发表机构
Beijing University of Posts and Telecommunications; Peng Cheng Laboratory; Shanghai Jiao Tong University（北京邮电大学; 鹏城实验室; 上海交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对无人机部署忽略飞行控制约束的问题，提出LLM增强的多智能体强化学习框架LERE，演化混合奖励优化3D部署与功率分配，显著提升频谱效率并减少位置误差。
AI 中文摘要
无小区大规模多输入多输出（CF-mMIMO）是未来无线网络的一种有前景的范式，提供以用户为中心的服务和协作覆盖。通过使用无人机（UAV）作为空中接入点，CF-mMIMO网络可以利用无人机的移动性来增强三维（3D）覆盖和频谱效率（SE）。然而，大多数现有的用于通信优化的无人机部署研究通常假设无人机遵循理想的质点运动（IM），忽略了实际飞行执行中的飞行控制约束和有限时域位置误差。因此，基于IM训练的部署策略在实际中可能严重退化或难以执行。受此启发，我们将每架无人机建模为具有级联飞行控制器的六自由度四旋翼刚体，以捕捉飞行控制约束运动（FM）对通信优化的影响。基于该模型，我们在FM下制定了一个联合无人机3D部署和功率分配问题，以最大化CF-mMIMO网络中的下行平均SE。为了解决这个问题，我们提出了LERE，一个大型语言模型（LLM）增强的多智能体强化学习（MARL）框架。在LERE中，LLM通过多级反馈演化具有全局和局部组件的混合奖励。演化的混合奖励指导MARL策略优化并促进多无人机协作。实验结果表明，LERE在显著减少无人机位置误差的同时，实现了比奖励设计基线更高的SE。值得注意的是，在FM执行下测试时，基于FM训练的LERE策略比其基于IM训练的对应策略实现了60.49%的SE增益，证实了将飞行控制约束纳入无人机辅助CF-mMIMO优化的必要性。
英文摘要
Cell-free massive multiple-input multiple-output (CF-mMIMO) is a promising paradigm for future wireless networks, providing user-centric services and cooperative coverage. By using unmanned aerial vehicles (UAVs) as aerial access points, CF-mMIMO networks can exploit UAV mobility to enhance three-dimensional (3D) coverage and spectral efficiency (SE). However, most existing studies on UAV deployment for communication optimization typically assume that UAVs follow ideal point-mass motion (IM), neglecting flight-control constraints and finite-horizon position errors in real flight execution. Consequently, IM-trained deployment policies may degrade severely or become difficult to execute in practice. Motivated by this, we model each UAV as a six-degree-of-freedom quadrotor rigid body with a cascaded flight controller to capture the impact of flight-control-constrained motion (FM) on communication optimization. Based on this model, we formulate a joint UAV 3D deployment and power allocation problem under FM to maximize the downlink average SE in CF-mMIMO networks. To address this problem, we propose LERE, a large language model (LLM)-enhanced multi-agent reinforcement learning (MARL) framework. In LERE, the LLM evolves hybrid rewards with both global and local components via multi-level feedback. The evolved hybrid rewards guide MARL policy optimization and promote multi-UAV cooperation. Experimental results demonstrate that LERE achieves higher SE than reward-design baselines while substantially reducing UAV position errors. Notably, when tested under FM execution, the FM-trained LERE policy achieves a 60.49\% SE gain over its IM-trained counterpart, confirming the necessity of incorporating flight-control constraints into UAV-enabled CF-mMIMO optimization.
Comments13 pages. 12 figures
