用扩散模型动态调整语义传输，让信息更贴合接收端任务需求。
Task-Adaptive Semantic Communications with Controllable Diffusion-based Data Regeneration
- 基于扩散模型的语义通信框架，支持接收端反馈引导发送端更新信息。
- 在保持高压缩率前提下，自适应保留对下游任务关键的语义信息。
- 适合需要灵活适配多种任务的智能通信系统应用。
语义通信是下一代网络的新范式，将传统的比特级数据传输转向语义意义的传递以提升带宽效率。为有效适应接收端可能出现的各种下游任务，需自适应地传输最关键的信息。本文提出一种基于扩散模型的任务自适应语义通信框架，可依据不同下游任务动态调整语义消息的传输内容。具体而言，发送端初始化传输一个深度压缩的通用语义表示，使接收端能基于扩散模型实现粗粒度的数据重建。接收端识别任务需求后生成文本提示作为反馈，通过注意力机制，发送端据此更新语义传输，增加与目标任务更契合的细节。测试结果表明，该方法在保持高压缩效率的同时，能自适应地保留对任务至关重要的语义信息。
原文摘要 · Abstract (English)
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.
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