# Place, Slice and Schedule: Hierarchical O-RAN Control of a Tethered mmWave UAV-gNB

arXiv: https://arxiv.org/abs/2608.23824 · HTML: https://mlsanity.com/paper/2608.23824/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 22 references that are in the map.
- First posted on arXiv 24 Aug 2026.

## Builds on (most cited first, 14 of 14)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling](https://mlsanity.com/paper/2303.11103/) (2023)
- [Wireless Communications with Unmanned Aerial Vehicles: Opportunities and Challenges](https://mlsanity.com/paper/1602.03602/) (2016)
- [Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RAN](https://mlsanity.com/paper/2410.03737/) (2024)
- [Near-Real-Time Resource Slicing for QoS Optimization in 5G O-RAN using Deep Reinforcement Learning](https://mlsanity.com/paper/2509.14343/) (2025)
- [REAL: Reinforcement Learning-Enabled xApps for Experimental Closed-Loop Optimization in O-RAN with OSC RIC and srsRAN](https://mlsanity.com/paper/2502.00715/) (2025)
- [3D Placement and Orientation of mmWave-based UAVs for Guaranteed LoS Coverage](https://mlsanity.com/paper/2104.12993/) (2021)
- [Deep Reinforcement Learning Based Placement for Integrated Access Backhauling in UAV-Assisted Wireless Networks](https://mlsanity.com/paper/2312.14247/) (2023)
- [Graph Reinforcement Learning for QoS-Aware Load Balancing in Open Radio Access Networks](https://mlsanity.com/paper/2504.19499/) (2025)
- [Telecom World Models: Unifying Digital Twins, Foundation Models, and Predictive Planning for 6G](https://mlsanity.com/paper/2604.06882/) (2026)
- [Coverage analysis of Tethered UAV-assisted Large Scale Cellular Networks](https://mlsanity.com/paper/2304.05283/) (2023)
- [CollabORAN: A Collaborative rApp-xApp-dApp Control Architecture for Fairness-Adaptive Resource Sharing in O-RAN](https://mlsanity.com/paper/2603.20805/) (2026)

## Abstract (arXiv)

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.