Semantic communication marks a new paradigm shift from bit-wise data transmission to semantic information delivery for the purpose of bandwidth reduction. To more effectively carry out specialized downstream tasks at the receiver end, it is crucial to define the most critical semantic message in the data based on the task or goal-oriented features. In this work, we propose a novel goal-oriented communication (GO-COM) framework, namely Goal-Oriented Semantic Variational Autoencoder (GOS-VAE), by focusing on the extraction of the semantics vital to the downstream tasks. Specifically, we adopt a Vector Quantized Variational Autoencoder (VQ-VAE) to compress media data at the transmitter side. Instead of targeting the pixel-wise image data reconstruction, we measure the quality-of-service at the receiver end based on a pre-defined task-incentivized model. Moreover, to capture the relevant semantic features in the data reconstruction, imitation learning is adopted to measure the data regeneration quality in terms of goal-oriented semantics. Our experimental results demonstrate the power of imitation learning in characterizing goal-oriented semantics and bandwidth efficiency of our proposed GOS-VAE.

翻译：语义通信标志着从比特级数据传输向语义信息传递的新范式转变，旨在降低带宽需求。为了在接收端更有效地执行特定下游任务，基于任务或目标导向特征定义数据中最关键的语义信息至关重要。本文提出一种新颖的目标导向通信（GO-COM）框架——目标导向语义变分自编码器（GOS-VAE），重点关注对下游任务至关重要的语义特征提取。具体而言，我们在发送端采用矢量量化变分自编码器（VQ-VAE）压缩媒体数据。不同于以像素级图像数据重建为目标，我们在接收端基于预定义的任务激励模型评估服务质量。此外，为捕捉数据重建中的相关语义特征，采用模仿学习方法从目标导向语义角度衡量数据再生质量。实验结果表明，模仿学习在表征目标导向语义方面具有显著优势，且我们提出的GOS-VAE框架展现出优异的带宽效率。