🤖 AI Summary
Semantic communication for 6G must transcend Shannon’s bit-level fidelity paradigm to jointly model multidimensional semantic metrics—such as information value (VoI), age of information (AoI), and age of incorrect information (AoII)—while balancing correctness, timeliness, and utility.
Method: We propose the first tensorized Goal-of-Transmission (GoT) model, enabling unified representation and composable optimization of heterogeneous semantic metrics. Integrating semantic information theory, tensor algebra, and deep learning, we design an end-to-end, goal-oriented architecture that tightly couples perception, transmission, and control.
Contribution/Results: This work establishes the first systematic, scalable framework for goal-oriented semantic communication. It rigorously formalizes key challenges—including semantic abstraction, dynamic metric composition, and cross-layer optimization—and provides a theoretically grounded, implementation-ready pathway toward both theoretical advancement and practical deployment of semantic communication in 6G systems.
📝 Abstract
Since Shannon's pioneering masterpiece which established the prototype of modern information theory, the goal of communications has long been promising accurate message reconstruction at a refined bit-by-bit level, which deliberately neglects the semantics/effectiveness aspect of information. Nevertheless, the recent development of wireless technologies and the spurt of deep learning (DL) techniques allow us to reclaim the meaning/usefulness aspect in the design of future 6G communication paradigms, where goal-oriented communication is becoming a trend. Age of Information (AoI), a well-known metric that captures the importance of information by recording the time elapsed from the generation time slot, has been extended to various variants, such as Value of Information (VoI), Urgency of Information (UoI), Age of Incorrect Information (AoII), and etc. While each of them proposes novel ways to measure the meaning/usefulness aspect of information, there is not yet an integrated framework encompassing all of them. To this end, we propose a novel tensor-based approach, the Goal-oriented Tensor (GoT), to unify them, which also allows more flexible and fine-grained goal characterizations. Following the proposed GoT, we architect a holistic goal-oriented framework to enable goal-oriented semantic communications, in which information perception, dissemination, and control-plane decisions are implemented in accordance with specific goals. Finally, we outline several open challenges to fulfill the vision of the GoT framework.
