Toward Semantic Communication Protocols: A Probabilistic Logic Perspective

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 rational...

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Bibliographic Details
Published in:IEEE journal on selected areas in communications Vol. 41; no. 8; pp. 2670 - 2686
Main Authors: Seo, Sejin, Park, Jihong, Ko, Seung-Woo, Choi, Jinho, Bennis, Mehdi, Kim, Seong-Lyun
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Access control
Artificial neural networks
Collision avoidance
Logic programming
Media Access Protocol
medium access control (MAC)
multi-agent deep reinforcement learning
Neural networks
Nonstationary environments
Probabilistic logic
probabilistic logic programming language (ProbLog)
Programming languages
protocol learning
Protocols
Reconfiguration
Semantic communication protocol
semantic information theory
Semantics
Task analysis
Vocabulary
ISSN:0733-8716, 1558-0008
Online Access:Get full text
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Summary: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.
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ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2023.3288268