6G语义通信框架可自适应选语义组合，提升带宽效率。
SAFE: Semantic Adaptive Feature Extraction with Rate Control for 6G Wireless Communications
- 根据信道条件动态选择子语义组合，实现自适应特征提取。
- 在不同带宽下均能有效传输语义，主观与客观质量评估达标。
- 适合需要高效低延迟通信的6G应用场景，如智能交通、工业物联网。
当前基于深度学习的语义通信系统大多仅针对特定单信道条件设计与训练，限制了其适应性与整体带宽利用率。为此，本文提出一种创新的语义自适应特征提取（SAFE）框架，通过允许用户依据信道状况选择不同子语义组合，显著提升带宽效率。本文还引入三种先进学习算法，以优化SAFE框架的整体性能。一系列仿真实验表明，该框架能在不同信道带宽条件下有效且自适应地提取并传输语义信息，其有效性通过客观与主观质量评估得到验证。
原文摘要 · Abstract (English)
Most current Deep Learning-based Semantic Communication (DeepSC) systems are designed and trained exclusively for particular single-channel conditions, which restricts their adaptability and overall bandwidth utilization. To address this, we propose an innovative Semantic Adaptive Feature Extraction (SAFE) framework, which significantly improves bandwidth efficiency by allowing users to select different sub-semantic combinations based on their channel conditions. This paper also introduces three advanced learning algorithms to optimize the performance of SAFE framework as a whole. Through a series of simulation experiments, we demonstrate that the SAFE framework can effectively and adaptively extract and transmit semantics under different channel bandwidth conditions, of which effectiveness is verified through objective and subjective quality evaluations.
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