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Towards Optimal Semantic Communications: Reconsidering the Role of Semantic Feature Channels

Yongjeong Oh, Jihong Park, Jinho Choi, Yo-Seb Jeon

TL;DR

The paper reframes semantic communications as an encoder–SF channel–decoder pipeline and shows that the SF channel is configurable via transmission strategies, not fixed. It provides analytical results for Gaussian sources with linear enc–dec, showing the optimal SF channel prioritizes low-noise transmission for high-variance features and connects to the rate–distortion bound as dimensionality grows. It then develops an end-to-end training framework to jointly optimize non-linear enc–dec and the SF channel for both analog and digital SC, using a mutual information constraint I( z ; ẑ ) ≤ C (analog) or I( b ; b̂ ) ≤ C (digital) and a mean-field decomposition for tractability. Finally, it introduces PHY calibration methods to realize the trained SF channel in practice, including single-user analog and multi-user digital scenarios, demonstrating superior task performance across diverse environments. Collectively, the work delivers theoretical, algorithmic, and practical tooling to realize more efficient, robust semantic communications in real wireless systems.

Abstract

This paper investigates the optimization of transmitting the encoder outputs, termed semantic features (SFs), in semantic communication (SC). We begin by modeling the entire communication process from the encoder output to the decoder input, encompassing the physical channel and all transceiver operations, as the SF channel, thereby establishing an encoder-SF channel-decoder pipeline. In contrast to prior studies that assume a fixed SF channel, we note that the SF channel is configurable, as its characteristics are shaped by various transmission and reception strategies, such as power allocation. Based on this observation, we formulate the SF channel optimization problem under a mutual information constraint between the SFs and their reconstructions, and analytically derive the optimal SF channel under a linear encoder-decoder structure and Gaussian source assumption. Building upon this theoretical foundation, we propose a joint optimization framework for the encoder-decoder and SF channel, applicable to both analog and digital SCs. To realize the optimized SF channel, we also propose a physical-layer calibration strategy that enables real-time power control and adaptation to varying channel conditions. Simulation results demonstrate that the proposed SF channel optimization achieves superior task performance under various communication environments.

Towards Optimal Semantic Communications: Reconsidering the Role of Semantic Feature Channels

TL;DR

The paper reframes semantic communications as an encoder–SF channel–decoder pipeline and shows that the SF channel is configurable via transmission strategies, not fixed. It provides analytical results for Gaussian sources with linear enc–dec, showing the optimal SF channel prioritizes low-noise transmission for high-variance features and connects to the rate–distortion bound as dimensionality grows. It then develops an end-to-end training framework to jointly optimize non-linear enc–dec and the SF channel for both analog and digital SC, using a mutual information constraint I( z ; ẑ ) ≤ C (analog) or I( b ; b̂ ) ≤ C (digital) and a mean-field decomposition for tractability. Finally, it introduces PHY calibration methods to realize the trained SF channel in practice, including single-user analog and multi-user digital scenarios, demonstrating superior task performance across diverse environments. Collectively, the work delivers theoretical, algorithmic, and practical tooling to realize more efficient, robust semantic communications in real wireless systems.

Abstract

This paper investigates the optimization of transmitting the encoder outputs, termed semantic features (SFs), in semantic communication (SC). We begin by modeling the entire communication process from the encoder output to the decoder input, encompassing the physical channel and all transceiver operations, as the SF channel, thereby establishing an encoder-SF channel-decoder pipeline. In contrast to prior studies that assume a fixed SF channel, we note that the SF channel is configurable, as its characteristics are shaped by various transmission and reception strategies, such as power allocation. Based on this observation, we formulate the SF channel optimization problem under a mutual information constraint between the SFs and their reconstructions, and analytically derive the optimal SF channel under a linear encoder-decoder structure and Gaussian source assumption. Building upon this theoretical foundation, we propose a joint optimization framework for the encoder-decoder and SF channel, applicable to both analog and digital SCs. To realize the optimized SF channel, we also propose a physical-layer calibration strategy that enables real-time power control and adaptation to varying channel conditions. Simulation results demonstrate that the proposed SF channel optimization achieves superior task performance under various communication environments.
Paper Structure (17 sections, 4 theorems, 58 equations, 8 figures)

This paper contains 17 sections, 4 theorems, 58 equations, 8 figures.

Table of Contents

  1. Introduction
  2. System Model and Concept of SF Channel
  3. System Model
  4. SF Channel
  5. Motivation and Case Study
  6. Motivation for Joint Optimization
  7. Case Study: Analog SC with Linear Enc-Dec and Gaussian Input
  8. Proposed Joint Enc-Dec and SF Channel Optimization for SCs
  9. End-to-End Training for Analog SC
  10. End-to-End Training for Digital SC
  11. Proposed PHY Calibration for Realizing the Trained SF Channel
  12. Single-User Analog SCs
  13. Multi-User Digital SCs
  14. Simulation Results
  15. Conclusion
  16. ...and 2 more sections

Key Result

Lemma 1

For any matrix ${\bm A}$, there exists a partial permutation matrix $\tilde{\bm P}$, such that

Figures (8)

  • Figure 1: The overall analog and digital SC pipelines: Encoder--SF channel--decoder.
  • Figure 2: MSE curves over the mutual information limit $C$ and the SF vector length $M$.
  • Figure 3: The proposed end-to-end training strategy jointly optimizing the enc-dec and the SF channel for analog and digital SCs.
  • Figure 4: PSNR curves over the mutual information limit $C$ and the SF vector length $M$ for analog SCs.
  • Figure 5: PSNR curves over the mutual information limit $C$ and the SF vector length $B$ for digital SCs.
  • ...and 3 more figures

Theorems & Definitions (4)

  • Lemma 1
  • Lemma 2
  • Corollary 1
  • Theorem 1: Optimal Solution