# Research on Data Link Channel Decoding Optimization Scheme for Drone Power Inspection Scenarios
## 1 Introduction
There are two methods in deep learning: model-driven methods and data-driven methods. [PERSON] et al. [23] proposed an iterative BP-CNN architecture that connects a trained CNN with a BP decoder to improve the BER performance. [PERSON] et al. [24] addressed short codes and introduced a deep-learning-based decoder that improves BER performance through training on a large number of codewords. [PERSON] et al. [25] combined the traditional BP algorithm with the FGNN algorithm [26]. These data-driven methods show excellent training performance for short codewords. In the context of drone power inspection scenarios, the collected data primarily consist of long code, such as power facility images. The data-driven approach relies on feeding a large amount of data to train neural network models, which results in high computational complexity and increased resource consumption. This makes the data-driven methods less suitable for channel decoding algorithms applied to drone scenarios. However, the model-driven methods can effectively address this issue. In [27; 28], a model-driven neural network BP decoding was proposed, which achieves decoding performance similar to or higher than traditional BP algorithms with fewer iterations. [PERSON] et al. [29] introduced a model-driven learning offset minimum algorithm framework, which exhibits low complexity and can be applied to any decoding schedule. [PERSON] et al. [30] presented a model-driven polar decoding algorithm that effectively balances complexity and performance. [PERSON] et al. [31] proposed a model-driven normalized min-sum (NMS) decoding method, which requires fewer iterations compared to traditional LDPC decoding and achieves improved decoding performance. Therefore, to minimize computational complexity and resource consumption while ensuring BER performance, this study designs a model-driven LDPC decoding algorithm, aiming to reduce decoding complexity by reducing the number of iterations or operations. Although the method reduces decoding complexity to some extent, the presence of a large number of weights in the neural network still results in high computational complexity. In the context of drone power inspection, further improvements are needed to make it more suitable for practical applications. To address the issue of high decoding complexity caused by multiple weight parameters, eliminating redundant parameters is crucial. [PERSON] et al. [32] mentioned a neural two-dimensional normalized minimum sum decoder with a simplified parameter set, which allows for assigning the same weight to a class of similar messages. [PERSON] et al. [33] introduced a parameter sharing scheme in the weighted belief propagation [27] decoder, where certain edges share the same weights to reduce storage and computational burden. In [34; 31], a shared neural normalization min-sum (SNNMS) decoding network was presented, where all edges share the same weights in each iteration, reducing the number of correction factors. It achieves an improved bit error rate (BER) with fewer trainable parameters compared to NNMS. [PERSON] et al. [35] mentioned training quantization and weight sharing methods using codebook-based weight quantization for weight sharing, resulting in deep neural network compression. Building upon [35], [PERSON] et al. [36] proposed a novel shared offset min-sum (SNOMS) approach, reducing the number of network weights through parameter sharing, with comparable decoding performance to NOMS, but significantly reduced computational complexity. [PERSON] et al. [37] proposed a low-complexity recursive neural network (RNN) polar decoder based on codebook weight quantization to reduce the weight parameters. This method effectively reduces memory overhead by 98%. Parameter sharing methods effectively address the issue of excessive learnable weights in the network, thus reducing the implementation complexity of the decoding algorithm while maintaining decoding performance.