标题： 面向6G网络中代码无关解码的交叉注意力消息传递变换器 显示英文标题

标题： Cross-Attention Message-Passing Transformers for Code-Agnostic Decoding in 6G Networks

摘要： 针对6G网络的信道编码预计能够支持来自异构通信场景的各种需求。 这些需求对传统的特定代码解码器构成了挑战，因为这些解码器缺乏下一代系统所需的灵活性和可扩展性。 为了解决这个问题，我们提出了一种基于Transformer架构的AI原生基础模型，用于统一且与代码无关的解码。 我们首先介绍了一种交叉注意力信息传递Transformer（CrossMPT）。 CrossMPT采用两个遮罩交叉注意力块，迭代更新两种不同的输入表示——幅度向量和校验向量，使模型能够有效地学习解码问题。 值得注意的是，我们的CrossMPT在单个神经解码器中达到了最先进的解码性能。 在此基础上，我们通过使架构对代码长度、速率和类别不变，开发了基础CrossMPT（FCrossMPT），使得一个训练好的模型能够在不重新训练的情况下解码广泛的代码。 为了进一步提高解码性能，特别是对于短块长度代码，我们提出了CrossMPT集成解码器（CrossED），该解码器由多个并行的CrossMPT块组成，使用不同的校验矩阵。 这种架构也可以作为基础模型，在各种代码类型中表现出强大的泛化能力。 总体而言，所提出的AI原生的与代码无关的解码器具有灵活性、可扩展性和高性能，为6G网络的信道编码提供了一个有前景的方向。 显示英文摘要

摘要： Channel coding for 6G networks is expected to support a wide range of requirements arising from heterogeneous communication scenarios. These demands challenge traditional code-specific decoders, which lack the flexibility and scalability required for next-generation systems. To tackle this problem, we propose an AI-native foundation model for unified and code-agnostic decoding based on the transformer architecture. We first introduce a cross-attention message-passing transformer (CrossMPT). CrossMPT employs two masked cross-attention blocks that iteratively update two distinct input representations-magnitude and syndrome vectors-allowing the model to effectively learn the decoding problem. Notably, our CrossMPT has achieved state-of-the-art decoding performance among single neural decoders. Building on this, we develop foundation CrossMPT (FCrossMPT) by making the architecture invariant to code length, rate, and class, allowing a single trained model to decode a broad range of codes without retraining. To further enhance decoding performance, particularly for short blocklength codes, we propose CrossMPT ensemble decoder (CrossED), an ensemble decoder composed of multiple parallel CrossMPT blocks employing different parity-check matrices. This architecture can also serve as a foundation model, showing strong generalization across diverse code types. Overall, the proposed AI-native code-agnostic decoder offers flexibility, scalability, and high performance, presenting a promising direction to channel coding for 6G networks.