UAV-Based UE Nodes in 5G Networks

Updated 10 December 2025

UAV-based UE nodes are unmanned aerial systems that integrate 5G user equipment and mobile cell functions to deliver agile, resilient connectivity in airfields and industrial environments.
They employ advanced methodologies such as onboard RU/DU architectures, dynamic handover optimization, and edge computing integration to ensure low latency and high throughput.
Field experiments validate their performance by demonstrating improved service reliability, robust security measures, and efficient network slicing for mission-critical applications.

Unmanned Aerial Vehicle (UAV)-based User Equipment (UE) nodes represent a critical intersection between aerial robotics, 5G private networking, and softwarized RAN design, enabling dynamic, resilient communications in airfield and industrial settings. These systems use UAVs as platforms for both 5G UEs and, in advanced architectures, as mobile radio access nodes (Mobile Cells, MCs), extending or reinforcing on-demand network coverage and capacity. UAV-based UEs are central to applications requiring high availability, low latency, and rapid mobility—such as autonomous inspection, real-time asset tracking, drone-supported airside logistics, or emergency response augmentation—where permanent infrastructure is impractical or insufficient (Coelho et al., 10 Nov 2024, Mykytyn et al., 3 Dec 2025). The deployment and operation of UAV-based UE nodes at airfields is grounded in principles of nomadic/movable RAN design, edge computing integration, ultra-reliable low-latency communications (URLLC), and robust security.

1. UAV-Based UE Node Architectures and Roles

UAV-based UE nodes are realized as either 5G NR/COTS UEs mounted on aerial platforms, or as integral components within a mobile 5G network cell—implementing RU/DU (Option B) or even full gNB stacks onboard (Option A, RU+DU+CU)—with PDU session backhaul established via overlay networks to the 5G Core (5GC) (Coelho et al., 10 Nov 2024, Mykytyn et al., 3 Dec 2025). Architecturally:

Conventional UAV-UE: UAV carries a 5G UE (e.g., QualiPoc Android UE); data and control exchanged over private 5G infrastructure for telemetry, video uplink/download, and application traffic (Mykytyn et al., 3 Dec 2025).
UAV-mounted MC Node: UAV acts as a movable cell site with onboard RU/DU and sometimes CU, dynamically establishing RAN coverage in coordination with fixed infrastructure via Xn handover and overlay backhaul (Coelho et al., 10 Nov 2024).

The following table summarizes the principal UAV-based node types:

Node Type	Functional Stack	Example Role
UAV-mounted UE	5G NR UE (COTS or custom)	Video/telemetry uplink, real-time control
UAV-mounted MC (B)	RU+DU	Dynamic coverage extension, relay
UAV-mounted MC (A)	RU+DU+CU (gNB)	Fully autonomous, on-demand cell deployment

The MC paradigm decouples network availability from site-fixed infrastructure, enabling rapid instantiation for exceptional operations (air shows, emergencies). Upstream integration for each MC is performed via a Mobile Termination (MT), overlaying PDU sessions on local and/or remote core (NOC) (Coelho et al., 10 Nov 2024).

2. Radio and Link Characteristics with UAV-Based Nodes

Radio planning for UAV-based UEs and MCs requires consideration of altitude, 3D mobility, open-field propagation models, and beamforming strategies. Experimental studies in private 5G airfield environments show:

Carrier Frequency: Sub-6 GHz C-band (3.5–3.8 GHz) is commonly used for coverage and backhaul, with mmWave (e.g., 28 GHz) for local capacity hotspots (Coelho et al., 10 Nov 2024).
Propagation: Open-field runways leverage Free-Space Path Loss (FSPL). For f = 3.7 GHz, λ ≈ 0.081 m, the model is PL_FS(dB)=20 log₁₀(4πd/λ) (Coelho et al., 10 Nov 2024).
Antenna Placement and Gain: Onboard UAV antennas are typically omnidirectional or sectorized, with MC platforms at 10–15 m height for line-of-sight; tower-top sector panels provide complementary coverage (Coelho et al., 10 Nov 2024).

Experimental evaluation under directional jamming demonstrates central trends in physical- and network-layer metrics (Mykytyn et al., 3 Dec 2025):

UAV Speed (m/s)	Avg RSRP (dBm)	Avg SINR (dB)	BLER (%)	Avg Throughput (Mbps)	Outage Time (%)	HO Fail Rate (%)
3	–95 ± 5	5 ± 2	12	22 ± 8	30	10
6	–97 ± 5	4 ± 2	18	18 ± 6	20	5
12	–99 ± 5	3 ± 2	25	15 ± 4	5	0

Shorter dwell times in interference “hot zones” at higher UAV velocities mitigate sustained service outages despite lower average SINR. Key link stability thresholds identified include SINR > 5 dB for stable throughput and a rapid rise in BLER >50% for SINR < 0 dB (Mykytyn et al., 3 Dec 2025).

3. Network Slicing, Resource Management, and Edge Integration

UAV-based UE nodes interact with advanced network slicing frameworks, notably 3GPP NR network slices for mission partitioning (e.g., URLLC for ATC/control, eMBB for data/video, mMTC for sensor telemetry) (Coelho et al., 10 Nov 2024). Distributed resource management employs zone-based UPF/MEC placement, dynamic slice allocation (α-tuning), queue-aware scheduling, and end-to-end latency control (Choudhury et al., 2023):

MEC-IA: Centralized intelligent agent orchestrates optimal UPF/MEC selection for each new UE session, minimizing lane-jitter and tail delays under skewed uRLLC workloads by up to 77.8% versus baseline (Choudhury et al., 2023).
Latency Targets: E2E latency <5 ms for uRLLC UAV control, <10 ms for eMBB traffic, with deterministic jitter <2 ms (Choudhury et al., 2023, Coelho et al., 10 Nov 2024).

Edge computing instances on MCs or at fixed NOC allow localized breakout of critical low-latency traffic, directly supporting UAV-based flight ops, video analytics, and IoT aggregation (Coelho et al., 10 Nov 2024).

4. Mobility, Handover, and Interference Dynamics

UAV-based UEs fundamentally alter mobility and handover patterns compared to terrestrial UEs:

Mobility Management: UAV/MC mobility supported via Xn-based inter-gNB handovers, with dual connectivity (EN-DC) for session continuity (Coelho et al., 10 Nov 2024, Kochems et al., 2018).
Mobility-Jamming Interplay: Experiments confirm that slow UAVs in high-interference zones endure multiple RLFs and failed handovers, while fast motion reduces outage by swiftly transiting jamming footprints—suggesting that mobility-aware handover parameter tuning is mandatory (Mykytyn et al., 3 Dec 2025).
Design Recommendations: Fast handover triggering (reduced hysteresis, TTT), dynamic beamforming/null-steering to suppress known interferers, and SINR-threshold–based handover logic for aerial UEs (Mykytyn et al., 3 Dec 2025).

5. Security and Privacy for UAV-Based Deployments

UAV-based UEs in airfield networks are exposed to enhanced privacy and replay risks due to physical exposure and over-the-air adversarial capabilities (Lutz et al., 1 Sep 2025). Analysis of the 5G-AKA protocol highlights:

Vulnerabilities: Linkability and replay attacks exploiting SUCI reuse, failure cause discrimination, and AUTS resynchronization fields.
Mitigations: UE-generated nonce insertion in SUCI provides robust unlinkability (IND-CCA-secure), adding only +3% CPU, +1 ms authentication latency, and +16 B signaling per SUCI (Lutz et al., 1 Sep 2025). Backward compatibility via optional NAS IE (UE-Nonce) is feasible.
Operationalization: Tight cache synchronization for UE nonces at the HN, drive-testing for high-mobility UEs, and SIEM integration for intrusion detection in the airfield context.

6. Deployment Models, Integration, and Standardization

Realizing UAV-based UE nodes at scale integrates architectural models from the Mobile Cell paradigm (Coelho et al., 10 Nov 2024), nomadic/AMMCOA architectures (Kochems et al., 2018), X5G programmable testbed experiences (Villa et al., 22 Jun 2024), and 5G NPN taxonomy (Ordonez-Lucena et al., 2019):

On-demand, MC-based 5G NPNs: Enable rapid deployment for ad hoc coverage or capacity extension, suitable for UAV missions where coverage flexibility is paramount (Coelho et al., 10 Nov 2024).
Programmable RAN/Edge: GPU-accelerated PHY and O-RAN compliant stacks, with near-RT RIC/xApps for UAV-centric load balancing and fast handover (Villa et al., 22 Jun 2024).
Formal Slice Frameworks: ONAP-based slice lifecycle management for multi-domain, airfield-wide network orchestration—including RAN, core, transport, edge domains—all can include UAV-based UEs and MCs as orchestrated end-points (Rodriguez et al., 2019).

7. Performance Benchmarks and KPIs

Rigorous airfield-specific KPIs for UAV-based UEs/MCs include (Coelho et al., 10 Nov 2024, Mykytyn et al., 3 Dec 2025):

Coverage: ≥99% (MC cluster design)
Throughput: ≥1 Gb/s per MC cell
E2E tail latency: ≤5 ms (URLLC), ≤10 ms (eMBB), jitter <2 ms
HO success rate: ≥99.9%
Security: no linkability or replay channel via nonce-augmented authentication
Service reliability: 99.99–99.999% for critical slices
Availability: 4-9s or better

These benchmarks are directly substantiated by field tests, analytical link budget and capacity formulas, and programmable RAN deployments with COTS and digital UEs (Coelho et al., 10 Nov 2024, Choudhury et al., 2023, Mykytyn et al., 3 Dec 2025).

In summary, UAV-based UE nodes are an enabler for adaptive, resilient, and high-performance private 5G networks in airfield and industrial environments. They leverage modular mobile cell architectures, advanced radio planning, edge-integrated network slicing, and robust mobility/security primitives, validated by experimental and analytical results across recent research (Coelho et al., 10 Nov 2024, Mykytyn et al., 3 Dec 2025, Choudhury et al., 2023, Lutz et al., 1 Sep 2025, Kochems et al., 2018, Ordonez-Lucena et al., 2019, Villa et al., 22 Jun 2024, Rodriguez et al., 2019).