Semantic Communication with Distribution Learning through Sequential Observations

2025-08-14

AI Summary

This work addresses the learnability of source semantic distributions at the receiver in semantic communication, where prior knowledge is unavailable and learning must rely solely on sequential observations. Methodologically, it integrates information-theoretic analysis, matrix rank characterization, convergence modeling of distribution estimation, and semantic distortion quantification. The paper establishes the first statistical learning theory framework tailored to semantic communication, revealing a fundamental trade-off between semantic performance and system learnability. It proves that full rankness of the effective transmission matrix is a necessary condition for distribution learnability and quantifies how estimation error propagates into semantic distortion. End-to-end experiments on CIFAR-10 demonstrate that encoding strategies critically influence both learning speed and semantic fidelity, explicitly characterizing the intrinsic trade-off between learning efficiency and reconstruction quality across different coding schemes.

Technology Category

• Machine Learning: Information Theory
• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Semantic communication aims to convey meaning rather than bit-perfect reproduction, representing a paradigm shift from traditional communication. This paper investigates distribution learning in semantic communication where receivers must infer the underlying meaning distribution through sequential observations. While semantic communication traditionally optimizes individual meaning transmission, we establish fundamental conditions for learning source statistics when priors are unknown. We prove that learnability requires full rank of the effective transmission matrix, characterize the convergence rate of distribution estimation, and quantify how estimation errors translate to semantic distortion. Our analysis reveals a fundamental trade-off: encoding schemes optimized for immediate semantic performance often sacrifice long-term learnability. Experiments on CIFAR-10 validate our theoretical framework, demonstrating that system conditioning critically impacts both learning rate and achievable performance. These results provide the first rigorous characterization of statistical learning in semantic communication and offer design principles for systems that balance immediate performance with adaptation capability.

Problem

• Study distribution learning in semantic communication systems
• Establish conditions for learning source statistics without priors
• Analyze trade-off between immediate performance and long-term learnability

Innovation

• Distribution learning via sequential semantic observations
• Full rank matrix ensures learnability and convergence
• Trade-off between immediate performance and long-term learnability