  Intelligent task-oriented semantic communications (SemComs) have witnessed
great progress with the development of deep learning (DL). In this paper, we
propose a semantic-aware hybrid automatic repeat request (SemHARQ) framework
for the robust and efficient transmissions of semantic features. First, to
improve the robustness and effectiveness of semantic coding, a multi-task
semantic encoder is proposed. Meanwhile, a feature importance ranking (FIR)
method is investigated to ensure the important features delivery under limited
channel resources. Then, to accurately detect the possible transmission errors,
a novel feature distortion evaluation (FDE) network is designed to identify the
distortion level of each feature, based on which an efficient HARQ method is
proposed. Specifically, the corrupted features are retransmitted, where the
remaining channel resources are used for incremental transmissions. The system
performance is evaluated under different channel conditions in multi-task
scenarios in Internet of Vehicles. Extensive experiments show that the proposed
framework outperforms state-of-the-art works by more than 20% in rank-1
accuracy for vehicle re-identification, and 10% in vehicle color classification
accuracy in the low signal-to-noise ratio regime.
