In this paper, we propose a novel multi-task, multi-link relay semantic communications (MTML-RSC) scheme that enables the destination node to simultaneously perform image reconstruction and classification with one transmission from the source node. In the MTML-RSC scheme, the source node broadcasts a signal using semantic communications, and the relay node forwards the signal to the destination. We analyze the coupling relationship between the two tasks and the two links (source-to-relay and source-to-destination) and design a semantic-focused forward method for the relay node, where it selectively forwards only the semantics of the relevant class while ignoring others. At the destination, the node combines signals from both the source node and the relay node to perform classification, and then uses the classification result to assist in decoding the signal from the relay node for image reconstructing. Experimental results demonstrate that the proposed MTML-RSC scheme achieves significant performance gains, e.g., $1.73$ dB improvement in peak-signal-to-noise ratio (PSNR) for image reconstruction and increasing the accuracy from $64.89\%$ to $70.31\%$ for classification.

翻译：本文提出了一种新颖的多任务多链路中继语义通信（MTML-RSC）方案，该方案使得目的节点能够通过源节点的一次传输同时完成图像重建与分类任务。在MTML-RSC方案中，源节点采用语义通信方式广播信号，中继节点则将该信号转发至目的节点。我们分析了两个任务（图像重建与分类）与两条链路（源-中继链路和源-目的链路）之间的耦合关系，并为中继节点设计了一种语义导向的转发方法：中继节点仅选择性转发相关类别的语义信息，同时忽略其他信息。在目的节点处，通过融合来自源节点和中继节点的信号执行分类任务，随后利用分类结果辅助解码来自中继节点的信号以完成图像重建。实验结果表明，所提出的MTML-RSC方案取得了显著的性能提升：图像重建的峰值信噪比（PSNR）提高了$1.73$ dB，分类准确率从$64.89\%$提升至$70.31\%$。