MIMO-OTFS-Based Semantic Communication for High-Mobility Scenarios

In high-mobility scenarios with time-frequency doubly-selective channels, existing semantic communication systems suffer significant performance degradation. To address this issue, we propose a semantic communication framework that synergistically integrates multiple-input multiple-output orthogonal time frequency space (MIMO-OTFS) with semantic-aware sub-channel allocation. First, an entropy module is employed to evaluate importance of different semantic features, and the Kendall correlation coefficient is used to quantify the alignment between semantic importance and sub-channel conditions. Subsequently, joint optimization of the encoder and decoder is achieved through a comprehensive loss function that balances image classification accuracy, reconstruction quality, and sub-channel matching degree. Experimental results confirm the superior reconstruction quality of our proposed framework compared to conventional semantic communication systems based on orthogonal frequency division multiplexing in high-mobility channel environment.