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.

翻译：当前大多数基于深度学习的语义通信系统仅针对特定单信道条件进行设计与训练，这限制了其适应性与整体带宽利用率。为解决此问题，我们提出了一种创新的语义自适应特征提取框架，该框架通过允许用户根据信道条件选择不同的子语义组合，显著提升了带宽效率。本文还引入了三种先进的学习算法以整体优化SAFE框架的性能。通过一系列仿真实验，我们证明SAFE框架能够在不同信道带宽条件下有效且自适应地提取与传输语义，其有效性通过主客观质量评估得到了验证。