用任务导向的语义压缩，让通信更高效。
Task-Driven Semantic Quantization and Imitation Learning for Goal-Oriented Communications
- 基于任务需求提取关键语义，压缩传输数据。
- 结合模仿学习评估语义重建质量，提升任务性能。
- 适合追求低带宽、高任务效率的通信系统设计者。
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，旨在降低带宽消耗。为在接收端更高效地完成特定下游任务，需根据任务目标定义数据中最具关键性的语义内容。本文提出一种新型面向任务的通信框架——目标导向语义变分自编码器（GOS-VAE），聚焦于提取对下游任务至关重要的语义。具体而言，在发送端采用向量量化变分自编码器（VQ-VAE）压缩媒体数据；不以像素级图像重建为目标，而是基于预定义的任务激励模型衡量接收端的服务质量。此外，通过模仿学习衡量数据再生质量，以捕捉与任务相关的语义特征。实验结果表明，模仿学习能有效刻画任务导向语义，并显著提升GOS-VAE的带宽效率。
原文摘要 · Abstract (English)
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.
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