A Scalable Multi-Device Semantic Communication System for Multi - Task Execution
Mingze Gong, Shuoyao Wang, Suzhi Bi · 2023
Motivated by the success of deep learning, semantic communication has emerged as new paradigm shifts in 6G from the conventional data-oriented communications. However, the semantic communication systems suffer performance degradation at the receiver side or computation latency accumulation at the transmitter side, when serves multiple task execution. To address the issue, we develop a VisionTransformer based multi-device semantic communication system called MDSC to effectively perform multiple tasks. In particular, we introduce the shared semantic encoder to the transmitter to extract global semantic information, preventing for computation accumulation at the transmitter side. To cope with the fact that the semantic information for different task may differ from each other, we propose multiple-encoder-multiple-decoder channel codec architecture, improving the compression efficiency with task-specific codec. The compression efficiency leads to higher noise robustness and downstream task execution accuracy at the receiver side. In the experiments, we validate the proposed system with two tasks in NYUD-v2 and four tasks in PASCAL-Context, respectively. Compared with the state-of-the-art multi-task semantic communication system, MDSC achieves higher performance simultaneously for all tasks in both datasets.