SemHARQ: Semantic-Aware HARQ for Multi-task Semantic Communications

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.

Scalable Multi-task Semantic Communication System with Feature Importance Ranking

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.