FAST: Feature Arrangement for Semantic Transmission

Although existing semantic communication systems have achieved great success, they have not considered that the channel is time-varying wherein deep fading occurs occasionally. Moreover, the importance of each semantic feature differs from each other. Consequently, the important features may be affected by channel fading and corrupted, resulting in performance degradation. Therefore, higher performance can be achieved by avoiding the transmission of important features when the channel state is poor. In this paper, we propose a scheme of Feature Arrangement for Semantic Transmission (FAST). In particular, we aim to schedule the transmission order of features and transmit important features when the channel state is good. To this end, we first propose a novel metric termed feature priority, which takes into consideration both feature importance and feature robustness. Then, we perform channel prediction at the transmitter side to obtain the future channel state information (CSI). Furthermore, the feature arrangement module is developed based on the proposed feature priority and the predicted CSI by transmitting the prior features under better CSI. Simulation results show that the proposed scheme significantly improves the performance of image transmission compared to existing semantic communication systems without feature arrangement.