# LLM-Aided Joint Secrecy Precoding and Trajectory for RSMA-Based Heterogeneous UAV Networks

arXiv: https://arxiv.org/abs/2507.17188 · HTML: https://mlsanity.com/paper/2507.17188/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 54 references that are in the map.
- First posted on arXiv 23 Jul 2025.

## Builds on (most cited first, 23 of 23)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Conservative Q-Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2006.04779/) (2020)
- [Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction](https://mlsanity.com/paper/1906.00949/) (2019)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [Multi-objective Aerial Collaborative Secure Communication Optimization via Generative Diffusion Model-enabled Deep Reinforcement Learning](https://mlsanity.com/paper/2407.08914/) (2024)
- [Large Language Model Based Multi-Objective Optimization for Integrated Sensing and Communications in UAV Networks](https://mlsanity.com/paper/2410.05062/) (2024)
- [What Will the Future of UAV Cellular Communications Be? A Flight from 5G to 6G](https://mlsanity.com/paper/2105.04842/) (2021)
- [Rate-Splitting Multiple Access: Fundamentals, Survey, and Future Research Trends](https://mlsanity.com/paper/2201.03192/) (2022)
- [Evolutionary Multi-Objective Reinforcement Learning Based Trajectory Control and Task Offloading in UAV-Assisted Mobile Edge Computing](https://mlsanity.com/paper/2202.12028/) (2022)
- [Multi-objective Optimization for Multi-UAV-assisted Mobile Edge Computing](https://mlsanity.com/paper/2404.15292/) (2024)
- [A Secure and Intelligent Data Sharing Scheme for UAV-Assisted Disaster Rescue](https://mlsanity.com/paper/2211.12988/) (2022)
- [Multi-Objective Optimization for UAV Swarm-Assisted IoT with Virtual Antenna Arrays](https://mlsanity.com/paper/2308.01511/) (2023)
- [Collaborative Ground-Space Communications via Evolutionary Multi-objective Deep Reinforcement Learning](https://mlsanity.com/paper/2404.07450/) (2024)
- [Energy Efficient UAV Communication with Energy Harvesting](https://mlsanity.com/paper/1911.00351/) (2019)
- [UAV Trajectory and Beamforming Optimization for Integrated Periodic Sensing and Communication](https://mlsanity.com/paper/2203.10223/) (2022)
- [SpaceRIS: LEO Satellite Coverage Maximization in 6G Sub-THz Networks by MAPPO DRL and Whale Optimization](https://mlsanity.com/paper/2307.15469/) (2023)
- [Deep Graph Reinforcement Learning for UAV-Enabled Multi-User Secure Communications](https://mlsanity.com/paper/2504.01446/) (2025)
- [LLM-guided DRL for Multi-tier LEO Satellite Networks with Hybrid FSO/RF Links](https://mlsanity.com/paper/2505.11978/) (2025)
- [Joint User Association and Beamforming Design for ISAC Networks with Large Language Models](https://mlsanity.com/paper/2506.05637/) (2025)
- [Optimization of Rate Allocation and Power Control for Rate Splitting Multiple Access (RSMA)](https://mlsanity.com/paper/1903.08068/) (2019)
- [Deep Reinforcement Learning for RIS-aided Multiuser Full-Duplex Secure Communications with Hardware Impairments](https://mlsanity.com/paper/2208.07820/) (2022)
- [On the Energy Consumption of Rotary Wing and Fixed Wing UAVs in Flying Networks](https://mlsanity.com/paper/2406.19009/) (2024)

## Built on by (most cited first, 1 of 1)

- [Can LLM Agents Solve Collaborative Tasks? A Study on Urgency-Aware Planning and Coordination](https://mlsanity.com/paper/2508.14635/) (2025)

## Abstract (arXiv)

This paper investigates secure communications in rate-splitting multiple access (RSMA) enabled heterogeneous UAV networks, where multiple UAVs collaboratively serve ground terminals in the presence of eavesdroppers. By jointly considering secrecy rate maximization and propulsion energy consumption minimization, we formulate a multi-objective optimization problem involving UAV trajectory design, service association, power allocation, and secrecy precoding under mobility, collision-avoidance, service-capacity, and communication constraints. The formulated problem is highly non-convex due to the coupling among UAV trajectories, RSMA transmission variables, and secrecy constraints. To address the resulting non-convex and highly coupled optimization problem, we propose a hierarchical optimization framework. The inner layer uses a semidefinite relaxation (SDR)-based S2DC algorithm combining penalty functions and difference-of-convex (D.C.) programming to solve the secrecy precoding problem with fixed UAV positions. The outer layer introduces a Large Language Model (LLM)-guided heuristic multi-agent reinforcement learning approach (LLM-HeMARL) for trajectory optimization. LLM-HeMARL efficiently incorporates LLM-generated expert heuristic policy, enabling UAVs to learn energy-aware, security-driven trajectories without the inference overhead of real-time LLM calls. The simulation results show that our method outperforms existing baselines in secrecy rate and energy efficiency, with consistent robustness across varying UAV swarm sizes and random seeds.