From Ideal Motion to Flight-Executable Communications: LLM-Evolved Multi-UAV Deployment for Cell-Free Massive MIMO

Abstract

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.