位置、切片与调度： tethered毫米波无人机gNB的分层O-RAN控制
Place, Slice and Schedule: Hierarchical O-RAN Control of a Tethered mmWave UAV-gNB
- Clemson University（克莱姆森大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究针对tethered毫米波无人机gNB的O-RAN控制问题，提出分层RIC架构，实现位置、切片与调度协同，提升eMBB和URLLC性能。
AI中文摘要：
无人机（UAV）搭载的5G新空口基站（gNB）可通过按需可重新定位的频率范围2（FR2）容量层增强地面网络。然而，这种灵活性将物理网络拓扑与无线资源管理耦合在一起：无人机移动会改变阻塞情况、信道质量和有效服务用户集合，而流量需求、队列和服务要求则以快得多的时间尺度演进。现有支持开放无线接入网（O-RAN）的无人机研究通常单独优化轨迹、部署、关联或资源分配，未协调慢速空中控制与快速用户级调度。我们利用O-RAN的解耦、关键性能指标（KPI）监控和多时间尺度无线接入网智能控制器（RIC）控制来解决该耦合问题：非实时RIC rApp使用聚合KPI和无线环境上下文共同控制 tethered无人机位置以及增强移动宽带（eMBB）/超可靠低延迟通信（URLLC）切片预算，近实时RIC xApp在该预算内分配用户级资源。我们将该xApp实现为置换等变DeepSets软演员-评论家（D-SAC）调度器，将用户视为无序集合，在Sionna RT射线追踪信道中训练。与经典和学习型调度器相比，所得分层控制器可将eMBB服务水平协议（SLA）满意度提升最高17%，URLLC按时交付率提升最高42%；学习型rApp还可将URLLC按时交付率较基准提升最高20%。
英文摘要：
Unmanned aerial vehicle (UAV)-mounted 5G New Radio base stations (gNBs) can augment terrestrial networks with an on-demand, repositionable Frequency Range 2 (FR2) capacity layer. This flexibility, however, couples the physical network topology with radio-resource management: UAV movement reshapes blockage, channel quality, and the set of effectively served users, while traffic demand, queues, and service requirements evolve at a much faster timescale. Existing Open Radio Access Network (O-RAN)-enabled UAV studies optimize trajectory, deployment, association, or resource allocation, but typically in isolation, without coordinating slow aerial control with fast per-user scheduling. We instead exploit O-RAN disaggregation, Key Performance Indicator (KPI) monitoring, and multi-timescale RAN Intelligent Controller (RIC) control to address this coupling: a Non-Real-Time RIC rApp uses aggregated KPIs and radio-environment context to jointly control tethered UAV placement and the enhanced Mobile Broadband (eMBB)/Ultra-Reliable Low-Latency Communication (URLLC) slice budget, while a Near-Real-Time RIC xApp allocates per-user resources within that budget. We realize this xApp as a permutation-equivariant DeepSets Soft Actor-Critic (D-SAC) scheduler that treats the users as an unordered set, trained in a Sionna RT ray traced channel. The resulting hierarchical controller improves eMBB SLA satisfaction by up to 17% and URLLC on-time delivery by up to 42% over classical and learned schedulers; the learned rApp further raises URLLC on-time delivery by up to 20% over baselines.
