mlsanityData to 14 Oct 2026

Towards Semantic Communication Protocols: A Probabilistic Logic Perspective

arXiv 2207.03920 ↗First posted 8 Jul 2022cs.IT cs.AI cs.LGcited by 10

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Abstract (arXiv)

Classical medium access control (MAC) protocols are interpretable, yet their task-agnostic control signaling messages (CMs) are ill-suited for emerging mission-critical applications. By contrast, neural network (NN) based protocol models (NPMs) learn to generate task-specific CMs, but their rationale and impact lack interpretability. To fill this void, in this article we propose, for the first time, a semantic protocol model (SPM) constructed by transforming an NPM into an interpretable symbolic graph written in the probabilistic logic programming language (ProbLog). This transformation is viable by extracting and merging common CMs and their connections while treating the NPM as a CM generator. By extensive simulations, we corroborate that the SPM tightly approximates its original NPM while occupying only 0.02% memory. By leveraging its interpretability and memory-efficiency, we demonstrate several SPM-enabled applications such as SPM reconfiguration for collision-avoidance, as well as comparing different SPMs via semantic entropy calculation and storing multiple SPMs to cope with non-stationary environments.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 34 of its 93 references that are in the map. First posted on arXiv 8 Jul 2022.

Builds on

34 of 93 references.

References · 34

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22 more
  1. 2020Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applicationscited by 62
  2. 2020Toward a 6G AI-Native Air Interfacecited by 55
  3. 2018Diversity in Machine Learningcited by 48
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  5. 20206G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunitiescited by 24
  6. 2022Resource allocation for text semantic communicationscited by 24
  7. 2019Scalable Cell-Free Massive MIMO Systemscited by 23
  8. 2020Semantic Communications for Speech Signalscited by 19
  9. 2020Towards Joint Learning of Optimal MAC Signaling and Wireless Channel Accesscited by 18
  10. 2021The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learningcited by 15
  11. 2019Grant-free Non-orthogonal Multiple Access for IoT: A Surveycited by 12
  12. 2019A New Framework for Multi-Agent Reinforcement Learning -- Centralized Training and Exploration with Decentralized Execution via Policy Distillationcited by 12
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  15. 2022A Unified Approach to Semantic Information and Communication based on Probabilistic Logiccited by 5
  16. 2020MAC Protocol Design Optimization Using Deep Learningcited by 4
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  18. 2020Toward End-to-End, Full-Stack 6G Terahertz Networkscited by 3
  19. 2021Learning Emergent Random Access Protocol for LEO Satellite Networkscited by 3
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  22. 2020Understanding Uncertainty of Edge Computing: New Principle and Design Approachcited by 1

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Cited by year

Citing papers per year: 2022: 2; 2023: 4; 2024: 3; 2025: 1.

10 papers.

  1. 2022Neuro-Symbolic Causal Reasoning Meets Signaling Game for Emergent Semantic Communicationscited by 21
  2. 2023Language-Oriented Communication with Semantic Coding and Knowledge Distillation for Text-to-Image Generationcited by 17
  3. 2023Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approachcited by 14
  4. 2024Language-Oriented Semantic Latent Representation for Image Transmissioncited by 10
  5. 2023Towards Semantic Communication Protocols for 6G: From Protocol Learning to Language-Oriented Approachescited by 7
  6. 2022Enabling the Wireless Metaverse via Semantic Multiverse Communicationcited by 6
  7. 2023Energy-Efficient Downlink Semantic Generative Communication with Text-to-Image Generatorscited by 3
  8. 2024Knowledge Distillation from Language-Oriented to Emergent Communication for Multi-Agent Remote Controlcited by 3
  9. 2024Energy-Efficient Edge Learning via Joint Data Deepening-and-Prefetchingcited by 2
  10. 2025LLM-Empowered Agentic MAC Protocols: A Dynamic Stackelberg Game Approachcited by 0