  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.
