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A Joint JSCC-Resource Allocation Framework for QoS-Aware Semantic Communication in LEO Satellite-based EO Missions

Hung Nguyen-Kha, Ti Ti Nguyen, Vu Nguyen Ha, Eva Lagunas, Symeon Chatzinotas, Bjorn Ottersten

Abstract

In Earth observation (EO) missions with Low Earth orbit (LEO) satellites, high-resolution image acquisition generates a massive data volume that poses a significant challenge for transmission under the limited satellite power budget, while LEO movement introduces dynamic systems. To enable efficient image transmission, this paper employs semantic communication (SemCom) with joint source-channel coding (JSCC), which focuses on transmitting meaningful information to reduce power consumption. Under a quality-of-service (QoS) requirement defined by image reconstruction quality, this work aims to minimize the total transmit power by jointly optimizing the JSCC encoder-decoder parameters and resource allocation. However, the implicit relationship among JSCC parameters, link quality, and image quality, coupled with the presence of mixed integer-continuous variables, makes the problem difficult to solve directly. To address this, a curve-fitting model is proposed to approximate the JSCC compression-SNR-quality relationship. Then, the joint compression ratio-resource allocation (JCRRA) algorithm is proposed to address the underlying problem. Numerical results demonstrate that the proposed method achieves substantial power savings compared to both greedy algorithms and conventional transmission paradigms.

A Joint JSCC-Resource Allocation Framework for QoS-Aware Semantic Communication in LEO Satellite-based EO Missions

Abstract

In Earth observation (EO) missions with Low Earth orbit (LEO) satellites, high-resolution image acquisition generates a massive data volume that poses a significant challenge for transmission under the limited satellite power budget, while LEO movement introduces dynamic systems. To enable efficient image transmission, this paper employs semantic communication (SemCom) with joint source-channel coding (JSCC), which focuses on transmitting meaningful information to reduce power consumption. Under a quality-of-service (QoS) requirement defined by image reconstruction quality, this work aims to minimize the total transmit power by jointly optimizing the JSCC encoder-decoder parameters and resource allocation. However, the implicit relationship among JSCC parameters, link quality, and image quality, coupled with the presence of mixed integer-continuous variables, makes the problem difficult to solve directly. To address this, a curve-fitting model is proposed to approximate the JSCC compression-SNR-quality relationship. Then, the joint compression ratio-resource allocation (JCRRA) algorithm is proposed to address the underlying problem. Numerical results demonstrate that the proposed method achieves substantial power savings compared to both greedy algorithms and conventional transmission paradigms.
Paper Structure (16 sections, 2 theorems, 15 equations, 6 figures, 2 tables, 3 algorithms)

This paper contains 16 sections, 2 theorems, 15 equations, 6 figures, 2 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. LEO-UE Association Model
  4. SemCom Model
  5. Transmisison Model
  6. Problem Formulation
  7. Proposed Solution
  8. Curve-Fitting-based Approximation
  9. Discrete Relaxation and Problem Approximation
  10. Other Benchmarks
  11. JSCC-based Greedy Algorithm
  12. JPEG-Compression-based Greedy Algorithm
  13. Numerical Results
  14. Conclusion
  15. Proof of Theorem \ref{['theorem: transform prob']}
  16. ...and 1 more sections

Key Result

Theorem 1

Problem $(\mathcal{P}_{1})$ can be transformed into an equivalent problem $(\mathcal{P}_{2})$ (has the same optimal solution) as

Figures (6)

  • Figure 1: Satellite Earth-observation system model.
  • Figure 2: Reconstruction quality versus compression ratio and SNR.
  • Figure 3: Curve-Fitting-based Approximation.
  • Figure 4: Average transmit power versus delay requirement.
  • Figure 5: Average transmit power versus number of images per user.
  • ...and 1 more figures

Theorems & Definitions (2)

  • Theorem 1
  • Proposition 1