Sequence Spreading-Based Semantic Communication Under High RF Interference

TL;DR

This paper introduces a sequence spreading-based semantic communication framework with a signal refining network to improve robustness against high RF interference in industrial environments, achieving significant performance gains.

Contribution

It proposes a novel sequence spreading approach combined with a signal refining network to enhance robustness and scalability of semantic communication under high interference conditions.

Findings

Achieves 25% BLEU score improvement

Attains 12% higher semantic similarity

Supports scalable multi-user SemCom

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.