cs.NIAug 24, 2026

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

Authors: Alireza Mohammadhosseini, Fatemeh Afghah

Organizations: Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, USA

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Abstract

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.

Figures & tables

Fig. 1 : Hierarchical O-RAN control of the tethered mmWave UAV-gNB: the rApp sets UAV 3D-placement and the slice PRB budget, and the D-SAC xApp allocates PRBs per user.
ParameterValue
Carrier frequency / band2828 GHz (n257)
PRBs / PRB bandwidth132132 / 1.441.44 MHz
Tx power / rApp, xApp slots3636 dBm / 11 s, 1010 ms
UEs / eMBB:URLLC ratio30,40,5030,40,50 / 0.6:0.40.6:0.4
Episode length100100 steps
eMBB RtargetR_{\mathrm{target}} / Ru,min⁡R_{u,\min}1010 / [2,70][2,70] Mbps
TABLE I : Simulation and training parameters.
Fig. 2 : Contribution of the rApp. The rApp improves eMBB SLA satisfaction and URLLC on-time delivery over baselines.
Fig. 3 : Performance comparison; D-SAC outperforms all classical and learned baselines.
Fig. 4 : Throughput and Jain’s fairness comparison across scheduling methods.
Fig. 5 : Scalability performance. D-SAC leads on both.
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