提出序列扩频增强语义通信抗射频干扰能力，支持多用户场景。
Sequence Spreading-Based Semantic Communication Under High RF Interference
- 融合序列扩频与语义通信，提升系统抗干扰性。
- 信号重构网络使语义相似度提升12%，BLEU分数提高25%。
- 无需端到端训练，适合工业高干扰环境部署。
在无线通信演进背景下，语义通信（SemCom）作为6G关键技术，强调信息意义与上下文相关性而非传统比特传输。然而，工业场景中高射频干扰（RFI）严重制约其性能。为此，本文提出一种结合序列扩频与语义通信的新方法，显著提升系统鲁棒性并支持可扩展多用户（MU）语义通信。同时设计新型信号重构网络（SRN），在解扩与均衡后优化接收信号。该网络避免了计算量大的端到端（E2E）训练，仅用相同带宽即实现语义相似度提升12%，BLEU分数提高25%。
原文摘要 · Abstract (English)
In the evolving landscape of wireless communications, semantic communication (SemCom) has recently emerged as a 6G enabler that prioritizes the transmission of meaning and contextual relevance over conventional bit-centric metrics. However, the deployment of SemCom systems in industrial settings presents considerable challenges, such as high radio frequency interference (RFI), that can adversely affect system performance. To address this problem, in this work, we propose a novel approach based on integrating sequence spreading techniques with SemCom to enhance system robustness against such adverse conditions and enable scalable multi-user (MU) SemCom. In addition, we propose a novel signal refining network (SRN) to refine the received signal after despreading and equalization. The proposed network eliminates the need for computationally intensive end-to-end (E2E) training while improving performance metrics, achieving a 25% gain in BLEU score and a 12% increase in semantic similarity compared to E2E training using the same bandwidth.
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