## 4 Experimental Results and Discussion
The results show that the BER for the 10 Gbaud data reached the HD-FEC threshold of 3.8 \(\times\) 10\({}^{-3}\), while the BERs for the 20 Gbaud and 30 Gbaud data reached the 20% SD-FEC threshold at 2.0 \(\times\) 10\({}^{-3}\). The neural network demonstrated significant effectiveness in processing signals for the 2 \(\times\) 2 MIMO D-band photonic-assisted terahertz communication system, providing an effective means to improve system performance. However, during the training process, to capture complex patterns and features in the data, the network learns a large number of weights and neurons. Some of these weights and neurons may be redundant, meaning they can be removed without significantly affecting the network's performance. Pruning, as an optimization technique, can reduce the network's complexity by removing these redundant weights and neurons, thereby lowering storage requirements, reducing computational resource demands, and improving runtime efficiency and generalization capabilities. Figure 7 illustrates the network sparsity results of a three-layer DNN neural network after iterative pruning, using V-pol as an example. We employed a standard deviation-based weight pruning method, with each pruning round followed by 10 learning epochs, and set a threshold ratio of 0.8, where the pruning threshold equals the threshold ratio multiplied by the standard deviation. After 20 iterations, we observed that the model's sparsity gradually converged. Notably, the sparsity of the I path after pruning was approximately 8% higher than that of the Q path, and it significantly decreased with increasing signal rate. This is attributed to the fact that, during signal transmission, the Q path may have experienced more severe distortions and noise effects compared with the I path. The path differences between the I and Q paths led the neural network to process the I path data with more redundant information, hence exhibiting higher sparsity post-pruning. Additionally, as the signal rate increases, the network sparsity obtained through pruning decreases. This is because higher-rate signals are subject to greater inter-symbol interference and nonlinear effects during transmission, necessitating the retention of more weights by the network to capture these details, resulting in lower sparsity. Furthermore, we observed that the sparsity of the fc1 layer is significantly greater than that of the fc2 layer. This is likely because the fc1 layer serves as the initial layer of the network, potentially responsible for capturing more general features of the input signal, which are more redundant and thus more prone to pruning. In contrast, the fc2 layer may be responsible for more specific feature extraction, which is more critical to the network's final output, so more weights are retained during pruning, leading to lower sparsity. In Figure 8, we further explored the impact of varying threshold ratios (from 0.1 to 1.0) on the sparsity of I and Q path signals at different rates (10 Gbaud, 20 Gbaud, 30 Gbaud) and different polarization directions (V-pol and H-pol). The experimental results indicate that after 20 rounds of iterative pruning, the black and blue lines, representing the I path signals of V-pol and H-pol, respectively, exhibit significantly and consistently higher sparsity  
Figure 7: V-pol neural network pruning rounds vs. sparsity: (**a**) 10 Gbaud-I, (**b**) 10 Gbaud-Q, (**c**) 20 Gbaud-I, (**d**) 20 Gbaud-Q, (**e**) 30 Gbaud-I, and (**f**) 30 Gbaud-Q. than the red and green lines, which represent the Q path signals, in agreement with the findings in Figure 7. Moreover, the sparsity of H-pol signals is notably greater than that of V-pol signals. This discrepancy is attributed to the distinct propagation characteristics of signals under different polarization states; for instance, in wireless communications, horizontally polarized signals may be more susceptible to ground reflections, whereas vertically polarized signals may be less sensitive to such reflections.