A Multi-Task Training Semantic Communication System for Image Reconstruction and Classification Tasks

Semantic communication provides a task-oriented alternative to conventional bit-level transmission. For wireless image transmission, existing systems mainly optimize image reconstruction, while downstream classification is often handled by a separate model. This paper proposes Mission-ADWITT, a multi-task semantic communication framework that extends the ADWITT backbone with a classification branch and jointly optimizes image reconstruction and classification. To improve joint training, the reconstruction backbone is initialized from a pretrained ADWITT-CIFAR10 model, while the classification branch is randomly initialized and fine-tuned together with the backbone. Experiments are conducted on CIFAR-10 over AWGN channels at SNR values of 0, 5, 10, 15, and 20 dB. Compared with Mission-ADWITT without ADWITT initialization, the baseline-initialized model improves average classification accuracy from 75.156% to 82.988%, PSNR from 27.933 dB to 30.828 dB, and MS-SSIM from 0.9637 to 0.9804. It also achieves classification accuracy comparable to the separate ADWITT + ResNet18 baseline, although reconstruction quality remains lower. These results highlight the importance of reconstruction-pretrained initialization and the trade-off between visual fidelity and semantic performance.