Recently, semantic communication has been widely applied in wireless image transmission systems as it can prioritize the preservation of meaningful semantic information in images over the accuracy of transmitted symbols, leading to improved communication efficiency. However, existing semantic communication approaches still face limitations in achieving considerable inference performance in downstream AI tasks like image recognition, or balancing the inference performance with the quality of the reconstructed image at the receiver. Therefore, this paper proposes a contrastive learning (CL)-based semantic communication approach to overcome these limitations. Specifically, we regard the image corruption during transmission as a form of data augmentation in CL and leverage CL to reduce the semantic distance between the original and the corrupted reconstruction while maintaining the semantic distance among irrelevant images for better discrimination in downstream tasks. Moreover, we design a two-stage training procedure and the corresponding loss functions for jointly optimizing the semantic encoder and decoder to achieve a good trade-off between the performance of image recognition in the downstream task and reconstructed quality. Simulations are finally conducted to demonstrate the superiority of the proposed method over the competitive approaches. In particular, the proposed method can achieve up to 56\% accuracy gain on the CIFAR10 dataset when the bandwidth compression ratio is 1/48.

翻译：近年来，语义通信在无线图像传输系统中得到广泛应用，因其优先保留图像中有意义的语义信息而非传输符号的准确性，从而提升了通信效率。然而，现有语义通信方法在实现下游AI任务（如图像识别）的显著推理性能，或在推理性能与接收端图像重建质量之间取得平衡方面仍存在局限性。为此，本文提出一种基于对比学习的语义通信方法以克服这些局限。具体而言，我们将传输过程中的图像失真视为对比学习中的数据增强，利用对比学习缩小原始图像与失真重建图像之间的语义距离，同时维持不相关图像间的语义距离，以提升下游任务的区分能力。此外，我们设计了两阶段训练流程及相应的损失函数，用于联合优化语义编码器和解码器，从而在下游图像识别任务性能与重建质量之间实现良好权衡。最后通过仿真验证了所提方法相较于竞争方法的优越性。特别地，当带宽压缩比为1/48时，所提方法在CIFAR10数据集上可获得高达56%的准确率提升。