多用户语义通信新框架，抗干扰且省带宽。
NOC4SC: A Bandwidth-Efficient Multi-User Semantic Communication Framework for Interference-Resilient Transmission
- 用共享参数的统一编码解码结构提取语义特征
- 动态调节信噪比生成近正交语义特征，减少干扰
- 支持同频并发传输，适合高密度设备场景
随着连接设备激增和新应用涌现，无线网络面临海量用户接入需求，用户间干扰已成为保障服务质量的关键挑战。为此，我们提出一种高效带宽的语义通信范式——非正交编码语义通信（NOC4SC），实现无需频谱扩展的同频并发传输。通过引入Swin Transformer，NOC4SC框架采用统一的编码解码架构，所有用户共享网络参数，使各用户数据可免于未授权解码。此外，我们设计了自适应的非正交编码与信噪比调制（NSM）模块，利用深度学习动态调控信噪比，并在不同特征子空间生成近似正交的语义特征，有效缓解用户间干扰。大量实验表明，所提NOC4SC在性能上与DeepJSCC-PNOMA相当，优于其他多用户语义通信基线方法。
原文摘要 · Abstract (English)
With the explosive growth of connected devices and emerging applications, current wireless networks are encountering unprecedented demands for massive user access, where the inter-user interference has become a critical challenge to maintaining high quality of service (QoS) in multi-user communication systems. To tackle this issue, we propose a bandwidth-efficient semantic communication paradigm termed Non-Orthogonal Codewords for Semantic Communication (NOC4SC), which enables simultaneous same-frequency transmission without spectrum spreading. By leveraging the Swin Transformer, the proposed NOC4SC framework enables each user to independently extract semantic features through a unified encoder-decoder architecture with shared network parameters across all users, which ensures that the user's data remains protected from unauthorized decoding. Furthermore, we introduce an adaptive NOC and SNR Modulation (NSM) block, which employs deep learning to dynamically regulate SNR and generate approximately orthogonal semantic features within distinct feature subspaces, thereby effectively mitigating inter-user interference. Extensive experiments demonstrate the proposed NOC4SC achieves comparable performance to the DeepJSCC-PNOMA and outperforms other multi-user SemCom baseline methods.
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