## 5 Results and Discussion
### Ablation Study  
In order to demonstrate that the proposed CAE-LSTM-Net-based decoding algorithm has a state-of-the-art performance, we experimentally investigate the performance improvement brought by the LSTM-based equalizer and channel-attention-Net-based encoder. We first perform experimental comparison at two different conditions: one is with an LSTM-based equalizer, and the other is without an LSTM-based equalizer. In the second condition, we place the Softmax layer following the channel-attention-Net-based encoder, and we use its output as the classification result at time step \(t\). Additionally, the traditional equalizer based on ANN is also investigated for comparison. Figures 13 and 14 illustrate the comparison of BER performance and data rate performance at a symbol rate from 32 to 46 kbaud/s. The BER performance of the scheme with a channel-attention-Net-based encoder and ANN-based equalizer is better than that without an ANN-based equalizer, but the difference between them is not apparent. The reason is that each classification problem is separate, which means that a sharp accuracy deterioration will happen when most of the pixels in the sub-image stand for the wrong symbol due to ISI. Using the proposed CAE-LSTM-Net, the short-term memory of LSTM is appropriately utilized to mitigate decoding system performance deterioration. As shown in Figure 13, using CAE-LSTM-Net, the BER can be reduced from \(9.23\times 10^{-3}\) to \(3.61\times 10^{-3}\) at a symbol rate of 46 kbaud/s, and BERs are meeting the pre-forward error correction (FEC, \(BER=3.8\times 10^{-3}\)) requirement in all cases. As shown in Figure 14, the proposed LSTM-based equalizer outperforms those with an ANN-based equalizer or without a specific equalizer in data rate performance. The data rate is significantly improved by using an LSTM-based equalizer. The reason is that the long-term memory of LSTM contributes to recognizing the specific symbol sequence of the packet header, which means that it can use the correctly recognized symbol sequence in the header to assist in identifying the rest of the parts. Through increasing the header recognition accuracy, more packets can be collected, and the data rate goes higher.  
Next, we investigate the sensitivity of BER and data rate performance to the identification ability of the encoder. Here, we build two CNN-based encoders for comparison, one is with the CAE-LSTM-Net-like structure but replacing the recalibration unit with a residual unit, and the other is with a structure comprised of basic convolutional layers, ReLU layers and BatchNormalizaiton layers such as LeNet-5, as shown in Figure 3. These encoders are named residual-Net and convolution-Net, respectively, and the scale of their weights are set to be the same level as that of channel-attention-Net for fair comparison. Figures 15 and 16 demonstrate the BER and data rate performance using encoders of different structures. As shown in Figure 15, the channel-attention-Net-based encoder significantly improves the BER performance in all cases of symbol rate. In a lower symbol rate, BER will not increase in a linear relationship with the increasing of symbol rate, and the residual-Net-based encoder can perform satisfactorily. However, when the symbol rate is higher than 40 kbaud/s, only the scheme with a channel-attention-Net-based encoder still meets the FEC requirement, which means that more sufficient feature extraction contributes to generating a descriptor with better quality and mitigating ISI. As shown in Figure 16, in the conditions of higher symbol rate, the channel-attention-Net based encoder outperforms in data rate. Better recognition of the header is the main reason for the increase of data rate.  
Figure 14: Data rate comparison by using different equalizers.  
Figure 13: BER performance by using different equalizers.  
Figure 15: BER performance by using different CNN-based encoders.