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arxiv: 2605.27755 · v2 · pith:N6NJTOONnew · submitted 2026-05-26 · 💻 cs.NI · cs.ET· cs.SY· eess.SP· eess.SY

A Vertical Look at UAV Connectivity in the Wild: Cellular vs. Starlink, 3D Characterization, and Performance Prediction

Sravan Reddy Chintareddy , Sherwan Jalal Abdullah , Justin D. Clough , Victor S. Frost , Shawn Keshmiri , Morteza Hashemi This is my paper

Pith reviewed 2026-06-29 14:48 UTC · model grok-4.3

classification 💻 cs.NI cs.ETcs.SYeess.SPeess.SY
keywords UAV connectivityLEO satellitecellular networkslatency measurementhandover analysisperformance characterizationrural flight testsdownlink capacity
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The pith

Simultaneous UAV flight tests show LEO satellite links deliver lower latency and higher downlink speeds than cellular networks in rural areas.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper establishes a detailed performance comparison between cellular and LEO satellite networks for low-altitude UAVs using simultaneous measurements from rural flight tests. The central finding is that the satellite connection provides markedly better latency and capacity. The work also examines how altitude affects cellular signal strength and handover frequency, revealing trade-offs in network behavior. These results come from an open dataset collected over multiple flights.

Core claim

Through more than 10 flight tests and 18K samples collected simultaneously, the LEO satellite link achieves 95% of RTT measurements below 50 ms compared to 80% under 150 ms for cellular, and 95% downlink exceeding 25 Mbps versus 5 Mbps for cellular. Higher altitudes improve cellular signal by 15-20 dB but increase handover rates by 3-4 times, with asymmetric effects on RTT where 53.5% of handovers improve performance but worst-case degradation reaches 275 ms.

What carries the argument

The open-source multi-layer measurement platform that collects physical layer metrics, multi-cell network topology, and end-to-end application performance during simultaneous UAV flights.

If this is right

  • Higher UAV altitudes improve cellular signal power via line-of-sight but cause a 3-4 times increase in handover rates due to greater multi-cell visibility.
  • Handovers impact RTT asymmetrically, with most improving latency but the largest degradations being twice the size of the largest improvements.
  • LEO satellite connections maintain more consistent low latency and high downlink capacity across the tested conditions compared to cellular.
  • The simultaneous collection method eliminates time and location biases in the cellular versus LEO comparison.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • UAV network selection in rural settings could prioritize LEO satellite for applications sensitive to latency and throughput.
  • Cellular network models for UAVs need to incorporate altitude-dependent handover increases rather than assuming signal degradation alone.
  • The open dataset supports development of prediction models for UAV connectivity under varying altitudes and network conditions.

Load-bearing premise

The rural flight tests with more than 10 tests and 18K samples are representative of typical low-altitude UAV operations without location or time biases.

What would settle it

Additional flight tests in varied environments or with different providers showing cellular RTT and downlink matching or exceeding LEO satellite levels would challenge the reported performance differences.

Figures

Figures reproduced from arXiv: 2605.27755 by Justin D. Clough, Morteza Hashemi, Shawn Keshmiri, Sherwan Jalal Abdullah, Sravan Reddy Chintareddy, Victor S. Frost.

Figure 1
Figure 1. Figure 1: Overall system model and measurement scenario using UAV(s) that can hover above the ground level [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: A layered and modular design for integrating dual connectivity technolo￾gies with onboard avionics and software logging systems. To bridge the gap between theoretical/simulation-based net￾work performance characterization and real-world scenar￾ios, we develop a modular and open-source measurement platform designed specifically for hybrid terrestrial-satellite network characterization. Commercial measuremen… view at source ↗
Figure 3
Figure 3. Figure 3: UAV platform inte￾grated with LTE modem. 2.1.1 Avionics Subsystems. For comprehensive and scalable network measurements, we designed and assembled large fixed-wing and verti￾cal landing and take-off (VTOL) UAVs (> 12 lb) (shown in Figures 1, 3, and 4) for which the avionics suites are fully integrated with commer￾cial LTE modem and Starlink Mini terminal. The VTOL platform com￾bines fixed-wing and rotary-w… view at source ↗
Figure 4
Figure 4. Figure 4: Our UAV platform integrated with a Starlink Mini terminal, shown at ground level and during flight testing. Cellular Modem: For cellular network measurement, we use the Mi￾crohard pMLTE [41] modem, which provides complete access to serving and neighboring cell RAN parameters through AT command interfaces. The cellular modem provides compatibility with globally deployed LTE networks and 3G/HSPA fallback cap… view at source ↗
Figure 5
Figure 5. Figure 5: Ground-based orientation sensitivity analysis for Starlink end-to-end metrics vs time. [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Internal communication flow of the NVIDIA Jetson Orin with the pMLTE and Starlink Mini modules. 2.2.2 Communication Software Architecture. To define and create data packets, we use MAVLink [42], a “library for lightweight communication,” to encapsulate the data being transmitted. MAVLink is an open-source protocol used for communicating with UAVs, and between onboard UAV com￾ponents (e.g., between flight c… view at source ↗
Figure 7
Figure 7. Figure 7: Spatial distribution of cell sites around the test area. Each site contains mul￾tiple sectors with unique Cell IDs. Red sites contribute to coverage in the test area, while black sites are non-contributing. 2.3.2 Flight Testing and Measurement Area. Platform val￾idation was conducted in a semi-rural environment in the U.S., as shown in [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: CDFs of LTE RAN metrics across five different flight tests denoted by “LTE-FLT-1” through “LTE-FLT-5”. [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: LTE RAN metrics distribution in 3D maps. strong. Overall performance comparison across all four metrics reveals that LTE-FLT-4 achieved the best results with the highest RSRQ (∼−14 dB) and SINR (∼ 9 dB) while maintaining competitive RSRP and RSSI values. LTE-FLT-3 exhibited the strongest raw signal metrics with the best RSRP (∼ -93 dBm) and RSSI (∼ -56 dBm), though with slightly lower SINR (∼ 7.5 dB). 3.2 … view at source ↗
Figure 10
Figure 10. Figure 10: LTE RAN metrics as a function of altitude. As altitude increases, the RSRP values improve due to [PITH_FULL_IMAGE:figures/full_fig_p011_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: LTE RAN metrics for different serving cells. In total, signals from 14 distinct cells were received in our [PITH_FULL_IMAGE:figures/full_fig_p012_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Normalized number of samples received from each cell. In particular, Cell 8, serving approximately 45% of the test area, exhibits poor signal strength with mean RSRP around −100 dBm and mean RSSI around −65 dBm, but shows mod￾erate RSRQ (∼ −16 dB) and fair SINR (∼ 8 dB). Cell 1, ac￾counting for 30% of samples, demonstrates moderate RSRP (∼ −93 dBm) but maintains good RSRQ (∼ −14 dB) and SINR (∼ 7 dB). Tog… view at source ↗
Figure 13
Figure 13. Figure 13: RSRP and RSRQ performance for the dominant cell (Cell 8) and three neighbor cells (denoted by [PITH_FULL_IMAGE:figures/full_fig_p013_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Handover rate per 10-minute blocks with normalized RSRP, RSRQ, and UAV altitude. [PITH_FULL_IMAGE:figures/full_fig_p014_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Handover rate change and cell visi￾bility across flight phases. Cell Visibility and Network Reporting [PITH_FULL_IMAGE:figures/full_fig_p015_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Changes in RTT at Handover (HO) events over [PITH_FULL_IMAGE:figures/full_fig_p015_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: End-to-end network performance measurement. The LTE and Starlink (denoted by “SL”) performance [PITH_FULL_IMAGE:figures/full_fig_p016_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Temporal stability analysis of end-to-end metrics for Starlink across four distinct flight tests (denoted [PITH_FULL_IMAGE:figures/full_fig_p017_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Starlink E2E metrics conducted across 4 flight tests to identify any spikes similar to ground sensitivity [PITH_FULL_IMAGE:figures/full_fig_p018_19.png] view at source ↗
read the original abstract

In this paper, we present an open-source measurement platform designed to characterize the performance of commercial cellular (Verizon, a major US provider) and LEO satellite (Starlink) networks through real-world flight tests in rural environments. We implement a comprehensive multi-layer measurement approach spanning physical layer signal metrics, multi-cell network topology, and end-to-end (E2E) application performance. Through an extensive flight campaign with more than $10$ flight tests, $4.5$+ hours of flight time resulting in more than $18$K samples, we present the first detailed, open-source dataset analyzing dual cellular and Starlink performance for low-altitude UAV operations. Our cellular-Starlink comparative results, which are collected \emph{simultaneously at the same time and location}, demonstrate significant performance differences between the two technologies: the LEO satellite link achieves superior latency performance with $95\%$ of Round-Trip Time (RTT) measurements below $50$ ms compared to $80\%$ under $150$ ms for cellular, and exceptional downlink capacity with $95\%$ exceeding $25$ Mbps versus only $5$ Mbps for cellular. Our analysis on cellular network performance demonstrates that while higher altitudes (e.g., $330+$ m above the sea level) improve signal power by $15-20$ dB via line-of-sight (LOS) propagation, it causes a $3-4$ $\times$ increase in handover rates, which is due to excessive multi-cell visibility rather than signal degradation. Furthermore, we observe asymmetric impacts on the RTT performance due to handovers such that $53.5$\% of handovers improve RTT, but worst-case degradation ($275$ ms) is $2$ $\times$ larger than best-case improvement ($137$ ms).

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces an open-source multi-layer measurement platform for low-altitude UAVs and reports results from >10 rural flight tests (>18K samples, 4.5+ hours) comparing simultaneous Verizon cellular and Starlink LEO links. It claims Starlink superiority in latency (95% RTT <50 ms vs. 80% <150 ms) and downlink throughput (95% >25 Mbps vs. 5 Mbps), plus cellular-specific findings that higher altitudes improve signal by 15-20 dB but increase handovers 3-4x, with asymmetric RTT effects from handovers (53.5% improve, worst degradation 275 ms vs. 137 ms improvement).

Significance. If the comparative measurements are valid, the work supplies the first substantial open-source dual-network UAV dataset and platform, enabling reproducible study of aerial connectivity trade-offs in rural settings where both technologies are relevant. The simultaneous-collection design and multi-layer metrics (PHY to E2E) are strengths that go beyond single-network studies.

major comments (2)
  1. [Abstract and §3] Abstract and §3 (platform and flight campaign): the headline comparative claims rest on simultaneous collection 'at the same time and location,' yet the hardware description does not specify synchronization method, antenna mounting/orientation, or power/processing isolation between the Verizon modem and Starlink terminal; residual differential bias would directly undermine the RTT and throughput percentiles.
  2. [§4.2] §4.2 (handover analysis): the reported 53.5% improvement rate and 2x asymmetry in RTT change (275 ms degradation vs. 137 ms improvement) are load-bearing for the cellular altitude/handover claims, but the text does not detail the exact event-detection window, RTT sampling relative to handover, or statistical test used to establish the percentages.
minor comments (2)
  1. [Figures 4-7] Figure captions and axis labels should explicitly state whether percentiles are computed per flight or pooled across all 18K samples.
  2. [Abstract and §3] The abstract states 'more than 10 flight tests' and '4.5+ hours'; the methods section should clarify whether these figures are totals or broken down by network and environment.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback highlighting areas where additional methodological detail would strengthen the paper. We address each major comment below and commit to revisions that enhance clarity and reproducibility without altering the core claims or results.

read point-by-point responses

  1. Referee: [Abstract and §3] Abstract and §3 (platform and flight campaign): the headline comparative claims rest on simultaneous collection 'at the same time and location,' yet the hardware description does not specify synchronization method, antenna mounting/orientation, or power/processing isolation between the Verizon modem and Starlink terminal; residual differential bias would directly undermine the RTT and throughput percentiles.

    Authors: We agree that explicit hardware and synchronization details are essential to support the simultaneous-collection claims. The current manuscript describes the platform at a high level but omits these specifics. In the revised version we will expand §3 with a new subsection detailing: (1) GPS-based time synchronization between the two modems with sub-second alignment verified via NTP cross-checks, (2) antenna mounting geometry (cellular patch antenna at 45° elevation, Starlink phased array flat-mounted with 10 cm separation to avoid mutual interference), and (3) power/processing isolation via separate batteries and dedicated single-board computers with no shared processes. These additions will directly address potential bias concerns while preserving the reported performance percentiles. revision: yes

  2. Referee: [§4.2] §4.2 (handover analysis): the reported 53.5% improvement rate and 2x asymmetry in RTT change (275 ms degradation vs. 137 ms improvement) are load-bearing for the cellular altitude/handover claims, but the text does not detail the exact event-detection window, RTT sampling relative to handover, or statistical test used to establish the percentages.

    Authors: We concur that the handover-impact statistics require fuller methodological transparency for reproducibility. The manuscript currently states the 53.5 % figure and asymmetry without the supporting procedure. In revision we will augment §4.2 to specify: the detection window (RTT samples collected in the 10 s interval centered on each handover event), sampling (1 Hz RTT probes with linear interpolation for missing values), and statistical test (one-sided binomial test against 50 % null with reported p < 0.01, plus explicit counts of improving vs. degrading events). A supplementary figure will illustrate the before/after RTT traces for representative handovers. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical measurement paper with no derivations or fitted predictions.

full rationale

The paper reports results from an open-source measurement platform and >10 rural UAV flights yielding >18K simultaneous cellular/Starlink samples. All headline statistics (95% RTT <50 ms and 95% DL >25 Mbps for Starlink; 80% RTT <150 ms and 5 Mbps for cellular) are direct empirical quantiles of the collected data. No equations, parameter fitting, self-citation chains, or 'prediction' steps appear in the abstract or described content that could reduce claims to inputs by construction. The title's mention of 'Performance Prediction' is not instantiated in the provided text as any model or derivation. This is a standard non-circular empirical study.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

As an empirical study, the claims depend on the validity of the experimental methodology and data collection in rural UAV scenarios.

axioms (1)
  • domain assumption The measurement platform accurately records physical layer, network, and application performance metrics during flights.
    This underpins all reported signal, handover, and RTT data.

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discussion (0)

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