectively enhance signal transmission under various frequencies and distances in complex THz channels.