# Research on Data Link Channel Decoding Optimization Scheme for Drone Power Inspection Scenarios
## 1 Introduction
When drones perform inspection tasks in complex environments, they need to maintain real-time wireless connectivity with ground servers and drone relays, and transmit a large amount of data, such as captured power facility images, to ground servers. This requires the data link to have high transmission efficiency and reliability. Therefore, the primary focus lies in exploring methods and techniques that enable the efficient and reliable transmission in data links. In the drone cluster relay system, data communication between drones or between drones and ground servers is susceptible to channel interference and additive noise. In order to improve the reliability of the data link, channel encoding, also known as error control encoding, is usually used. This article aims to focus on the research of channel encoding and decoding technology for the data link between drones and drone relays. Convolutional code, turbo code and low-density parity-check (LDPC) code are commonly used for data channel coding and decoding. In power inspection scenarios, some drones serve as relays for auxiliary communication, necessitating the encoding and decoding of a substantial amount of received power data. Due to the limited payload and computing power of drones, the channel encoding and decoding algorithms for drone relays must have the characteristics of low complexity, fast computation speed, and high reliability. Therefore, a low-complexity and high-reliability encoding and decoding method for drone data links is needed. Research has shown that turbo codes exhibit excellent performance even under low signal-to-noise ratio conditions. [PERSON] et al. [10] point out turbo codes can achieve high decoding performance in the presence of Gaussian white noise interference. But turbo decoding algorithms are characterized by low throughput and complex implementation [11]. This limits their applicability in drone communication scenarios. LDPC codes not only possess strong error correction capabilities and are easy to perform parallel operations, but also are closest to the Shannon limit and have low decoding complexity [12; 13]. A performance comparison of the three channel coding and decoding schemes is shown in Table 1.  
Therefore, LDPC codes are considered the optimal data channel coding scheme for UAV data links. Utilizing LDPC codes can enhance the reliability of data transmission in drone relay communication and improve the resistance to interference. In order to better meet the high reliability and low complexity requirements of unmanned aerial vehicle data link transmission, this study discusses LDPC decoding algorithms, with a focus on the trade-off between LDPC decoding performance and computational complexity. LDPC codes were initially proposed by [PERSON] in 1962 [14]. The main LDPC decoding algorithms include belief propagation (BP) and min-sum (MS) algorithms. The BP decoding algorithm exhibits excellent performance but requires extensive logarithmic and multiplication operations, resulting in high computational complexity. In contrast, the MS decoding algorithm significantly reduces computational complexity at the expense of performance degradation. Furthermore, the MS algorithm can be improved by using correction factors to adjust the calculation results of the checking nodes, known as the normalized MS (NMS) algorithm or offset MS (OMS) algorithm [15; 16]. These decoding algorithms can improve decoding performance and reduce complexity [17]. The field of UAV data link communication faces multiple technological challenges, including high-speed motion and Doppler effects, channel fading, signal interference and noise, limited bandwidth and spectrum scarcity, resource constraints, rapid deployment, and dynamic network topologies, as well as data transmission security and privacy protection. These challenges collectively constitute the complexity of ensuring reliability, performance, and security in UAV data transmission, necessitating innovative channel decoding algorithms and communication technologies to address them.