RIS-Enhanced Semantic Communication Systems Oriented towards Semantic Importance and Robustness

ZHANG Zufan; YIN Xingran; ZHOU Jianping; LIU Yue

doi:10.11999/JEIT250159

Volume 47 Issue 8

Aug. 2025

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ZHANG Zufan, YIN Xingran, ZHOU Jianping, LIU Yue. RIS-Enhanced Semantic Communication Systems Oriented towards Semantic Importance and Robustness[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2608-2620. doi: 10.11999/JEIT250159

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ZHANG Zufan, YIN Xingran, ZHOU Jianping, LIU Yue. RIS-Enhanced Semantic Communication Systems Oriented towards Semantic Importance and Robustness[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2608-2620. doi: 10.11999/JEIT250159

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RIS-Enhanced Semantic Communication Systems Oriented towards Semantic Importance and Robustness

doi: 10.11999/JEIT250159 cstr: 32379.14.JEIT250159

ZHANG Zufan¹,
YIN Xingran^{1
,
,},
ZHOU Jianping^{1, 2},
LIU Yue¹

1.
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
School of Information Engineering, Sanming University, Sanming 365004, China

Funds: The Natural Science Foundation of Chongqing, China (CSTB2025NSCQ-LZX0051)

Received Date: 2025-03-15
Rev Recd Date: 2025-07-17

Available Online: 2025-07-29

Publish Date: 2025-08-27

Abstract

Abstract

Objective The deep integration of Deep Learning (DL) and Semantic Communication (SC) has become a key trend in next-generation communication systems. Current SC systems primarily adopt DL-based Joint Source-Channel Coding (JSCC) with end-to-end training to enable efficient semantic transmission. However, several limitations remain. Existing systems often optimize physical-layer channel characteristics or semantic-layer feature extraction in isolation, without establishing cross-layer mapping mechanisms. In addition, protection strategies for critical semantic features in fading channel environments are insufficient, limiting semantic recovery performance. To address these challenges, this study integrates Reconfigurable Intelligent Surfaces (RIS) into SC systems and proposes an intelligent transmission scheme based on dual-dimensional semantic feature metrics. The proposed approach effectively enhances semantic recovery capability under adverse channel conditions. This work provides a new intelligent solution for protecting semantic features in fading channels and establishes theoretical support for collaborative mechanisms between physical and semantic layers in SC systems. Methods This study develops a joint semantic importance-robustness metric model. Semantic importance is quantified using Bidirectional Encoder Representations from Transformers (BERT) combined with cosine similarity, while semantic robustness is assessed by measuring the loss increments of high-dimensional feature vectors during transmission. A dynamically updated background knowledge base is constructed to support a priority evaluation framework for semantic features (Fig. 2). During transmission, the system partitions the original text into high- and low-priority data streams based on feature priority. High-priority streams are transmitted through RIS-assisted channels, whereas low-priority streams are transmitted over conventional fading channels. At the physical layer, an alternating optimization algorithm jointly designs active precoding beamforming vectors and RIS passive phase matrices. At the receiver, semantic reconstruction is performed under the guidance of feature priority index lists (Fig. 1). Results and Discussions The proposed SISR-RIS system effectively reduces the distortion effects of channel fading on critical semantic features by establishing cross-layer mapping between semantic features and physical channels. Simulation results show that, in medium-to-low Signal-to-Noise Ratio (SNR) environments, the SISR-RIS system maintains high low-order BLEU scores and approaches the theoretical performance boundary near the 10 dB SNR threshold, achieving approximately 95% recovery accuracy for BLEU-1 and 92% for BLEU-2 (Fig.3(a)). As the n-gram order increases, the system outperforms the baseline Deep-SC system by approximately 10% in BLEU-4, confirming its improved capability for contextual semantic reconstruction (Fig.3(b)). Owing to the dual-dimensional metric mechanism, the system demonstrates stable performance with less than 1% variance in recovery accuracy across short and long sentences (Fig. 4). Case analysis indicates that when the original statements cannot be fully restored, the system maintains semantic equivalence through appropriate synonym substitutions. Additionally, core verbs and nouns are consistently assigned higher feature priority scores, which reduces the effect of channel fading on critical semantic features (Tables 2 and 3; Figs. 5 and 6). Conclusions This study proposes a RIS-enhanced SC system designed to account for semantic importance and robustness. By extracting semantic importance and robustness features to prioritise transmission and implementing a joint physical-semantic layer design enabled by RIS, the system provides enhanced protection for high-importance, low-robustness semantic features. Evaluations based on BLEU scores, BERT Semantic Similarity (BERT-SS) metrics, and case analyses demonstrate the following: (1) The proposed system achieves a 15% performance improvement over baseline systems in low SNR environments, with performance approaching theoretical limits near the 10 dB SNR threshold; (2) In high-SNR conditions, the system performs comparably to state-of-the-art methods across both BLEU and BERT-SS metrics; (3) The dual-dimensional semantic feature metric mechanism enhances contextual semantic relevance, reduces the recovery discrepancy between long and short sentences to below 1% in high-SNR scenarios, and demonstrates strong adaptability to varying text lengths.
- Semantic Communication (SC),
- Semantic importance,
- Semantic robustness,
- Reconfigurable Intelligent Surface(RIS)

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References(27)

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