研究语义通信中如何通过连续观测学习数据分布。
Semantic Communication with Distribution Learning through Sequential Observations
- 基于连续观测推断源分布的统计特性，建立可学习性条件。
- 证明有效传输矩阵满秩是学习前提，量化估计误差对语义失真的影响。
- 揭示即时性能与长期可学习性的根本权衡，适合系统设计参考。
语义通信旨在传递意义而非比特级复现，代表了通信范式的转变。本文研究语义通信中的分布学习问题，即接收端需通过连续观测推断底层意义分布。传统语义通信优化单个意义的传输，而本文在先验未知条件下，建立了源统计特性学习的基本条件。我们证明了可学习性要求有效传输矩阵满秩，刻画了分布估计的收敛速率，并量化了估计误差对语义失真的影响。分析揭示了一个根本权衡：为即时语义性能优化的编码方案往往牺牲长期可学习性。在CIFAR-10上的实验验证了理论框架，表明系统条件对学习速率和性能实现具有关键影响。这些结果首次对语义通信中的统计学习提供了严格刻画，为平衡即时性能与适应能力的系统设计提供了原则。
原文摘要 · Abstract (English)
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.
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