动态适配信道，多任务优先传输语义信息。
Take What You Need: Flexible Multi-Task Semantic Communications with Channel Adaptation
- 用掩码自编码器架构，按任务重要性筛选关键信息。
- 实测在图像重建与目标检测中性能优于传统方法。
- 适合资源受限下需多任务协同的智能通信系统。
日益增长的高效语义通信需求，要求系统能处理多样任务并适应波动信道。本文提出一种基于掩码自编码器的通道自适应、多任务感知语义通信框架。通过多任务评分机制识别并优先传输跨多个并发任务的语义关键数据；采用通道感知提取器，根据实时信道条件动态选择相关信息。联合优化语义相关性与传输效率，在资源受限下实现最小性能损失。实验表明，该框架在图像重建和目标检测等任务中显著优于传统方法，展现出对异构信道环境的良好适应性及多任务应用的可扩展性，为下一代语义通信网络提供有力解决方案。
原文摘要 · Abstract (English)
The growing demand for efficient semantic communication systems capable of managing diverse tasks and adapting to fluctuating channel conditions has driven the development of robust, resource-efficient frameworks. This article introduces a novel channel-adaptive and multi-task-aware semantic communication framework based on a masked auto-encoder architecture. Our framework optimizes the transmission of meaningful information by incorporating a multi-task-aware scoring mechanism that identifies and prioritizes semantically significant data across multiple concurrent tasks. A channel-aware extractor is employed to dynamically select relevant information in response to real-time channel conditions. By jointly optimizing semantic relevance and transmission efficiency, the framework ensures minimal performance degradation under resource constraints. Experimental results demonstrate the superior performance of our framework compared to conventional methods in tasks such as image reconstruction and object detection. These results underscore the framework's adaptability to heterogeneous channel environments and its scalability for multi-task applications, positioning it as a promising solution for next-generation semantic communication networks.
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