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Related Experiment Videos

Reinforcement Learning for Secure Semantic LEO Satellite Networks: Joint Fidelity-Secrecy Power Allocation.

Feifei Zhou1,2, Xiaorong Zhu1

  • 1College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.

Sensors (Basel, Switzerland)
|May 4, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a secure framework for semantic communications in satellite networks, balancing information meaning and secrecy. A reinforcement learning approach optimizes power and semantic weighting for enhanced security and fidelity.

Keywords:
LEO satellite networksphysical-layer securityreinforcement learningsemantic communicationssemantic fidelity

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Area of Science:

  • Wireless Communications
  • Information Theory
  • Network Security

Background:

  • Semantic communications are crucial for 6G networks, transmitting information meaning over bit accuracy.
  • Low Earth Orbit (LEO) satellite links face significant eavesdropping risks due to their broadcast nature and wide coverage.
  • Existing security measures may not adequately address the unique challenges of semantic transmissions in satellite environments.

Purpose of the Study:

  • To develop a unified theoretical and algorithmic framework for secure semantic downlink transmission in satellite networks.
  • To quantitatively characterize the trade-off between semantic fidelity and secrecy capacity under realistic constraints.
  • To propose a learning-based optimization approach for balancing semantic fidelity and information secrecy.

Main Methods:

  • An integrated mathematical model coupling semantic representation, satellite propagation, and information-theoretic secrecy was developed.
  • A joint semantic security cost function was defined to analyze the fidelity-secrecy trade-off.
  • A reinforcement-learning-based optimization framework using an actor-critic agent was proposed for power allocation and semantic weighting.

Main Results:

  • The study quantitatively characterized the trade-off between semantic fidelity and secrecy capacity.
  • The reinforcement learning approach demonstrated autonomous control without requiring channel distribution knowledge or offline tuning.
  • Simulation results showed consistent enhancement in both semantic fidelity and secrecy performance compared to conventional methods.

Conclusions:

  • The proposed framework provides a unified approach to secure semantic communications in satellite networks.
  • The reinforcement learning optimization effectively balances semantic fidelity and information secrecy.
  • The approach shows potential as a foundational architecture for secure and intelligent semantic communications in future satellite networks.