# Research on Data Link Channel Decoding Optimization Scheme for Drone Power Inspection Scenarios
## 4 Discussion  
In this paper, the proposed new-SNNMS algorithm exhibits superior performance compared to the SNNMS, NNMS, and NNMS algorithms. This may be attributed to the following points: Firstly, similar to the SNNMS decoding network, this decoding algorithm also maintains closer adherence to the original LDPC code structure in terms of its model mechanism. The proposed improved algorithm assigns different learnable parameters to the messages calculated by each variable node. This allows the algorithm to more flexibly adapt to complex data patterns and relationships, enabling finer parameter adjustments and, thus, enhancing the model's representation ability. Therefore, the model  
Figure 10: BER performance of shared-NNMS-CQ under different quantization settings.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Item** & **NMS** & **NNMS** & **SNNMS** & **Shared-NNMS-CQ** \\ \hline CMP/XOR & \(\sum_{j=1}^{N-k}a_{j}\Big{(}a_{j}-2\Big{)}T\) & \(\sum_{j=1}^{N-k}a_{j}\Big{(}a_{j}-2\Big{)}T\) & \(\sum_{j=1}^{N-k}a_{j}\Big{(}a_{j}-2\Big{)}T\) & \(\sum_{j=1}^{N-k}a_{j}\Big{(}a_{j}-2\Big{)}T\) \\ \hline ADD & \(\sum_{i=1}^{N-k}(b_{i}(b_{i}-1)T+1)T\) & \(\sum_{i=1}^{N-k}(b_{i}(b_{i}-1)+1)T\) & \(\sum_{i=1}^{N-k}(b_{i}(b_{i}-1)+1)T\) & \(\sum_{i=1}^{N-k}(b_{i}(b_{i}-1)+1)T\) \\ \hline ML & T & \(\sum_{j=1}^{N-k}a_{j}\Big{(}a_{j}-2\Big{)}T+\) & 2T & \(\sum_{j=1}^{N-k}a_{j}\Big{(}a_{j}-2\Big{)}T+T\) \\  & & \(\sum_{i=1}^{N}b_{i}(b_{i}-2)T\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Complexity comparison of LDPC decoders.  
exhibits higher accuracy and stronger generalization ability, resulting in more favorable training results.  
By utilizing the proposed codebook-based weight quantization scheme to quantize the weights of our improved LDPC decoding network, we observed that this method even outperforms the unquantized BER performance. This may be because the quantization method can be seen as a regularization measure in the neural network. This method helps to reduce the risk of overfitting by reducing the representation ability of parameters, meaning that by reasonably reducing the type and accuracy of weights, the model is more inclined to learn simple and universal features. The effect of reducing overfitting may lead to better accuracy in test data, thus playing a positive role in improving performance. This scheme can effectively reduce the memory overhead and computational complexity of the proposed improved algorithm.  
The experimental results have verified the applicability of our proposed LDPC decoding algorithm, which can be effectively applied to drones and improve the reliability of drone data link communication. This achievement in efficient and reliable communication transmission can bring numerous advantages for UAV power patrol, such as ensuring data integrity, enabling real-time monitoring and response, enhancing fault diagnosis and maintenance efficiency, and providing better security measures. And to a significant extent, it has significantly contributed to the real-time monitoring and response, fault diagnosis, and maintenance efficiency, as well as the overall safety of drone power inspection, thereby improving the operational efficiency of power companies, reducing maintenance costs, and improving the reliability and safety of power systems. Although the proposed decoding scheme demonstrates better BER performance, it still has relatively higher complexity compared to SNNMS. In future work, we aim to focus on complexity reduction and explore further improvement methods to achieve lower complexity and higher reliability in channel coding and decoding schemes, ensuring lighter and more reliable data transmission between UAVs. While the proposed decoding scheme exhibits better BER performance, it still demonstrates relatively higher complexity compared to SNNMS. In future work, our goal is to focus on reducing the complexity and exploring further improvement methods to design lighter and more reliable decoding schemes for unmanned aerial vehicle data link channels.