Scene Understanding Enabled Semantic Communication with Open Channel Coding

TL;DR

This paper introduces OpenSC, a novel semantic communication system for 6G that uses scene understanding, open channel coding, and scene graphs to improve adaptability, efficiency, and robustness over traditional static methods.

Contribution

We propose a dynamic semantic communication framework combining scene understanding, open channel coding, and scene graphs to enhance generalization and efficiency in 6G networks.

Findings

Significant improvements in semantic understanding accuracy.

Enhanced transmission efficiency through selective encoding.

Better adaptability across diverse tasks and environments.

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 \textbf{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.