Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·Jun 10·5 min read
LLM-Aided Joint Secrecy Precoding and Trajectory for RSMA-Based Heterogeneous UAV Networks
TL;DR
Secure communication in heterogeneous UAV networks is challenged by eavesdroppers and energy constraints. A hierarchical optimization framework was developed, combining semidefinite relaxation for secrecy precoding and a Large Language Model-guided reinforcement learning approach for trajectory optimization.
✦ Why It Matters
Engineers can leverage this framework to enhance secure communication and energy efficiency in UAV networks.
Key Takeaways
- Implement hierarchical optimization for UAV trajectory design.
- Utilize LLM-guided reinforcement learning for efficient path planning.
- Focus on maximizing secrecy rates in communication systems.
- Consider energy consumption alongside security in UAV operations.
Full Summary
Heterogeneous Unmanned Aerial Vehicle (UAV) networks face security threats from eavesdroppers while needing to optimize energy consumption. This study introduces a hierarchical optimization framework that addresses a complex multi-objective problem involving UAV trajectory design, service association, power allocation, and secrecy precoding.
The inner layer employs a semidefinite relaxation (SDR)-based algorithm to solve secrecy precoding with fixed UAV positions, while the outer layer utilizes a Large Language Model (LLM)-guided heuristic multi-agent reinforcement learning (LLM-HeMARL) approach for trajectory optimization. This innovative method allows UAVs to learn energy-efficient and secure paths without the computational burden of real-time LLM calls.
Simulation results indicate that the proposed approach outperforms existing methods, achieving higher secrecy rates and better energy efficiency, with consistent performance across different UAV swarm sizes and random conditions. These findings suggest significant advancements in secure communication strategies for UAV networks.