论文标题
短块代码的可扩展图神经网络解码器
A Scalable Graph Neural Network Decoder for Short Block Codes
论文作者
论文摘要
在这项工作中，我们提出了一种基于边缘加权神经网络（EW-GNN）的短块代码的新型解码算法。 EW-GNN解码器在Tanner图上运行，具有迭代消息 - 通信结构，该结构将算法与常规信念传播（BP）解码方法保持一致。在每次迭代中，沿每个边缘传递的消息上的“权重”都是从完全连接的神经网络获得的，该神经网络具有从节点/边缘作为输入的可靠性信息。与现有的基于深度学习的解码方案相比，EW-GNN解码器的特征在于其可伸缩性，这意味着1）可训练的参数的数量与编码版本无关，而2）经过较短/简单代码的EW-GNNN解码器可以直接用于长/复杂的代码，以使其长度/复杂的编码不同编码。此外，模拟结果表明，根据解码错误率，EW-GNN解码器的表现优于文献中的BP和基于深度学习的BP方法。
In this work, we propose a novel decoding algorithm for short block codes based on an edge-weighted graph neural network (EW-GNN). The EW-GNN decoder operates on the Tanner graph with an iterative message-passing structure, which algorithmically aligns with the conventional belief propagation (BP) decoding method. In each iteration, the "weight" on the message passed along each edge is obtained from a fully connected neural network that has the reliability information from nodes/edges as its input. Compared to existing deep-learning-based decoding schemes, the EW-GNN decoder is characterised by its scalability, meaning that 1) the number of trainable parameters is independent of the codeword length, and 2) an EW-GNN decoder trained with shorter/simple codes can be directly used for longer/sophisticated codes of different code rates. Furthermore, simulation results show that the EW-GNN decoder outperforms the BP and deep-learning-based BP methods from the literature in terms of the decoding error rate.
