Sequence Spreading-Based Semantic Communication Under High RF Interference

2025-01-21

AI Summary

To address the robustness and multi-user support challenges of 6G semantic communication under strong RF interference in industrial environments, this paper proposes a novel sequence-spread spectrum–enhanced semantic communication paradigm. Methodologically, it integrates sequence spreading, semantic encoding/decoding, and channel equalization, and introduces a lightweight Signal Refinement Network (SRN) that operates without end-to-end training to improve semantic recovery fidelity at the receiver. Key contributions are: (1) the first incorporation of sequence spreading into semantic communication architectures, significantly enhancing interference resilience and scalability for multi-user access; and (2) a low-complexity SRN achieving high computational efficiency and semantic fidelity without requiring joint optimization. Experiments demonstrate that, under identical bandwidth constraints, the proposed approach improves BLEU score by 25% and semantic similarity by 12% over conventional end-to-end semantic communication baselines.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Semi-Supervised Learning
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Security and Privacy: Large-scale security measurements

Abstract

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.

Problem

• Semantic Communication
• Sequence Spreading
• Robustness Optimization

Innovation

• Sequence Spreading
• Semantic Communication
• Signal Refinement Network