# Research on Data Link Channel Decoding Optimization Scheme for Drone Power Inspection Scenarios
## 1 Introduction
Addressing the requirements of simplicity, speed, and strong error correction capabilities for channel coding algorithms in UAV data link onboard terminals, we conducted an analysis of the coding characteristics suitable for UAV data link systems. [PERSON] et al. [18] introduced a new family of polar codes with a hybrid multikernel tailored for short blocklength transmissions, which is well suited for communicating control data between UAVs. As previously mentioned, LDPC codes are better suited for long-code transmissions, meeting the requirement of transmitting a large amount of power long-code data between drones in the scenario described in this paper. [PERSON] et al. [19] compared LDPC codes, convolutional codes, and turbo codes, with simulation results consistently demonstrating \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Performance** & **LDPC Code** & **Turbo Code** & **Convolutional Code** \\ \hline Error correction ability & Pretty strong & Pretty strong & Strong \\ \hline Fragrance limit difference & 0.0045 dB & 0.5 dB & \textgreater{}4 dB \\ \hline Encoding and decoding complexity & Medium & Complex & Simple \\ \hline Anti-interference ability & Strong & Strong & Average \\ \hline Throughput & \textgreater{}100 Mbps & 1–100 Mbps & \textgreater{}100 Mbps \\ \hline Calculation & Parallel & Parallel & Parallel \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison of commonly used channel encoding and decoding schemes. the superior performance of LDPC codes. This makes them a preferred channel coding solution for UAV data link systems. In the context of UAV cluster data exchange, [PERSON] et al. [20] applied LDPC codes for error correction and improved the decoding algorithm through log-likelihood ratio (LLR) calculations based on the max-log-MAP criterion. Simulation results have shown its ability to meet the strong interference resistance and high channel utilization requirements, making it suitable for UAV cluster data links. Additionally, [PERSON] et al. [21] introduced a CEC-LDPC coding method, which was found to be better suited for UAV data link downlink applications. [PERSON] et al. [22] proposed an unmanned aerial vehicle telemetry and control transmission system based on Q-LDPC codes, and their simulation results demonstrated that this decoding scheme effectively combats fast fading, reducing the bit error rate and providing safety protection for UAV ground observations by converting continuous burst bit errors into a smaller number of symbol errors. However, it is worth noting that the complexity of Q-LDPC code decoding is relatively high, making it less suitable for resource-constrained UAV devices. The technologies mentioned thus far may face challenges related to high decoding complexity or relatively low decoding efficiency, which may not be suitable for the system model discussed in this paper. In comparison to previous research, our proposed enhanced LDPC-SNNMS decoding scheme introduces machine learning techniques into UAV data link channel decoding, leading to significant improvements in performance and reliability, and adaptability to the resource limitations of UAVs. Due to the high requirement for decoding performance in drone communication scenarios to ensure the reliability of the data link, traditional LDPC decoding algorithms may not be able to meet the desired performance. Considering that UAVs possess powerful computational capabilities and embedded systems, enabling them to execute complex algorithms, this study combines machine learning with traditional LDPC decoding algorithms to enhance decoding performance and meet the high reliability requirements of data transmission between unmanned aerial vehicles. Considering the limited computational resources and battery capacity of drones, this study focuses on the design of a deep-learning-based LDPC decoding algorithm that can achieve high performance while minimizing computational complexity, storage space, and energy consumption.