arxiv: 2604.23132 · v1 · submitted 2026-04-25 · 💻 cs.CE

Recognition: unknown

UAV Trajectory and Bandwidth Allocation for Efficient Data Collection in Low-Altitude Intelligent IoT: A Hierarchical DRL Approach

Zhenjia Xu , Xiaoling Zhang , Nan Qi , Xiaojie Li , Luliang Jia

Authors on Pith no claims yet

Pith reviewed 2026-05-08 06:57 UTC · model grok-4.3

classification 💻 cs.CE

keywords UAVIoT data collectionHierarchical DRLTrajectory optimizationBandwidth allocationISACDeep reinforcement learning

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The pith

A hierarchical deep reinforcement learning method lets UAVs maximize IoT data collection by planning trajectories at coarse time scales and bandwidth at fine scales.

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

The paper designs a hierarchical deep reinforcement learning framework to guide unmanned aerial vehicles in collecting data from ground-based IoT sensors. It separates the problem into an upper layer that plans broad flight trajectories over longer time steps and a lower layer that allocates wireless bandwidth in finer steps. This split lets the system handle unknown interference from jammers, changing data amounts at each sensor, and physical obstacles while running with less computing power on the UAV itself. Simulations show the approach reaches good performance faster and uses fewer resources than standard single-level reinforcement learning methods.

Core claim

The central discovery is that structuring the reinforcement learning policy hierarchically, with trajectory decisions at coarse temporal granularity and bandwidth decisions at fine granularity using the TBH-DDPG algorithm, allows maximization of collected data volume under interference, dynamic data volumes, and obstacles, while achieving faster convergence and lower computational cost.

What carries the argument

The hierarchical deep deterministic policy gradients (TBH-DDPG) algorithm, where the upper level decides UAV trajectory and the lower level decides bandwidth allocation at different time scales.

Load-bearing premise

The simulations with added jammers, varying data volumes, and obstacles fully represent real UAV flight and communication conditions, and the trained policies can execute on UAVs with limited onboard processing power.

What would settle it

Deploying the trained hierarchical policy on a physical UAV in an outdoor environment with actual jammers and obstacles and measuring whether data collection volume, convergence behavior, and compute usage match the simulation results.

Figures

Figures reproduced from arXiv: 2604.23132 by Luliang Jia, Nan Qi, Xiaojie Li, Xiaoling Zhang, Zhenjia Xu.

**Figure 1.** Figure 1: Data collection for the food processing industry in low-altitude IoT view at source ↗

**Figure 2.** Figure 2: Time slot division. communication time slots. The m-th communication time slot within the n-th flight period is represented by δn,m, where m ∈ 1, 2, . . . , M . During any communication time slot, the UAV employs a frequency division multiple access (FDMA) scheme for communication. The slot division for the entire mission is shown in view at source ↗

**Figure 4.** Figure 4: Scenario map of the system after abstraction. view at source ↗

**Figure 5.** Figure 5: Five layered maps. Finally, the output of the network is flattened and combined with the UAV’s remaining battery information to form the input state for the algorithm. B. SMDP model In DRL, the MDP model is commonly used to simplify the scenario. It assumes that state transitions in the environment depend only on the previous state and is primarily composed of the components ⟨S, A,Pr, R⟩. where S represent… view at source ↗

**Figure 6.** Figure 6: TBH-DDPG algorithm framework diagram. where rf (δn) represents the sum of the collision penalty, the return penalty, and the crash penalty. That is rf (δn) = rcollision(δn) + rreturn(δn) + rnland(δn). (15) The collision penalty is applied when the UAV enters a no-fly zone, assigning a fixed penalty value. The specific expression is as follows. rcollision(δn) = ( rcsn , if pu(δn) in red zone 0 , otherwise ,… view at source ↗

**Figure 7.** Figure 7: Reward training curves. allocation actions. The upper-level rewards include the lowerlevel rewards, but optimize only the flight options. In comparison, the non-hierarchical algorithm considers all rewards and simultaneously optimizes both flight and bandwidth allocation actions. Therefore, the proposed algorithm effectively reduces convergence time compared to the non-hierarchical approach. Moreover, th… view at source ↗

**Figure 8.** Figure 8: The first column illustrates the trajectories of different algorithms after convergence, the second column shows the cumulative data collected by the UAV view at source ↗

**Figure 9.** Figure 9: Impact of data growth per communication slot on data loss. view at source ↗

**Figure 10.** Figure 10: Average number of collisions for different algorithms in different view at source ↗

**Figure 11.** Figure 11: UAV trajectories of TBH-DDPG algorithm in different scenarios. view at source ↗

read the original abstract

Under the 6G wireless network evolution, the low-altitude Internet of Things (IoT), supported by unmanned aerial vehicles (UAVs) with Integrated Sensing and Communication (ISAC) capabilities, provides ground sensing networks with advanced real-time monitoring and data collection. To maximize data collection volume from distributed IoT nodes, AI-powered data collection technology plays a critical role in enabling intelligent decision-making. Among them, deep reinforcement learning (DRL) has gained particular attention. However, the existing DRL-based work on UAV-assisted IoT nodes data collection rarely address problems such as unknown interference and dynamic data volume. Moreover, these DRL models have high arithmetic requirements and slow convergence speed, making it difficult to carry on UAVs with limited load and arithmetic power. To address these challenges, a hierarchical deep reinforcement learning (HDRL), which can converge quickly and with smaller models, is designed to optimize UAV trajectories and bandwidth allocation to maximize data collection volume. Firstly, the proposed scenario incorporates interference from jammers, dynamic data volume of IoT nodes, and multiple types of obstacles. The entire task is hierarchically structured: the upper-level makes flight trajectory decisions at a coarse temporal granularity, while the lower-level makes bandwidth allocation decisions at a finer temporal granularity. Secondly, a trajectory and bandwidth allocation optimization algorithm based on hierarchical deep deterministic policy gradients (TBH-DDPG) is proposed to solve the problem. Finally, simulation results demonstrate that the proposed algorithm improves convergence speed by 44.44%, and reduces computational cost by 58.05%, compared to non-hierarchical algorithm.

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

The paper applies hierarchical DDPG to split coarse UAV trajectories from fine-grained bandwidth allocation in an IoT data collection setup with jammers and dynamic loads, but the reported gains rest on thin simulation evidence.

read the letter

The main move here is decomposing the UAV decision problem into two levels: an upper policy that picks trajectories on a slower timescale and a lower policy that adjusts bandwidth more often. This targets the combined effects of jammer interference, changing IoT data volumes, obstacles, and ISAC channels, with the explicit goal of fitting the controller onto UAVs that have limited compute and power. The formulation itself is straightforward and matches the practical constraints the authors describe. Breaking the action space this way is a reasonable way to reduce the size of the models and speed up training, which directly addresses the slow convergence and high arithmetic load they flag in prior flat DRL work. If the full paper shows clean pseudocode and consistent reward shaping across levels, that part of the contribution is usable as a template for similar resource-allocation tasks. The simulation numbers are the part that needs scrutiny. The abstract states clear percentage improvements over a non-hierarchical DDPG baseline, yet gives no definition of what counts as convergence, how computational cost is measured, the number of random seeds, or the exact parameter settings for jammers and data dynamics. Without those details it is difficult to judge whether the gains would survive a change in environment scale or a more realistic channel model. The assumption that the simulated flight dynamics and sensing accuracy translate to onboard hardware also remains untested in the summary. This work is aimed at people already working on DRL for wireless UAV systems who want an example of hierarchical decomposition applied to trajectory-plus-bandwidth control. It is not going to reset the field, but the problem setup is timely for 6G low-altitude IoT. I would bring it to a reading group for the hierarchical framing and the concrete scenario, then ask the authors for the missing experimental controls. It deserves peer review because the core idea is coherent and the application is relevant, even though the current evidence for the performance claims is incomplete and will need strengthening.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes a hierarchical deep reinforcement learning (HDRL) framework using a trajectory and bandwidth allocation optimization algorithm based on hierarchical deep deterministic policy gradients (TBH-DDPG). The approach addresses UAV-assisted data collection in low-altitude IoT networks with ISAC capabilities, incorporating jammers, dynamic IoT data volumes, and obstacles. The task is decomposed hierarchically: upper level handles coarse-grained flight trajectory decisions, lower level handles fine-grained bandwidth allocation. Simulation results are presented claiming that TBH-DDPG achieves 44.44% faster convergence and 58.05% lower computational cost relative to a non-hierarchical DDPG baseline.

Significance. If the performance gains can be reproduced with fully specified simulation parameters, statistical validation, and hardware-aware metrics, the hierarchical decomposition could provide a useful template for making DRL policies deployable on compute- and energy-constrained UAV platforms. The work directly targets practical barriers (slow convergence, high arithmetic demand) that currently hinder on-board DRL for UAV-IoT applications. However, the absence of detailed experimental protocols and external benchmarks currently limits the strength of this contribution.

major comments (2)

The central performance claims (44.44% convergence improvement and 58.05% computational-cost reduction) are stated in the abstract and presumably elaborated in the simulation results section, yet no definition is given for the metrics themselves (e.g., episodes until 95% of maximum reward, FLOPs, wall-clock time per decision, or parameter count). No simulation parameters (jammer power, data-volume arrival process, obstacle density, ISAC channel model, UAV dynamics), baseline implementation details, number of random seeds, or variance statistics are provided. Without these, the numerical gains cannot be independently verified and the claim that the hierarchical policy is suitable for limited-load UAVs remains unsupported.
The system model (presumably §2 or §3) includes jammers, dynamic data volumes, and multiple obstacle types, but the manuscript does not analyze or simulate the effect of imperfect state observation, sensing errors, or onboard inference latency on the hierarchical policy. The reported gains therefore rest on an idealized simulation environment whose fidelity to real UAV flight dynamics, battery constraints, and wireless conditions is not demonstrated.

minor comments (2)

Notation for the hierarchical levels (upper/lower) and the precise interface between trajectory and bandwidth actions should be clarified with a diagram or pseudocode in the algorithm description.
The abstract would benefit from a one-sentence statement of the key technical novelty (hierarchical temporal decomposition) before the numerical results.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thorough and constructive review. The comments highlight important aspects for strengthening the verifiability and practical relevance of our results. We address each major comment below and outline the revisions we will make to the manuscript.

read point-by-point responses

Referee: The central performance claims (44.44% convergence improvement and 58.05% computational-cost reduction) are stated in the abstract and presumably elaborated in the simulation results section, yet no definition is given for the metrics themselves (e.g., episodes until 95% of maximum reward, FLOPs, wall-clock time per decision, or parameter count). No simulation parameters (jammer power, data-volume arrival process, obstacle density, ISAC channel model, UAV dynamics), baseline implementation details, number of random seeds, or variance statistics are provided. Without these, the numerical gains cannot be independently verified and the claim that the hierarchical policy is suitable for limited-load UAVs remains unsupported.

Authors: We agree that the manuscript does not provide explicit definitions of the convergence and computational-cost metrics nor a complete enumeration of simulation parameters, baseline implementation details, or statistical reporting. In the revised manuscript we will insert a new subsection (Simulation Setup and Metrics) that (i) defines convergence as the number of training episodes required to reach 95 % of the maximum attainable reward and computational cost as the average number of FLOPs per decision step, (ii) lists all numerical values for jammer transmit power, data-volume arrival process, obstacle density, ISAC channel model, UAV kinematic constraints, and battery model, (iii) supplies pseudocode and hyper-parameter tables for both TBH-DDPG and the flat DDPG baseline, and (iv) reports all performance figures as means over 10 independent random seeds together with standard deviations. These additions will enable independent reproduction and will directly support the claim of suitability for resource-constrained UAV platforms. revision: yes
Referee: The system model (presumably §2 or §3) includes jammers, dynamic data volumes, and multiple obstacle types, but the manuscript does not analyze or simulate the effect of imperfect state observation, sensing errors, or onboard inference latency on the hierarchical policy. The reported gains therefore rest on an idealized simulation environment whose fidelity to real UAV flight dynamics, battery constraints, and wireless conditions is not demonstrated.

Authors: The referee is correct that our current simulations assume perfect state observation and do not incorporate sensing errors or onboard inference latency. The contribution of the work is to show that hierarchical decomposition yields faster convergence and lower per-decision compute under the modeled environment that already includes jammers, dynamic data volumes, and obstacles. Extending the evaluation to imperfect observations would require additional stochastic models of sensing error and latency measurements that lie outside the scope of the present study. In the revision we will add a dedicated paragraph in the Conclusions section that explicitly states these modeling assumptions as limitations and identifies imperfect state information and hardware-in-the-loop latency as important directions for future work. We do not intend to perform new simulations of sensing errors in this revision. revision: partial

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper proposes a hierarchical DRL algorithm (TBH-DDPG) for UAV trajectory and bandwidth allocation under interference and dynamic conditions, with performance claims based solely on simulation comparisons to a non-hierarchical DDPG baseline. No equations, self-citations, or load-bearing steps are quoted that reduce any claimed result to an input by construction, fitted parameter renamed as prediction, or self-referential uniqueness theorem. The simulation results and algorithm design remain independent of the reported metrics.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard DRL convergence assumptions, a simulation model of UAV dynamics and wireless channels, and the premise that hierarchical decomposition reduces compute without losing optimality. No explicit free parameters, new axioms, or invented entities are named in the abstract.

axioms (2)

standard math Standard assumptions of deep deterministic policy gradient convergence under Markov decision process formulation
Invoked implicitly when claiming faster convergence of TBH-DDPG
domain assumption Simulation environment faithfully represents real UAV flight dynamics, jammer interference, and time-varying IoT data volumes
Required for the reported performance numbers to transfer outside simulation

pith-pipeline@v0.9.0 · 5608 in / 1470 out tokens · 59465 ms · 2026-05-08T06:57:57.143601+00:00 · methodology

discussion (0)

Reference graph

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