A graph attention module that recombines intermediate encoder features with task-specific weights improves multi-task semantic communication accuracy, especially under tight bandwidth.
Task-Oriented Communications for 6G: Vision, Principles, and Technologies
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abstract
Driven by the interplay among artificial intelligence, digital twin, and wireless networks, 6G is envisaged to go beyond data-centric services to provide intelligent and immersive experiences. To efficiently support intelligent tasks with customized service requirements, it becomes critical to develop novel information compression and transmission technologies, which typically involve coupled sensing, communication, and computation processes. To this end, task-oriented communication stands out as a disruptive technology for 6G system design by exploiting the task-specific information structures and folding the communication goals into the design of task-level transmission strategies. In this article, by developing task-oriented information extraction and network resource orchestration strategies, we demonstrate the effectiveness of task-oriented communication principles for typical intelligent tasks, including federated learning, edge inference, and semantic communication.
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Multi-Task Semantic Communication With Graph Attention-Based Feature Correlation Extraction
A graph attention module that recombines intermediate encoder features with task-specific weights improves multi-task semantic communication accuracy, especially under tight bandwidth.