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arxiv: 2606.09014 · v1 · pith:G3LRJ7RCnew · submitted 2026-06-08 · 💻 cs.IT · math.IT

Deterministic versus Stochastic Optimization for Joint Path Planning and Dynamic Time Splitting in Multiple-UAV-Cached IoT Networks

Trinh Van Chien , Dinh Thanh Tung , Waqas Khalid , Ngo Cong Dung , Banh Thi Quynh Mai , Symeon Chatzinotas This is my paper

Pith reviewed 2026-06-27 15:03 UTC · model grok-4.3

classification 💻 cs.IT math.IT
keywords UAVIoTdynamic time splittingblock coordinate descentgenetic algorithmthroughput maximizationcachingbackscatter communication
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The pith

Joint optimization of dynamic time splitting, UAV trajectories, and power yields at least 31% throughput improvement in multi-UAV IoT networks.

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

This paper develops methods to maximize data throughput in wireless-powered IoT networks that use multiple UAVs with caching and backscatter capabilities. The source sends both information and power to the UAVs, which harvest energy and use it for passive or active communication to the destination. To solve the non-convex joint optimization of time splitting ratios, flight paths, and transmit powers, the authors propose an alternating block coordinate descent algorithm that derives a closed-form solution for the time splitting parameter. They also present a genetic algorithm approach. Simulations show both methods deliver at least 31 percent higher throughput than standard benchmarks while requiring less computation time.

Core claim

The paper claims that an efficient alternating algorithm based on block coordinate descent, using KKT conditions to obtain a closed-form expression for the optimal dynamic time splitting ratio, combined with a genetic algorithm using one-point crossover and rank-based selection, allows effective maximization of total throughput by jointly optimizing the DTS ratio, UAV trajectory, and transmission power in caching-enabled UAV-aided IoT networks, achieving at least 31% improvement over benchmarks.

What carries the argument

Block coordinate descent (BCD) alternating optimization with KKT-derived closed-form dynamic time splitting (DTS) ratio, alongside a genetic algorithm (GA) for stochastic search.

Load-bearing premise

The non-convex joint optimization problem can be decomposed and solved alternately via BCD with closed-form sub-solutions without substantial loss in optimality.

What would settle it

Comparing the throughput achieved by the proposed BCD and GA methods against a globally optimal solution obtained via exhaustive search or convex relaxation in a small-scale scenario would reveal if the 31% gain holds or if optimality gap is large.

Figures

Figures reproduced from arXiv: 2606.09014 by Banh Thi Quynh Mai, Dinh Thanh Tung, Ngo Cong Dung, Symeon Chatzinotas, Trinh Van Chien, Waqas Khalid.

Figure 1
Figure 1. Figure 1: The considered system model with multiple-UAV trajectory of UAVs. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The schematic time-slot diagram. provide flexibility for adjustments as the user at D changes location, T is defined as the total duration for a UBD to travel from its initial to final location and serve the destination. The time window T is divided into N slots, each lasting δt = T /N [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The optimized trajectory of the two UAVs. [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Throughput comparison of scenarios with different parameter settings: (a) The total throughput versus the demanded data. (b) The total throughput [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Performance comparison between algorithms: (a) The total throughput versus the traveling time [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The total throughput versus the cache ratio [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
read the original abstract

This paper examines wireless-powered Internet of Things (IoT) networks involving multiple unmanned aerial vehicles (UAVs) equipped with backscatter and caching technologies to relay and transmit signals. For data communication and energy harvesting (EH), the source transmits information and power to UAVs using the dynamic time splitting (DTS) method. UAVs use harvested energy for passive communication (backscatter) and for active communication (transmitting information) to the destination. The primary objective is to maximize the total throughput by jointly optimizing the DTS ratio, trajectory, and transmission power, leveraging the UAVs' caching capability. This optimization problem is challenging due to its non-convexity. Therefore, an efficient alternating algorithm using the block coordinate descent (BCD) method is proposed to optimize each variable given the fixed values of the other parameters. By applying the Karush-Kuhn-Tucker (KKT) conditions, we derive a closed-form expression for the optimal DTS ratio, significantly reducing computation time. The optimal values for the other two parameters are determined using the BCD. In order to thoroughly assess the effectiveness of various solutions for the original problem, this paper introduces an approach leveraging a genetic algorithm (GA). The GA in this context employs a one-point crossover method, value mutation, and rank-based selection based on fitness values. Numerical results show that the BCD and GA achieve at least 31% throughput improvement over the benchmarks, with reduced computational time. These findings demonstrate the performance gain and practical feasibility of our solutions in caching-enabled UAV-aided IoT networks.

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

3 major / 1 minor

Summary. The paper claims that in wireless-powered multi-UAV IoT networks with backscatter and caching, jointly optimizing the dynamic time splitting (DTS) ratio, UAV trajectories, and transmit powers via block coordinate descent (BCD) with a KKT-derived closed-form DTS solution (plus a genetic algorithm baseline) yields at least 31% throughput improvement over benchmarks while reducing computation time.

Significance. If the reported gains prove robust under stronger benchmarks and the BCD solutions are shown to be at least stationary points with verifiable convergence, the work would offer a practical alternating-optimization template for non-convex resource allocation in UAV-assisted energy-harvesting networks; the explicit KKT closed-form for the DTS subproblem is a concrete algorithmic contribution that could reduce online computation.

major comments (3)
  1. [Abstract] Abstract: the headline claim of 'at least 31% throughput improvement' is load-bearing for the paper's contribution, yet the abstract supplies neither the definitions of the benchmark schemes, the number of Monte-Carlo trials, error bars, nor the precise simulation parameters (number of UAVs, EH efficiency, caching sizes) needed to reproduce or statistically assess the margin.
  2. [Abstract] Abstract (method description): while fixing other blocks may admit a KKT closed-form for the DTS ratio, the joint problem remains non-convex in trajectory (distance-dependent rates) and power; BCD is stated to be 'efficient' but no convergence analysis, stationarity guarantee, or verification that the trajectory subproblem satisfies the conditions for BCD convergence (continuous differentiability, compactness) is provided, so the 31% figure may reflect local-search quality rather than method superiority.
  3. [Abstract] Abstract: the GA is introduced 'to thoroughly assess' the BCD solutions, yet no details are given on population size, number of generations, mutation rate, or comparison against a global solver or tighter relaxation; without these, it is unclear whether GA serves as an independent validation or merely another heuristic.
minor comments (1)
  1. [Abstract] Abstract: the phrase 'reduced computational time' is asserted without any quantitative comparison (e.g., CPU seconds or iteration counts) or reference to a table/figure that would allow readers to judge the claimed efficiency gain.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on the abstract. We address each major comment below and will incorporate revisions to improve clarity and reproducibility while preserving the paper's contributions.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the headline claim of 'at least 31% throughput improvement' is load-bearing for the paper's contribution, yet the abstract supplies neither the definitions of the benchmark schemes, the number of Monte-Carlo trials, error bars, nor the precise simulation parameters (number of UAVs, EH efficiency, caching sizes) needed to reproduce or statistically assess the margin.

    Authors: We agree that the abstract would benefit from additional context to support the reported gains. In the revised manuscript, we will expand the abstract with concise definitions of the benchmark schemes (e.g., equal time splitting and fixed trajectories), a reference to the simulation section for the number of Monte-Carlo trials and error bars shown in figures, and key parameters such as the number of UAVs and EH efficiency. This addresses reproducibility without exceeding abstract length limits. revision: yes

  2. Referee: [Abstract] Abstract (method description): while fixing other blocks may admit a KKT closed-form for the DTS ratio, the joint problem remains non-convex in trajectory (distance-dependent rates) and power; BCD is stated to be 'efficient' but no convergence analysis, stationarity guarantee, or verification that the trajectory subproblem satisfies the conditions for BCD convergence (continuous differentiability, compactness) is provided, so the 31% figure may reflect local-search quality rather than method superiority.

    Authors: The observation on non-convexity is accurate. We will add a discussion in the revised paper on the BCD convergence behavior, supported by numerical evidence of rapid stabilization, and clarify that solutions are locally optimal rather than globally guaranteed. A full stationarity proof for the trajectory subproblem is not feasible without altering the approach, but we will note the practical convergence and conditions where BCD applies. revision: partial

  3. Referee: [Abstract] Abstract: the GA is introduced 'to thoroughly assess' the BCD solutions, yet no details are given on population size, number of generations, mutation rate, or comparison against a global solver or tighter relaxation; without these, it is unclear whether GA serves as an independent validation or merely another heuristic.

    Authors: The GA parameters (population size, generations, mutation rate) are specified in the numerical results section. We will revise the abstract to briefly reference these settings and the GA's role as a heuristic benchmark for comparison, directing readers to the full implementation details in the manuscript. This will clarify its purpose without claiming global optimality. revision: yes

Circularity Check

0 steps flagged

No circularity: standard alternating optimization with KKT closed-form subproblem

full rationale

The derivation uses block coordinate descent to alternate between DTS ratio (closed-form via KKT), trajectory, and power. These are standard convexification and stationarity techniques applied to a non-convex joint problem; the closed-form DTS is derived from the Lagrangian of the subproblem given fixed other blocks, not defined in terms of the final throughput value. GA is presented as an independent heuristic benchmark. No self-citation chain, no fitted parameter renamed as prediction, and no ansatz smuggled via prior work. Numerical 31% gain is an empirical simulation outcome, not a tautological reduction to inputs. The approach is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review yields limited visibility into parameters or assumptions; the non-convexity statement and standard optimization techniques are treated as domain assumptions rather than new inventions.

axioms (1)
  • domain assumption The joint optimization problem is non-convex.
    Stated directly in the abstract as the reason for using alternating BCD.

pith-pipeline@v0.9.1-grok · 5841 in / 1128 out tokens · 21451 ms · 2026-06-27T15:03:07.635443+00:00 · methodology

discussion (0)

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