An Image Semantic Communication Method Based on SwinJSCC with Explicit MIMO and Hybrid Channel Modeling

To address the limited channel coverage of the original SwinJSCC in com-plex wireless scenarios, the lack of an explicit MIMO transmission mecha-nism, and the heavy training burden caused by separately training independ-ent models for different channel modes, this paper proposes a SwinJSCC-based image semantic communication method with explicit MIMO and hy-brid channel modeling. Built upon SwinJSCC, the proposed method pre-serves the original SNR adaptation and rate adaptation capabilities while uni-fying AWGN, Rayleigh fading, MIMO transmit diversity, MIMO spatial mul-tiplexing, and large-scale path-loss extended channels into a single end-to-end training and testing framework. To avoid training a large number of sep-arate models for different channel types and MIMO structures, a mode-oriented conditional modulation mechanism is introduced, enabling the en-coder and decoder to adjust features according to the current transmission mode while sharing the same backbone parameters. In channel modeling, the proposed method does not approximate multi-antenna gain by a simple equivalent SNR. Instead, 2×1, 4×1, and 8×1 are modeled as explicit transmit diversity, while 2×2 and 4×2 are modeled as spatial multiplexing, further combined with large-scale path-loss factors. This work provides a scalable single-model unified solution for image semantic transmission in complex wireless environments.