# Research on Data Link Channel Decoding Optimization Scheme for Drone Power Inspection Scenarios
## 2 Materials and Methods
### Prior Work
#### 2.3.2 Codebook-Based Quantization Method  
In this study, the network weights and parameters of the model-driven neural network are set as float32 floating-point type. However, a large number of floating-point parameters can hinder the hardware implementation of the neural network decoder. To address this issue, quantizing the parameters in the deep learning network can effectively reduce system resource overhead and decoding latency. The quantization methods mentioned in [35; 37; 44] do not degrade the BER performance of the LDPC decoding algorithm while significantly reducing complexity. First, multiple connections are made to share the same weights, reducing the number of effective weights that need to be stored. Then, a discretized codebook is designed to fine-tune these shared weights. The codebook is typically a finite set where each codeword represents a discrete value to replace the weights in the neural network. In neural networks, codebook-based parameter quantization is a technique that discretizes the representation of the network weights and activation values. It can effectively reduce the storage requirements and computational complexity of neural networks, thereby improving the model's operational efficiency. These methods can effectively reduce the number of parameters and lower the computational complexity. To maintain high performance, the weights are quantized after each training iteration, and allow the network to learn the optimal quantized weights. This method can be seen as performing two rounds of quantization to reduce the precision and quantity of the required weights. Specifically, the quantization process is as follows:  
First round of quantization: Quantifying the decimal places of 32-bit floating-point parameters to q bits. Second round of quantization: Designing a weight quantization method based on a codebook. When the size of the codebook is c bits, we calculate the frequency of occurrence of each type of weight from the \(2^{q}\) types of weights, and select the \(2^{c}\) most frequently used weights from large to small as the codebook values. Then, the weights are quantized to the nearest value in the codebook, further reducing the number of weight types in the neural network and, thus, reducing computational complexity. An example of the quantization process (q = 2, c = 2) is illustrated in Figure 5.  
By quantizing the network's weight parameters into a discrete codebook, the codebook-based weight quantization method can significantly reduce the storage requirements of the model. Moreover, the computational operations can be achieved through table lookup, reducing computational complexity. This makes it suitable for resource-constrained UAVs. The Proposed Shared-Parameter Neural-Network-Normalized Minimum Sum Decoding Algorithm Based on Codebook Quantization (Shared-NNMS-CQ)  
Building upon the aforementioned improvement methods, this study aims to design a high-precision, low-complexity LDPC long-code decoder. We propose a novel shared-parameter neural-network-normalized minimum sum decoding algorithm based on codebook quantization (shared-NNMS-CQ), which introduces innovative elements  
Figure 5: A weight quantization scheme based on a codebook. including shared neural network learnable parameters and codebook-based weight parameter quantization. The specific improvements are outlined as follows:  
* Shared neural network trainable parameters: A novel new shared-parameter NNMS (new-SNNMS) LDPC decoding is proposed. The weights \(w_{cv}\) and \(w_{vc}\) are independent and learnable. The weight vector \(w_{cv}\) is shared across different iterations, and different weights are assigned to the check node messages in each iteration. The correction factor \(w_{vc}\) is shared among all variable node messages in each iteration. This sharing mechanism reduces the number of correction factors and network weights, thereby reducing the computational complexity.