## 4 Experimental Results and Discussion
Additionally, as the threshold ratio increases, the sparsity achieved through pruning initially rises and then declines. This phenomenon occurs because, at lower threshold ratios, fewer weights are removed in each pruning round, resulting in insufficient pruning. As the threshold ratio gradually increases, more weights are removed, leading to a gradual rise in sparsity. However, when the threshold ratio becomes excessively high, an excessive number of important weights may be removed, and these weights are likely to be restored during the retraining process, leading to redundancy. Hence, the sparsity begins to decrease. Furthermore, we observed that different rates of signals achieve their maximum sparsity at distinct threshold ratios. Specifically, for 10 Gbaud signals, the threshold ratio is 0.8. For 20 Gbaud signals, the threshold ratio is 0.7. For 30 Gbaud signals, the I path threshold ratio is 0.7, and the Q path threshold ratio is 0.6. This variation is due to the fact that low-rate signals experience less inter-symbol interference and nonlinear effects, requiring fewer features from the network and containing more redundant weights that are easier to restore network performance during retraining, thus allowing for a higher pruning threshold. Conversely, for high-rate signals, the network requires more weights to capture the characteristics of the signals; hence, a lower pruning threshold should be applied. As mentioned in Figure 7, the I path signals experience significantly less inter-symbol interference and nonlinear effects compared with the Q path, and at a transmission rate of 30 Gbaud, the pruning threshold for the I path is greater than that for the Q path, further corroborating this observation. In Figure 9, we further explore the impact of varying threshold ratios (from 0.1 to 1.0) on the BERs of signals at different rates (10 Gbaud, 20 Gbaud, 30 Gbaud) and different polarization directions (V-pol and H-pol), as well as the effect of retraining epochs (epochs = 5, 10, 15) after pruning on the BER trend. After 20 rounds of pruning, it was found that setting the retraining period to 10 epochs resulted in the lowest BER. This may be due to the fact that 10 epochs represent a balance point, allowing the network to maintain generalization capability while improving performance. For 10 Gbaud signals, the BER curve generally shows that as the threshold ratio increases, the BER gradually rises. At a threshold ratio of 0.1, the BER can be reduced by approximately 37%. However, as the threshold ratio increases, the BER gradually rises until it reaches the same level as before pruning at a threshold ratio of 0.8. For 20 Gbaud and 30 Gbaud signals, the BER first decreases and then increases with the rise in the threshold ratio. For 20 Gbaud signals, the minimum BER is achieved at a threshold ratio of 0.7. For 30 Gbaud signals, the minimum BER for V-pol signals is achieved at a threshold ratio of 0.7, while for H-pol signals, it is achieved at a threshold ratio of 0.6. At these threshold ratios, the minimum BERs after pruning are all around 2.5 \(\times\) 10\({}^{-3}\), meeting the hard decision threshold. Figure 8: Neural network pruning threshold ratio vs. sparsity: (**a**) 10 Gbaud, (**b**) 20 Gbaud, and (**c**) 30 Gbaud. The gradual rise in BER for 10 Gbaud signals with an increasing threshold ratio may be because there are fewer features of low-rate signals, and the network before pruning has a certain degree of overfitting. Pruning eliminates these redundant weights, leading to an increase in the error rate, but this can enhance the network's adaptability. For 20 Gbaud and 30 Gbaud signals, the BER first decreases and then increases because, at lower threshold ratios, the network accurately prunes weights that contribute less to the output and further optimizes the distribution of weights through retraining, thereby reducing the BER.