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arxiv: 1907.10242 · v1 · pith:DXLIAVKSnew · submitted 2019-07-24 · 💻 cs.IT · eess.SP· math.IT

Receding Horizon Optimization for Energy-Efficient UAV Communication

Jingwei Zhang , Yong Zeng , Rui Zhang This is my paper

Pith reviewed 2026-05-24 16:55 UTC · model grok-4.3

classification 💻 cs.IT eess.SPmath.IT
keywords UAV communicationenergy efficiencyreceding horizon optimizationtrajectory optimizationpropulsion energywireless data collection
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The pith

Receding horizon optimization maximizes a UAV's energy efficiency by solving successive finite time windows rather than the full trajectory.

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

The paper studies a fixed-wing UAV collecting data from ground nodes and aims to maximize energy efficiency, defined as total achievable rate divided by propulsion energy used. To handle the continuous-time nature of the problem, it introduces receding horizon optimization that repeatedly solves the maximization over a moving window of finite length, which greatly reduces the number of variables at each step compared with discretizing the entire flight duration. A reader would care because full-trajectory optimization quickly becomes intractable for realistic mission lengths, while the windowed approach keeps computation manageable. If the successive local solutions remain close to the global optimum, the method enables practical energy-efficient flight planning without sacrificing much performance.

Core claim

We propose a receding horizon optimization method to solve the energy efficiency maximization problem for a UAV serving ground nodes, where the problem is sequentially solved over moving finite-duration time windows to reduce computational complexity relative to conventional time discretization.

What carries the argument

Receding horizon optimization (RHO), which sequentially optimizes energy efficiency over successive finite moving time windows.

If this is right

  • Each optimization subproblem contains far fewer variables than a single full-horizon discretization.
  • The method remains applicable to continuous-time trajectory functions without requiring a uniform fine grid over the entire mission.
  • Simulation results confirm that the approach maintains high energy efficiency while lowering computation time.
  • The same windowed strategy can be applied to other UAV communication objectives that trade rate against propulsion energy.

Where Pith is reading between the lines

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

  • The window length becomes a tunable parameter that trades off optimality gap against per-step solve time, suggesting an adaptive choice based on UAV speed or node density.
  • Because each window re-optimizes from the current state, the framework naturally supports online replanning when ground-node traffic changes.
  • The reduction in per-window variables scales with mission length, implying larger gains for long-duration data-collection flights than for short ones.

Load-bearing premise

Sequentially solving the energy efficiency maximization over successive finite moving time windows yields solutions close enough to the global optimum without unacceptable performance loss.

What would settle it

A numerical comparison in which the energy efficiency achieved by the receding-horizon solutions falls more than a few percent below the optimum obtained by a full-trajectory benchmark on identical scenarios would falsify the claim of effectiveness.

Figures

Figures reproduced from arXiv: 1907.10242 by Jingwei Zhang, Rui Zhang, Yong Zeng.

Figure 1
Figure 1. Figure 1: An illustration of the RHO-based method at the [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 5
Figure 5. Figure 5: Computation time comparison. VI. CONCLUSION This letter proposes an RHO-based method for the joint optimization problem of UAV trajectory and communication resource allocation. By discretizing the problem with different accuracies, the number of variables for each time-window optimization is significantly reduced, thus leading to an overall lower computation complexity compared to the conventional method w… view at source ↗
Figure 2
Figure 2. Figure 2: Trajectories obtained by the conventional time-discretization method. 0 500 1000 1500 2000 2500 3000 X (m) 0 500 1000 1500 2000 2500 3000 Y (m) [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
read the original abstract

In this letter, we study a wireless communication system with a fixed-wing unmanned aerial vehicle (UAV) employed to collect information from a group of ground nodes (GNs). Our objective is to maximize the UAV's energy efficiency (EE), which is defined as the achievable rate among all GNs per unit propulsion energy consumption of the UAV. To efficiently solve this problem with continuous-time functions, we propose a new method based on receding horizon optimization (RHO), which significantly reduces the computational complexity compared to the conventional time discretization method. Specifically, we sequentially solve the EE maximization problem over a moving time-window of finite duration, for each of which the number of optimization variables is greatly reduced. Simulation results are provided to show the effectiveness of the proposed method.

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 / 0 minor

Summary. The manuscript studies energy efficiency maximization for a fixed-wing UAV collecting data from ground nodes. It proposes a receding horizon optimization (RHO) approach that sequentially solves the problem over successive finite-duration moving time windows, claiming this greatly reduces the number of optimization variables relative to conventional time discretization while simulation results demonstrate effectiveness.

Significance. If the empirical results hold, the work offers a practical, lower-complexity alternative for real-time UAV trajectory optimization in energy-efficient communication scenarios, which is relevant for deployment constraints in UAV networks.

major comments (2)
  1. [RHO method description and simulation results] The central claim rests on the assumption that sequential finite-window solutions remain close to the global optimum without unacceptable performance loss, yet no theoretical bounds, approximation guarantees, or analysis of the performance gap introduced by the moving-window truncation are provided (see the RHO method description and the simulation claims).
  2. [Abstract] The abstract states that simulations demonstrate effectiveness versus time discretization, but provides no details on simulation setup, baselines, quantitative gains, or parameter values, leaving the empirical support for the complexity-performance tradeoff thin and difficult to assess.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed review and constructive comments. We address each major point below, clarifying the scope of our contributions and indicating where revisions will be made.

read point-by-point responses

  1. Referee: The central claim rests on the assumption that sequential finite-window solutions remain close to the global optimum without unacceptable performance loss, yet no theoretical bounds, approximation guarantees, or analysis of the performance gap introduced by the moving-window truncation are provided (see the RHO method description and the simulation claims).

    Authors: We agree that the manuscript provides no theoretical bounds or approximation guarantees on the optimality gap. The RHO method is presented as a practical heuristic inspired by receding-horizon control, trading global optimality for reduced complexity; its effectiveness is demonstrated empirically rather than proven analytically. In the revision we will add an explicit paragraph in Section III acknowledging this limitation and noting that the approach is validated only through the simulations in Section IV, which show the achieved EE remains within a small percentage of the full-horizon benchmark while the number of variables drops substantially. revision: partial

  2. Referee: The abstract states that simulations demonstrate effectiveness versus time discretization, but provides no details on simulation setup, baselines, quantitative gains, or parameter values, leaving the empirical support for the complexity-performance tradeoff thin and difficult to assess.

    Authors: Abstracts are length-limited and conventionally omit numerical details. All requested information—simulation parameters, the full time-discretization baseline, quantitative complexity reduction (e.g., variable count), and EE gains—is contained in Section IV and the associated figures. We will not expand the abstract itself, as doing so would violate typical letter-format constraints, but we can ensure the simulation section is even more explicit if the referee desires. revision: no

Circularity Check

0 steps flagged

No significant circularity; derivation is self-contained

full rationale

The paper proposes receding horizon optimization (RHO) as an algorithmic method to solve the EE maximization problem by sequentially optimizing over successive finite moving time windows, reducing the number of variables per window relative to full time discretization. The abstract and description present this as an independent computational technique whose effectiveness is checked via simulation; no equations, parameters, or claims reduce a 'prediction' to a fitted input by construction, invoke self-citations as load-bearing uniqueness theorems, or smuggle ansatzes. The central claim is an empirical complexity-performance tradeoff rather than a closed mathematical derivation that collapses to its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, invented entities, or paper-specific axioms; relies on standard continuous-time optimization assumptions.

axioms (1)
  • standard math Standard assumptions of continuous-time optimization problems being solvable over finite horizons
    Invoked when stating that the EE maximization problem is solved over moving time-windows of finite duration.

pith-pipeline@v0.9.0 · 5653 in / 1089 out tokens · 22640 ms · 2026-05-24T16:55:33.440733+00:00 · methodology

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Reference graph

Works this paper leans on

17 extracted references · 17 canonical work pages · 2 internal anchors

  1. [1]

    Wireless communications with unmanned aerial vehicles: Opportunities and challenges,

    Y . Zeng, R. Zhang, and T. J. Lim, “Wireless communications with unmanned aerial vehicles: Opportunities and challenges,” IEEE Commun. Mag. , vol. 54, no. 5, pp. 36–42, May 2016

    work page 2016

  2. [2]

    Accessing From The Sky: A Tutorial on UAV Communications for 5G and Beyond

    Y . Zeng, Q. Wu, and R. Zhang, “Accessing from the sky: A tutorial on UA V communications for 5G and beyond,” Online available at preprint arXiv:1903.05289, 2019

    work page internal anchor Pith review Pith/arXiv arXiv 1903

  3. [3]

    Optimal LAP altitude for maximum coverage,

    A. Al-Hourani, S. Kandeepan, and S. Lardner, “Optimal LAP altitude for maximum coverage,” IEEE Wireless Commun. Lett. , vol. 3, no. 6, pp. 569–572, Dec. 2014

    work page 2014

  4. [4]

    Throughput maximization for UA V-enabled mobile relaying systems,

    Y . Zeng, R. Zhang, and T. J. Lim, “Throughput maximization for UA V-enabled mobile relaying systems,”IEEE Trans. Commun., vol. 64, no. 12, pp. 4983–4996, Dec. 2016

    work page 2016

  5. [5]

    Mobile edge computing via a UA V- mounted cloudlet: Optimization of bit allocation and path planning,

    S. Jeong, O. Simeone, and J. Kang, “Mobile edge computing via a UA V- mounted cloudlet: Optimization of bit allocation and path planning,” IEEE Trans. V eh. Technol., vol. 67, no. 3, pp. 2049–2063, Mar. 2018

    work page 2049

  6. [6]

    Joint trajectory and communication design for multi-UA V enabled wireless networks,

    Q. Wu, Y . Zeng, and R. Zhang, “Joint trajectory and communication design for multi-UA V enabled wireless networks,” IEEE Trans. Wireless Commun., vol. 17, no. 3, pp. 2109–2121, Mar. 2018

    work page 2018

  7. [7]

    Energy-efficient UA V communication with trajectory optimization,

    Y . Zeng and R. Zhang, “Energy-efficient UA V communication with trajectory optimization,” IEEE Trans. Wireless Commun. , vol. 16, no. 6, pp. 3747–3760, Jun. 2017

    work page 2017

  8. [8]

    Energy minimization for wireless communication with rotary-wing UA V,

    Y . Zeng, J. Xu, and R. Zhang, “Energy minimization for wireless communication with rotary-wing UA V,”IEEE Trans. Wireless Commun., vol. 18, no. 4, pp. 2329–2345, Apr. 2019

    work page 2019

  9. [9]

    UA V-enabled radio access network: Multi-mode communication and trajectory design,

    J. Zhang, Y . Zeng, and R. Zhang, “UA V-enabled radio access network: Multi-mode communication and trajectory design,” IEEE Trans. Signal Process., vol. 66, no. 20, pp. 5269–5284, Oct. 2018. 6

    work page 2018

  10. [10]

    3D trajectory optimization in rician fading for UA V-enabled data harvesting,

    C. You and R. Zhang, “3D trajectory optimization in rician fading for UA V-enabled data harvesting,”IEEE Trans. Wireless Commun. , vol. 18, no. 6, pp. 3192–3207, Jun. 2019

    work page 2019

  11. [11]

    Optimal 3D-trajectory design and resource allocation for solar-powered UA V communication systems,

    Y . Sun, D. Xu, D. W. K. Ng, L. Dai, and R. Schober, “Optimal 3D-trajectory design and resource allocation for solar-powered UA V communication systems,” IEEE Trans. Commun. , vol. 67, no. 6, pp. 4281–4298, Jun. 2019

    work page 2019

  12. [12]

    Multi-UAV Interference Coordination via Joint Trajectory and Power Control

    C. Shen, T. H. Chang, J. Gong, Y . Zeng, and R. Zhang, “Multi-UA V interference coordination via joint trajectory and power control,” Online available at arXiv:1809.05697 , 2018

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  13. [13]

    MILP optimal path planning for real-time applications,

    C. S. Ma and R. H. Miller, “MILP optimal path planning for real-time applications,” in American Control Conference , Jun. 2006

    work page 2006

  14. [14]

    Receding horizon control of autonomous aerial vehicles,

    J. Bellingham, A. Richards, and J. P. How, “Receding horizon control of autonomous aerial vehicles,” in American Control Conference, vol. 5, pp. 3741–3746, 2002

    work page 2002

  15. [15]

    UA Vs trajectory planning by distributed MPC under radio communication path loss constraints,

    A. Grancharova, E. I. Grøtli, D. T. Ho, and T. A. Johansen, “UA Vs trajectory planning by distributed MPC under radio communication path loss constraints,” Journal of Intelligent & Robotic Systems , vol. 79, no. 1, pp. 115–134, 2015

    work page 2015

  16. [16]

    E. F. Camacho and C. B. Alba, Model predictive control . Springer Science & Business Media, 2013

    work page 2013

  17. [17]

    Trajectory design for completion time minimization in UA V-enabled multicasting,

    Y . Zeng, X. Xu, and R. Zhang, “Trajectory design for completion time minimization in UA V-enabled multicasting,” IEEE Trans. Wireless Commun., vol. 17, no. 4, pp. 2233–2246, Apr. 2018

    work page 2018