Semantic Communication Through the Lens of Context-Dependent Channel Modeling

TL;DR

This paper investigates semantic communication with context-dependent channel modeling, analyzing the impact of semantic noise and deriving capacity and achievable rates under ideal physical channel assumptions.

Contribution

It introduces a virtual state-dependent channel model incorporating context, and analyzes semantic encoder capabilities and communication limits in this setting.

Findings

Capacity results for specific semantic noise scenarios

Achievable rates derived for various semantic communication cases

Insights into the role of context in semantic noise modeling

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.