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Towards Goal-Oriented Semantic Communications: New Metrics, Framework, and Open Challenges

Aimin Li, Shaohua Wu, Siqi Meng, Rongxing Lu, Sumei Sun, Qinyu Zhang

TL;DR

The paper addresses the challenge of measuring and leveraging information relevance in future goal-oriented communications by moving beyond bit-level reconstruction to semantic and effectiveness-oriented design. It introduces the Goal-oriented Tensor (GoT) as a unified, tensor-based metric that can subsume existing metrics like AoI, VoI, MSE, AoS, AoII, Cost of Actuation Error, and UoI, enabling fine-grained goal characterization. A holistic framework is proposed where semantics perception, sparse semantics-aware sampling, goal-oriented coding, and intelligent decision making are orchestrated under the GoT objective. A case study on a real-time sparse semantic sampling scenario (fire monitoring) demonstrates that a GoT-driven policy can minimize long-term real-world costs while balancing communication overhead, highlighting practical benefits for IoE and 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.

Towards Goal-Oriented Semantic Communications: New Metrics, Framework, and Open Challenges

TL;DR

The paper addresses the challenge of measuring and leveraging information relevance in future goal-oriented communications by moving beyond bit-level reconstruction to semantic and effectiveness-oriented design. It introduces the Goal-oriented Tensor (GoT) as a unified, tensor-based metric that can subsume existing metrics like AoI, VoI, MSE, AoS, AoII, Cost of Actuation Error, and UoI, enabling fine-grained goal characterization. A holistic framework is proposed where semantics perception, sparse semantics-aware sampling, goal-oriented coding, and intelligent decision making are orchestrated under the GoT objective. A case study on a real-time sparse semantic sampling scenario (fire monitoring) demonstrates that a GoT-driven policy can minimize long-term real-world costs while balancing communication overhead, highlighting practical benefits for IoE and 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.
Paper Structure (20 sections, 1 equation, 4 figures, 1 table)

This paper contains 20 sections, 1 equation, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Beyond Bit-Oriented Transmission
  3. Overview of Semantic Communications
  4. Towards Goal-Oriented Semantic Communications
  5. Capturing the Relevance of Information: Critical Metrics
  6. Age of Information (AoI)
  7. Value of Information (VoI)
  8. Mean Square Error (MSE)
  9. Age of Synchronization (AoS)
  10. Age of Incorrect Information (AoII)
  11. Cost of Actuation Error
  12. Urgency of Information (UoI)
  13. One More Step Forward: the Goal-oriented Tensor
  14. Existing Metrics: Inherent Connection
  15. Goal-Oriented Tensor: A Unified Metric
  16. ...and 5 more sections

Figures (4)

  • Figure 1: Examples of tensor-based metric visualizations.
  • Figure 2: A sketch for the semantics-empowered goal-oriented networks.
  • Figure 3: Case study: A real-time wireless fire monitor and rescue system.
  • Figure 4: The performance comparisons among different sample policies. Here the goal is to reduce the long-term average cost caused by fire and rescue.