AI 中文总结
针对工业室内无人机网络的AP关联问题，提出支持RHS的无小区上行链路MIMO低复杂度排序规则，仿真显示该方法可提升无人机数据速率、频谱效率等性能。
AI 中文摘要
室内工业无人机上行链路网络面临来自货架、金属设备和生产设施的严重阻塞与阴影效应。无人机通常成簇飞行并沿相似的直线巡检路线以固定高度飞行，这些特性使得传统小小区部署适用性较差，尤其在需要高可靠性、连续覆盖及为弱信号无人机提供良好服务的场景下。无小区网络可通过分布式接入点（AP）和以无人机为中心的通信提升鲁棒性。支持可重构全息表面（RHS）的AP可提供可编程模拟接收波束并生成RHS后标量观测值，这些观测值在中央处理器（CPU）处联合处理，以较低硬件成本实现分布式上行链路MIMO检测。传统AP关联依赖距离、大尺度衰落或用户特定数字合并器得到的合并后SINR，但本文中，单馈源RHS AP服务的所有无人机共享一个幅度受限的接收模式和一个标量AP输出，因此从该物理输出推导类SINR得分，在弱AP间干扰相关条件下，将CPU侧对数行列式目标近似为加性单AP代理，得到低复杂度排序规则。结果表明，最近的AP并非总是最佳选择，AP与无人机的高度差可能存在最优值，更大的服务簇会带来收益递减。仿真显示，与基准方案相比，所提方法提升了最小无人机数据速率、平均频谱效率、公平性及能效。
英文摘要
Indoor industrial UAV uplink networks face serious blockage and shadowing from shelves, metal equipment, and production facilities. UAVs are also often clustered and fly along similar straight inspection routes at fixed heights. These features make traditional small-cell deployment less suitable, especially when high reliability, continuous coverage, and good service for weak UAVs are required. Cell-free networks can improve robustness through distributed access points (APs) and UAV?centric communications. Reconfigurable holographic surface (RHS)-enabled APs provide programmable analog receive beams and generate scalar post-RHS observations, which are jointly processed at the CPU for distributed uplink MIMO detection at relatively low hardware cost. Conventional AP association relies on distance, large-scale fading, or post-combining SINR obtained with user-specific digital combiners. Here, however, all UAVs served by a single-feed RHS AP share one amplitude?constrained receive pattern and one scalar AP output. We therefore derive an SINR-like score from this physical output and, under weak inter-AP disturbance correlation, approximate the CPU-side log-det objective by an additive per-AP surrogate, yielding a low-complexity ranking rule. The results show that the nearest AP is not always the best choice, the AP-UAV height difference may have an optimal value, and larger serving clusters bring diminishing returns. Simulations show that the proposed method improves the minimum UAV data rate, average spectral efficiency, fairness, and energy efficiency compared with benchmark schemes.
