arxiv: 2605.14116 · v1 · submitted 2026-05-13 · 📡 eess.SP

Recognition: no theorem link

UAV Energy Consumption Models for Wireless Systems Research: Model Selection and Misconceptions

Mohammadreza Barzegaran , Hamid Jafarkhani

Authors on Pith no claims yet

Pith reviewed 2026-05-15 01:58 UTC · model grok-4.3

classification 📡 eess.SP

keywords UAV energy consumptionwireless systemsmodel selectionflight phasesrotary-wing UAVfixed-wing UAVtrajectory optimizationpower modeling errors

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The pith

Using the wrong energy consumption model for UAVs produces large errors in wireless systems calculations.

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

The paper reviews energy models for uncrewed aerial vehicles in wireless research and shows that mismatched models cause substantial inaccuracies in power estimates. It organizes UAV types and flight phases, then maps existing models to those categories while flagging frequent misapplications in the literature. The review demonstrates concrete cases where incorrect model choice distorts energy figures used for trajectory planning and deployment. It closes by identifying uncovered scenarios that still lack suitable models.

Core claim

The paper shows that many wireless systems studies apply energy consumption models outside their intended scope for UAV type or flight phase, producing significant numerical errors in calculated power draw. By excluding data-driven and highly complex models, the review supplies clear selection guidelines based on rotary-wing versus fixed-wing aircraft and on phases such as hovering, climbing, and level flight. It documents how these mismatches arise and quantifies the resulting deviations in energy totals.

What carries the argument

UAV energy consumption models tied to specific aircraft categories and flight phases that compute instantaneous power for wireless service tasks.

If this is right

Trajectory optimization routines that rely on energy models will produce more reliable flight paths once the correct model is chosen for the UAV type and phase.
Deployment feasibility studies for UAV-enabled wireless networks will yield tighter bounds on required battery capacity and mission duration.
Researchers can avoid repeated errors by checking model applicability against the UAV category and flight phase before use.
New model development should target the flight phases and UAV types still missing from current literature.

Where Pith is reading between the lines

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

Wireless network simulators could embed automatic model-selection checks to reduce the frequency of scope-mismatch errors.
Real-world UAV battery-life predictions in communication networks may be off by the same margins shown in the reviewed misapplications.
Extending the review to include data-driven models would test whether the selection guidelines remain useful when empirical fitting is allowed.

Load-bearing premise

The reviewed models and documented mistakes accurately reflect the range of practices now used in wireless systems research.

What would settle it

A direct numerical comparison, for one fixed UAV trajectory, of energy totals obtained from a model applied inside its documented scope versus the same trajectory computed with a model taken from outside that scope.

Figures

Figures reproduced from arXiv: 2605.14116 by Hamid Jafarkhani, Mohammadreza Barzegaran.

**Figure 2.** Figure 2: Comparison of typical UAV types considered in [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** Figure 3: Overview of range comparison across representative [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

**Figure 4.** Figure 4: Common mistakes in energy consumption models [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

read the original abstract

Uncrewed aerial vehicles (UAVs) are gaining increasing attention in wireless systems, providing new opportunities to expand the reach and improve the quality of wireless services. Despite their versatility, UAVs are limited by available energy onboard, which results in significant challenges in deploying UAV-enabled wireless systems. Modeling energy consumption is an essential component of the deployment and trajectory optimization of UAVs. This article presents a comprehensive overview of UAV energy consumption models, with a focus on their relevance to wireless systems research. We deliberately exclude data-driven and overly complex models to provide clear and practical guidelines for their use in wireless systems research. We begin by categorizing the most common types of UAVs and describing the typical flight phases considered in the literature. We then review existing energy consumption models, focusing on their scope with respect to UAV types and flight phases. We also discuss common mistakes in the use of these models and highlight the existing gaps in the literature. In particular, we show how the use of a wrong model can lead to significant errors in energy consumption calculations. Finally, we emphasize the need to develop energy consumption models for missing scenarios.

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.

Desk Editor's Note private letter to a colleague

This survey maps common UAV energy models to wireless research needs and shows with numbers how picking the wrong one produces large errors.

read the letter

The core point is that this paper organizes existing UAV energy models by vehicle type and flight phase, then demonstrates through direct comparisons that swapping models can swing energy estimates by tens of percent. That demonstration is the most useful part for anyone running trajectory or placement optimizations in wireless settings. It stays practical by limiting itself to standard analytic models and spelling out why it leaves out data-driven alternatives. The categorization is clear, the scope notes are explicit, and the error examples are worked out numerically rather than left as general warnings. That makes the guidance concrete enough to act on. As a survey it adds no new derivations or measurements, so the advance is mainly in the mapping and the mismatch checks. The argument that the reviewed models cover typical wireless-system usage rests on the cited literature; the paper flags its own gaps, such as certain dynamic maneuvers, so readers know where to look further. No internal contradictions appear in the logic or the examples. This is aimed at researchers who need to choose an energy model for UAV wireless work without overcomplicating the setup. It is solid enough on its own terms to warrant referee time; the examples give referees something specific to evaluate. I would send it for review.

Referee Report

0 major / 3 minor

Summary. The manuscript surveys UAV energy consumption models relevant to wireless systems research. It categorizes UAV types and typical flight phases, reviews existing models by their applicability and scope, identifies common misconceptions in model selection and use, supplies concrete numerical examples demonstrating significant errors in energy calculations from incorrect model choices, and notes gaps for unaddressed scenarios while explicitly justifying the exclusion of data-driven and complex models.

Significance. If the reviewed models and error examples hold, the work offers practical value by distilling guidelines for model selection in UAV-enabled wireless research, where energy constraints directly affect trajectory optimization and deployment. Credit is due to the explicit numerical comparisons that quantify calculation errors and to the bounded scope that keeps the guidance accessible without overclaiming universality.

minor comments (3)

In the model categorization section, the notation for power terms (e.g., induced power, profile power) varies across reviewed models; a consolidated table mapping equivalent terms would improve cross-model comparison.
[Misconceptions and examples] The numerical error examples would benefit from an explicit statement of the assumed UAV mass, speed profile, and flight phase duration to facilitate exact reproduction by readers.
The gaps discussion lists missing scenarios but does not quantify their frequency in recent wireless UAV papers; adding a brief citation count or example application would strengthen the call for future work.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive review, the recognition of the manuscript's practical value in distilling model-selection guidelines, and the recommendation to accept. We appreciate the credit given to the numerical error examples and the bounded scope.

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper is a delimited survey synthesizing existing UAV energy models, categorizing flight phases, reviewing literature models, and illustrating misapplication errors via concrete numerical comparisons drawn from prior works. No new derivations, fitted parameters, or predictions are introduced that reduce to internal definitions or self-citations. Central claims rest on external references and explicit examples rather than any load-bearing self-referential step. This matches the default non-circular outcome for review papers with independent external support.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

As a review paper the work rests on standard domain assumptions about UAV propulsion physics and wireless energy accounting; no new free parameters, ad-hoc axioms, or invented entities are introduced.

axioms (1)

domain assumption Existing propulsion and communication energy models from the literature are sufficiently accurate and representative for wireless systems research when used within their stated scope.
The paper builds its guidelines directly on these models without independent verification or new validation data.

pith-pipeline@v0.9.0 · 5497 in / 1249 out tokens · 45965 ms · 2026-05-15T01:58:14.292830+00:00 · methodology

discussion (0)

Reference graph

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