AI 中文总结
针对多数神经接收机内外接收机未联合优化的问题，提出基于基础模型的FM-Receiver，利用分组纠错码Transformer实现符号级信道解码，集成内外接收机，并设计预训练策略，提升泛化能力，在不同配置下性能优于基线且有零样本泛化能力。
AI 中文摘要
随着人工智能技术的发展，已开发出应用人工智能改进无线接收机的神经接收机。然而，大多数现有神经接收机仅将深度学习应用于外部接收机，内部接收机仍采用传统信道解码，这阻碍了联合优化，难以构建高效统一的人工智能原生接收机。为解决此问题，我们提出了一种基于基础模型（FM）的统一神经接收机FM-Receiver，通过利用FM强大的表示能力，将内外部接收机集成到单个人工智能原生框架中。具体而言，我们引入了分组纠错码Transformer进行符号级信道解码，实现内外部接收机的无缝集成。在此基础上，我们展示了所提出的FM-Receiver，它直接将接收到的信号作为FM的输入，并输出恢复的传输比特。此外，还设计了一种三阶段配置自适应预训练策略，以提高对不同系统配置和场景的泛化能力。大量仿真表明，所提出的FM-Receiver在不同系统配置下比基线具有更好的性能。它还展示了对未见频段和场景的强大零样本泛化能力。
英文摘要
With the development of artificial intelligence (AI) techniques, neural receivers, which apply AI to improve wireless receivers have been developed. However, most existing neural receivers apply deep learning only to the outer receiver while retaining conventional channel decoding for the inner receiver, which prevents joint optimization and makes it difficult to build efficient and unified AI-native receivers. To address this issue, we propose a foundation model (FM)-enabled unified neural receiver, FM-Receiver, that integrates the outer and inner receivers into a single AI-native framework, by leveraging the strong representation capability of FMs. Specifically, we introduce a grouped error correction code Transformer that performs symbol-level channel decoding, enabling seamless integration of the inner and outer receiver. Building on this, we illustrate the proposed FM-Receiver, that directly takes the received signals as input of FM and outputs the recovered transmitted bits. In addition, a three-stage configuration-adaptive pre-training strategy is designed to improve the generalization ability to diverse system configurations and scenarios. Extensive simulations show that the proposed FM-Receiver achieves better performance than baselines across different system configurations. It also demonstrates strong zero-shot generalization to unseen frequency bands and scenarios.
