  Semantic communications are expected to be an innovative solution to the
emerging intelligent applications in the era of connected intelligence. In this
paper, a novel scalable multitask semantic communication system with feature
importance ranking (SMSC-FIR) is explored. Firstly, the multi-task correlations
are investigated by a joint semantic encoder to extract relevant features.
Then, a new scalable coding method is proposed based on feature importance
ranking, which dynamically adjusts the coding rate and guarantees that
important features for semantic tasks are transmitted with higher priority.
Simulation results show that SMSC-FIR achieves performance gain w.r.t.
individual intelligent tasks, especially in the low SNR regime.
