Semantic Communication Through the Lens of Context-Dependent Channel Modeling

2026-02-26

AI Summary

This work addresses the issue of semantic noise in semantic communication over ideal physical channels by proposing a context-aware, state-dependent semantic channel model, wherein context is explicitly modeled for the first time as a virtual state variable of the semantic channel. Within a probabilistic semantic communication framework and leveraging information-theoretic methods, the authors systematically analyze the representational capacity of semantic encoders and derive channel capacity or achievable rates under several representative semantic noise scenarios. The study offers a novel characterization of the fundamental performance limits of semantic communication and underscores the pivotal role of contextual information in semantic transmission.

Technology Category

• Machine Learning: Information Theory
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Cognitive Modeling & Cognitive Systems: Symbolic Representations

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Security and Privacy: Large-scale security measurements

Abstract

Semantic communication has emerged as a promising paradigm for next-generation networks, yet several fundamental challenges remain unresolved. Building on the probabilistic model of semantic communication and leveraging the concept of context, this paper examines a specific subclass of semantic communication problems, where semantic noise originates solely from the semantic channel, assuming an ideal physical channel. To model this system, we introduce a virtual state-dependent channel, where the state-representing context-plays a crucial role in shaping communication. We further analyze the representational capability of the semantic encoder and explore various semantic communication scenarios in the presence of semantic noise, deriving capacity results for some cases and achievable rates for others.

Problem

• semantic communication
• semantic noise
• context
• channel modeling
• semantic channel

Innovation

• semantic communication
• context-dependent channel
• state-dependent channel
• semantic noise
• channel capacity