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Cognitive Semantic Augmentation LEO Satellite Networks for Earth Observation

Hong-fu Chou, Vu Nguyen Ha, Prabhu Thiruvasagam, Thanh-Dung Le, Geoffrey Eappen, Ti Ti Nguyen, Duc Dung Tran, Luis M. Garces-Socarras, Juan Carlos Merlano-Duncan, Symeon Chatzinotas

TL;DR

A novel framework for semantic communication in EO satellite networks, aimed at enhancing data transmission efficiency and system performance through cognitive processing techniques, and designed for next-generation satellite networks supporting 6G.

Abstract

Earth observation (EO) systems are essential for mapping, catastrophe monitoring, and resource management, but they have trouble processing and sending large amounts of EO data efficiently, especially for specialized applications like agriculture and real-time disaster response. This paper presents a novel framework for semantic communication in EO satellite networks, aimed at enhancing data transmission efficiency and system performance through cognitive processing techniques. The proposed system leverages Discrete Task-Oriented Joint Source-Channel Coding (DT-JSCC) and Semantic Data Augmentation (SA) integrate cognitive semantic processing with inter-satellite links, enabling efficient analysis and transmission of multispectral imagery for improved object detection, pattern recognition, and real-time decision-making. Cognitive Semantic Augmentation (CSA) is introduced to enhance a system's capability to process and transmit semantic information, improving feature prioritization, consistency, and adaptation to changing communication and application needs. The end-to-end architecture is designed for next-generation satellite networks, such as those supporting 6G, demonstrating significant improvements in fewer communication rounds and better accuracy over federated learning.

Cognitive Semantic Augmentation LEO Satellite Networks for Earth Observation

TL;DR

A novel framework for semantic communication in EO satellite networks, aimed at enhancing data transmission efficiency and system performance through cognitive processing techniques, and designed for next-generation satellite networks supporting 6G.

Abstract

Earth observation (EO) systems are essential for mapping, catastrophe monitoring, and resource management, but they have trouble processing and sending large amounts of EO data efficiently, especially for specialized applications like agriculture and real-time disaster response. This paper presents a novel framework for semantic communication in EO satellite networks, aimed at enhancing data transmission efficiency and system performance through cognitive processing techniques. The proposed system leverages Discrete Task-Oriented Joint Source-Channel Coding (DT-JSCC) and Semantic Data Augmentation (SA) integrate cognitive semantic processing with inter-satellite links, enabling efficient analysis and transmission of multispectral imagery for improved object detection, pattern recognition, and real-time decision-making. Cognitive Semantic Augmentation (CSA) is introduced to enhance a system's capability to process and transmit semantic information, improving feature prioritization, consistency, and adaptation to changing communication and application needs. The end-to-end architecture is designed for next-generation satellite networks, such as those supporting 6G, demonstrating significant improvements in fewer communication rounds and better accuracy over federated learning.

Paper Structure

This paper contains 14 sections, 5 equations, 5 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Formation of semantic communication networks
  3. Orientation and attributes of semantic communication
  4. Semantic joint source-channel coding
  5. On-board semantic feature processing and deep learning for Resource-efficient EO systems
  6. Cognitive Semantic EO system model
  7. Semantic data transmission
  8. Cognitive semantic augmentation
  9. End-to-end semantic-augmented communication
  10. Numerical Results
  11. Channel model
  12. Physical layer transmission
  13. CSA satellite network
  14. Conclusion

Figures (5)

  • Figure 1: The proposed cognitive semantic augmentation LEO satellites for enhanced EO systems.
  • Figure 2: The proposed transmitter and receiver for cognitive semantic satellite networks.
  • Figure 3: Top1 accuracy using DT-JSCC based on 16APSK LEO Rician and LEO Rayleigh channel.
  • Figure 4: Top1 accuracy of confusion matrix using DTJSCC based on 16APSK Rician channel where PSNR=12dB and K=128.
  • Figure 5: Top1 accuracy of CSA satellite networks using DT-JSCC over 16APSK LEO Rician channel while DT-JSCC training at 4dB and CSA/federated learning training at 12dB.

Theorems & Definitions (1)

  • Definition 1