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Semantic Communication Through the Lens of Context-Dependent Channel Modeling

Javad Gholipour, Rafael F. Schaefer, Gerhard P. Fettweis

TL;DR

This paper examines a specific subclass of semantic communication problems, where semantic noise originates solely from the semantic channel, assuming an ideal physical channel.

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.

Semantic Communication Through the Lens of Context-Dependent Channel Modeling

TL;DR

This paper examines a specific subclass of semantic communication problems, where semantic noise originates solely from the semantic channel, assuming an ideal physical channel.

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.
Paper Structure (11 sections, 5 theorems, 6 equations, 5 figures)

This paper contains 11 sections, 5 theorems, 6 equations, 5 figures.

Table of Contents

  1. Introduction
  2. Main Results
  3. Preliminaries
  4. Semantic Channel Model
  5. Representationally Capable Semantic Sender
  6. Semantic Communication over a Semantic Channel
  7. Sender and Receiver with Similar Background Knowledge
  8. Receiver’s Background Knowledge as a Subset of the sender’s Knowledge
  9. Sender’s Background Knowledge as a Subset of the Receiver’s Knowledge
  10. Sender and Receiver Share Partial Background Knowledge
  11. Conclusion

Key Result

Theorem 1

The semantic sender illustrated in Figure Fig1, is representationally capable if and only if the semantic information rate does not exceed its representational capacity:

Figures (5)

  • Figure 1: Interrelationship between semantics $W$ and the messages $M$Gholipour, (a): in general, and (b): for a given context $Q=q$.
  • Figure 2: Semantic channel modeled as a context-dependent channel.
  • Figure 3: Semantic channel modeled as a context-dependent channel, where the semantic receiver's background knowledge is a subset of the semantic sender's knowledge.
  • Figure 4: Semantic channel modeled as a context-dependent channel, where the semantic sender’s background knowledge is a subset of the semantic receiver’s knowledge.
  • Figure 5: Semantic channel modeled as a context-dependent channel, where the semantic sender and receiver share partial background knowledge.

Theorems & Definitions (5)

  • Theorem 1
  • Theorem 2
  • Theorem 3
  • Theorem 4
  • Theorem 5