Low-altitude UAV Friendly-Jamming for Satellite-Maritime Communications via Generative AI-enabled Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2501.15468v2
• Date: Mon, 10 Nov 2025 02:49:13 GMT
• Title: Low-altitude UAV Friendly-Jamming for Satellite-Maritime Communications via Generative AI-enabled Deep Reinforcement Learning
• Authors: Jiawei Huang, Aimin Wang, Geng Sun, Jiahui Li, Jiacheng Wang, Dusit Niyato, Victor C. M. Leung,
• Abstract summary: This paper presents a satellite-maritime communication system assisted by low-altitude unmanned aerial vehicle (UAV) friendly-jamming.<n>We formulate a secure satellite-maritime communication multi-objective optimization problem (SSMCMOP)<n>In order to solve the dynamic and long-term optimization problem, we reformulate it into a Markov decision process.<n>We then propose a transformer-enhanced soft actor-critic (TransSAC) algorithm, which is a generative artificial intelligence-enabled deep reinforcement learning approach.
• Score: 72.23178920029957
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low Earth orbit (LEO) satellites can be used to assist maritime wireless communications for wide-area data transmission. However, the extensive coverage of LEO satellites, combined with the openness of channels, can cause the communication process to suffer from security risks. This paper presents a LEO satellite-maritime communication system assisted by low-altitude unmanned aerial vehicle (UAV) friendly-jamming to ensure data security at the physical layer. Since such a system requires balancing the conflicting performance metrics of secrecy rate and energy consumption of the UAV to meet evolving scenario demands, we formulate a secure satellite-maritime communication multi-objective optimization problem (SSMCMOP). In order to solve the dynamic and long-term optimization problem, we reformulate it into a Markov decision process. We then propose a transformer-enhanced soft actor-critic (TransSAC) algorithm, which is a generative artificial intelligence-enabled deep reinforcement learning approach to solve the reformulated problem, thus capturing strong temporal correlations and diversely exploring weights. Simulation results demonstrate that the TransSAC algorithm outperforms comparative approaches and algorithms, maximizing the secrecy rate while effectively minimizing the energy consumption of the UAV. Moreover, the results identify more suitable constraints for the system.

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