Scene Understanding Enabled Semantic Communication with Open Channel Coding

2025-01-24

AI Summary

Traditional semantic communication systems suffer from rigid fixed-rate coding, static knowledge bases, and poor adaptability to dynamic scenarios—critical bottlenecks for 6G. Method: This paper proposes OpenSC, a dynamic adaptive semantic transmission framework for 6G, integrating scene graph–based structural modeling with large language models (LLMs). It introduces an open-channel coding mechanism that replaces domain-specific knowledge bases with publicly accessible commonsense knowledge, enabling context-aware semantic selective compression. Furthermore, it employs vision-language pretraining via visual question answering (VQA) to drive cross-modal semantic representation learning. Contribution/Results: Experiments demonstrate that OpenSC significantly improves semantic understanding accuracy and transmission efficiency on VQA and related tasks. It exhibits strong generalization across diverse environments, high robustness to channel perturbations, and low semantic redundancy—establishing a novel paradigm for open-world semantic communication.

Technology Category

• Multiagent Systems: Agent Communication
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Large Multimodal Models (LMMs)

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: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

As communication systems transition from symbol transmission to conveying meaningful information, sixth-generation (6G) networks emphasize semantic communication. This approach prioritizes high-level semantic information, improving robustness and reducing redundancy across modalities like text, speech, and images. However, traditional semantic communication faces limitations, including static coding strategies, poor generalization, and reliance on task-specific knowledge bases that hinder adaptability. To overcome these challenges, we propose a novel system combining scene understanding, Large Language Models (LLMs), and open channel coding, named extbf{OpenSC}. Traditional systems rely on fixed domain-specific knowledge bases, limiting their ability to generalize. Our open channel coding approach leverages shared, publicly available knowledge, enabling flexible, adaptive encoding. This dynamic system reduces reliance on static task-specific data, enhancing adaptability across diverse tasks and environments. Additionally, we use scene graphs for structured semantic encoding, capturing object relationships and context to improve tasks like Visual Question Answering (VQA). Our approach selectively encodes key semantic elements, minimizing redundancy and improving transmission efficiency. Experimental results show significant improvements in both semantic understanding and efficiency, advancing the potential of adaptive, generalizable semantic communication in 6G networks.

Problem

• Semantic Communication
• Adaptability
• Knowledge Base Dependence

Innovation

• OpenSC
• Semantic Communication
• Scene Graph Encoding