Synchronous Multi-modal Semantic Communication System with Packet-level Coding
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Abstract (arXiv)
Although the semantic communication with joint semantic-channel coding design has shown promising performance in transmitting data of different modalities over physical layer channels, the synchronization and packet-level forward error correction of multimodal semantics have not been well studied. Due to the independent design of semantic encoders, synchronizing multimodal features in both the semantic and time domains is a challenging problem. In this paper, we take the facial video and speech transmission as an example and propose a Synchronous Multimodal Semantic Communication System (SyncSC) with Packet-Level Coding. To achieve semantic and time synchronization, 3D Morphable Mode (3DMM) coefficients and text are transmitted as semantics, and we propose a semantic codec that achieves similar quality of reconstruction and synchronization with lower bandwidth, compared to traditional methods. To protect semantic packets under the erasure channel, we propose a packet-Level Forward Error Correction (FEC) method, called PacSC, that maintains a certain visual quality performance even at high packet loss rates. Particularly, for text packets, a text packet loss concealment module, called TextPC, based on Bidirectional Encoder Representations from Transformers (BERT) is proposed, which significantly improves the performance of traditional FEC methods. The simulation results show that our proposed SyncSC reduce transmission overhead and achieve high-quality synchronous transmission of video and speech over the packet loss network.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 40 of its 55 references that are in the map. First posted on arXiv 8 Aug 2024.
Builds on
40 of 55 references.
References · 40
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28 more
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Built on by
Citing papers per year: 2024: 1; 2025: 4; 2026: 2.
7 papers.
- 2025Low-Complexity Semantic Packet Aggregation for Token Communication via Lookahead Searchcited by 5
- 2024Editable-DeepSC: Reliable Cross-Modal Semantic Communications for Facial Editingcited by 3
- 2025Conquering High Packet-Loss Erasure: MoE Swin Transformer-Based Video Semantic Communicationcited by 2
- 2025Semantic Compression via Multimodal Representation Learningcited by 0
- 2025Large Speech Model Enabled Semantic Communicationcited by 0
- 2026Communication-Efficient Multi-Modal Edge Inference via Uncertainty-Aware Distributed Learningcited by 0
- 2026From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivitycited by 0
- 2026From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity9 Sep 2026
- 2026Communication-Efficient Multi-Modal Edge Inference via Uncertainty-Aware Distributed Learning21 Jan 2026
- 2025Large Speech Model Enabled Semantic Communication4 Dec 2025
- 2025Semantic Compression via Multimodal Representation Learning29 Sep 2025
- 2025Conquering High Packet-Loss Erasure: MoE Swin Transformer-Based Video Semantic Communication2 Aug 2025
- 2025Low-Complexity Semantic Packet Aggregation for Token Communication via Lookahead Search24 Jun 2025
- 2024Editable-DeepSC: Reliable Cross-Modal Semantic Communications for Facial Editing24 Nov 2024