Thursday 23 January 2025
A team of researchers has developed a new approach to improve the security and efficiency of satellite communications. The method, known as heuristic deep reinforcement learning (HDRL), uses artificial intelligence to optimize the phase shifts of reconfigurable intelligent surfaces (RIS) in order to maximize secure data transmission.
Satellite communication systems are vulnerable to eavesdropping due to their inherent wireless nature. To address this issue, researchers have proposed various methods, including rate-splitting multiple access (RSMA), which divides data into common and private streams to enhance security. However, optimizing the performance of RSMA-based satellite communications is a complex task, requiring careful management of phase shifts at RIS elements.
The HDRL approach developed by the team addresses this challenge by integrating heuristic algorithms with deep reinforcement learning (DRL). The method first reduces the action space by selecting promising phase shift configurations using greedy and heuristic algorithms. This allows the AI agent to focus on exploring a smaller, more efficient search space.
In simulations, the HDRL approach significantly outperformed traditional DRL and other benchmark algorithms in terms of secure sum rate and computational efficiency. The results also showed that RSMA outperforms non-orthogonal multiple access (NOMA) schemes in secure communication performance, particularly when combined with an increased number of RIS elements.
The researchers’ findings have important implications for the development of secure satellite communications systems. By leveraging HDRL to optimize RIS phase shifts, system designers can improve the reliability and security of data transmission, enabling more efficient and effective communication networks.
In practice, the HDRL approach could be used to optimize RIS phase shifts in various scenarios, such as in integrated satellite-aerial-terrestrial relay networks or in cognitive satellite-terrestrial systems. The method’s ability to balance exploration and exploitation efficiency makes it an attractive solution for real-world applications.
The team’s research highlights the potential of HDRL in addressing complex optimization problems in wireless communication systems. As the demand for secure and efficient data transmission continues to grow, the development of innovative AI-powered solutions like HDRL is crucial for advancing the field of satellite communications.
Cite this article: “AI-Optimized Phase Shifts for Secure Satellite Communications”, The Science Archive, 2025.
Satellite Communications, Heuristic Deep Reinforcement Learning, Reconfigurable Intelligent Surfaces, Artificial Intelligence, Secure Data Transmission, Phase Shifts, Rate-Splitting Multiple Access, Non-Orthogonal Multiple Access, Cognitive Satellite-Terrestrial Systems
