Task-Driven Semantic Quantization and Imitation Learning for Goal-Oriented Communications

TL;DR

This paper introduces GOS-VAE, a goal-oriented semantic communication framework that uses VQ-VAE and imitation learning to efficiently transmit task-critical information, reducing bandwidth while maintaining task performance.

Contribution

The paper proposes a novel GOS-VAE framework combining VQ-VAE and imitation learning for goal-oriented semantic data transmission, focusing on task-relevant features.

Findings

GOS-VAE effectively captures goal-oriented semantics.

The framework improves bandwidth efficiency.

Imitation learning enhances semantic fidelity.

Abstract

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.