一传两用：通信中同时实现图像重建与分类
Two Birds with One Stone: Multi-Task Semantic Communications Systems over Relay Channel
- 源节点发送语义信号，中继选择性转发关键类别信息
- 图像重建PSNR提升1.73dB，分类准确率从64.89%升至70.31%
- 适合需多任务协同的低延迟智能通信系统
本文提出一种新型多任务、多链路中继语义通信（MTML-RSC）方案，使接收端能通过源节点一次传输同时完成图像重建与分类。源节点以语义通信方式广播信号，中继节点转发信号至接收端。我们分析了两个任务与两条链路（源到中继、源到接收端）间的耦合关系，并设计面向语义的中继转发策略，仅选择性转发相关类别语义信息，忽略其他内容。接收端融合来自源节点与中继节点的信号进行分类，再利用分类结果辅助解码中继信号以完成图像重建。实验表明，所提方案在图像重建上实现1.73 dB的峰值信噪比（PSNR）提升，分类准确率从64.89%提高至70.31%。
原文摘要 · Abstract (English)
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.
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