In modern cell-less wireless networks, mobility management is undergoing a significant transformation, transitioning from single-link handover management to a more adaptable multi-connectivity cluster reconfiguration approach, including often conflicting objectives like energy-efficient power allocation and satisfying varying reliability requirements. In this work, we address the challenge of dynamic clustering and power allocation for unmanned aerial vehicle (UAV) communication in wireless interference networks. Our objective encompasses meeting varying reliability demands, minimizing power consumption, and reducing the frequency of cluster reconfiguration. To achieve these objectives, we introduce a novel approach based on reinforcement learning using a masked soft actor-critic algorithm, specifically tailored for dynamic clustering and power allocation.

翻译：在现代无蜂窝无线网络中，移动性管理正经历重大变革，从单链路切换管理转向更灵活的多连接簇重构方法，其中常常包含相互冲突的目标，如节能功率分配和满足多样化可靠性需求。本文针对无线干扰网络中无人机通信的动态簇与功率分配问题展开研究。我们的目标涵盖满足动态可靠性要求、最小化功耗以及降低簇重构频率。为实现这些目标，我们提出一种基于掩蔽柔性演员-评论家算法的强化学习方法，专门用于动态簇和功率分配。