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Recently, semantic communication has been widely applied in wireless imagetransmission systems as it can prioritize the preservation of meaningfulsemantic information in images over the accuracy of transmitted symbols,leading to improved communication efficiency. However, existing semanticcommunication approaches still face limitations in achieving considerableinference performance in downstream AI tasks like image recognition, orbalancing the inference performance with the quality of the reconstructed imageat 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 ofdata augmentation in CL and leverage CL to reduce the semantic distance betweenthe original and the corrupted reconstruction while maintaining the semanticdistance among irrelevant images for better discrimination in downstream tasks.Moreover, we design a two-stage training procedure and the corresponding lossfunctions for jointly optimizing the semantic encoder and decoder to achieve agood trade-off between the performance of image recognition in the downstreamtask and reconstructed quality. Simulations are finally conducted todemonstrate the superiority of the proposed method over the competitiveapproaches. In particular, the proposed method can achieve up to 56\% accuracygain on the CIFAR10 dataset when the bandwidth compression ratio is 1/48.

Contrastive Learning based Semantic Communication for Wireless Image Transmission