Task-Adaptive Semantic Communications with Controllable Diffusion-based Data Regeneration

TL;DR

This paper introduces a diffusion model-based semantic communication framework that adaptively transmits task-relevant information, improving bandwidth efficiency and customization for various downstream tasks.

Contribution

It proposes a novel task-adaptive semantic communication method utilizing diffusion models and attention mechanisms for dynamic semantic data transmission.

Findings

Effective in preserving task-relevant information

Achieves high compression efficiency

Adapts transmission based on receiver feedback

Abstract

Semantic communications represent a new paradigm of next-generation networking that shifts bit-wise data delivery to conveying the semantic meanings for bandwidth efficiency. To effectively accommodate various potential downstream tasks at the receiver side, one should adaptively convey the most critical semantic information. This work presents a novel task-adaptive semantic communication framework based on diffusion models that is capable of dynamically adjusting the semantic message delivery according to various downstream tasks. Specifically, we initialize the transmission of a deep-compressed general semantic representation from the transmitter to enable diffusion-based coarse data reconstruction at the receiver. The receiver identifies the task-specific demands and generates textual prompts as feedback. Integrated with the attention mechanism, the transmitter updates the semantic transmission with more details to better align with the objectives of the intended receivers. Our test results demonstrate the efficacy of the proposed method in adaptively preserving critical task-relevant information for semantic communications while preserving high compression efficiency.