Game-Theoretic Multi-Agent Reinforcement Learning for Swarm Trajectory Planning in Low-Altitude Wireless Networks

Engineering notes

Engineering notes will be added by the Power for Solar editorial team.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original abstract

The Low-Altitude Economy (LAE) is rapidly expanding, giving rise to low-altitude wireless networks (LAWNs), where large-scale cellular-connected unmanned aerial vehicle (UAV) deployments support heterogeneous mission-critical applications over multi-cell ground base station (GBS) infrastructures. To ensure mission success, each UAV must jointly optimize communication throughput and mission completion efficiency. In fifth-generation (5G) new radio (NR) systems, the equal resource block (RB) allocation policy induces strong strategic coupling among UAV trajectories: when a UAV enters a GBS cell, it reduces the RB share available to all co-served UAVs, thereby altering their achievable rates and trajectory incentives through shared wireless resources. Existing studies either ignore this coupling or focus on single-cell infrastructure, leaving the multi-cell, congestion-aware UAV trajectory planning problem insufficiently addressed. To fill this gap, we formulate the problem as a cooperative stochastic congestion game with a communication-and-mission-aware utility function, and propose a centralized-training decentralized-execution multi-agent proximal policy optimization (CTDE-MAPPO) algorithm to maximize social welfare under multi-cell RB congestion. Simulation results show that the proposed method outperforms QMIX, independent Q-learning, and random baselines in terms of aggregate utility and mission success rate, while achieving stable convergence within practical training budgets.

Links and sources

• Official / arXiv page
• PDF from original source

Comments