Joint Resource Estimation and Trajectory Optimization for eVTOL-involved CR network: A Monte Carlo Tree Search-based Approach

Chau Yuen; Chenxin Yang; Kai Xiong; Wanzhi Ma; Yujie Qin

REVIEW 3 major objections 8 minor 39 references

Joint Resource Estimation and Trajectory Optimization for eVTOL-involved CR network: A Monte Carlo Tree Search-based Approach

T0 review · 3 major / 8 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Tree-search planner lifts eVTOL task success by 17 percent

desk verdict Real problem, sensible formulation, but Algorithm 2 is not MCTS and the reported gains are not what they seem. read the letter →

arxiv 2504.18031 v2 pith:4XK4RJET submitted 2025-04-25 cs.NI eess.SP

Kai Xiong , Chenxin Yang , Yujie Qin , Wanzhi Ma , Chau Yuen This is my paper · ORCID

classification cs.NIeess.SP

keywords eVTOLAdvancedAirMobilitycognitiveradioMonteCarloTreeSearchmulti-armedbandittrajectoryoptimizationtaskoffloadingspectrumavailabilityestimation

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Electric vertical take-off and landing (eVTOL) aircraft must offload safety-critical computation to ground base stations whose spectrum and CPU resources shift with the time of day, and this paper tries to plan the offloading so that tasks finish within time and battery limits. The central claim is that the joint choice of which base stations to visit, in what order, and for how long can be solved by separating temporal resource estimation from spatial route search. A multi-armed bandit learns each station's spectrum-availability pattern first; a Monte Carlo Tree Search then scores candidate station–time pairs by a weighted mix of spectrum availability, expected CPU cycle length, and flight energy. Simulations against shortest-path and epsilon-greedy baselines report up to 60 percent energy-efficiency gains and a 17 percent higher task-completion probability in resource-limited settings. A sympathetic reader would come away with a two-stage planner that also re-plans mid-flight when a station fails to deliver its promised resources.

What carries the argument

The load-bearing object is the modified UCB reward of Eq. (10), $\theta'(a) = c_1 \hat{P}_{\mathrm{sp}}(t_d^*) + c_2 \hat{L}_{\mathrm{cp}}(t_d^*) + c_3 W_i$, which converts the three competing concerns — spectrum availability, CPU-cycle length, and interval energy cost — into a single score used to select both time periods and base stations inside the MCTS tree. The tree itself alternates time-period nodes and base-station nodes, with backpropagation updating empirical success counts (as in Example 1), and access failures trigger re-expansion of the tree. The paper's complexity analysis gives the search a per-iteration cost of $O(n \cdot (b \log_b t + k))$, with the bandit prior argued to shrink the effective search space.

What would settle it

Take a small instance—five base stations and three time periods—enumerate every feasible BS sequence and time-slot assignment, and compare the true objective P1 value of the exhaustive optimum with the trajectory produced by the MCTS planner under the paper's default weights; if the MCTS trajectory's true task-completion probability falls below the exhaustive optimum by more than the reported 17 percent margin, the surrogate reward is misaligned with the stated objective.

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Extended reading notes

Core claim

The paper's core discovery is that the mixed-integer nonlinear program P1 — minimizing flight-plus-hover energy while maximizing offloading success probability under time, velocity, acceleration, and battery constraints — can be effectively attacked by a bandit-plus-tree-search pipeline without convex relaxation or exhaustive search. The bandit stage estimates the probability that each base station has an idle spectrum hole at each time period, using an upper confidence bound rule, and records the mean available CPU-cycle duration when a connection is made. The tree-search stage builds a decision tree whose nodes alternate time periods and base stations; each node's value is the modified UCB reward in Eq. (10), a weighted sum of estimated spectrum availability, estimated CPU processing time, and the energy cost of the travel interval. When execution reveals a failed access, the failed station is removed and the tree is re-expanded, so the same machinery handles both offline pre-planning and online re-planning. The reported 17 percent task-completion gain and 60 percent energy-efficiency gain are the claimed evidence that this two-stage architecture outperforms visiting all stations via the shortest path and epsilon-greedy exploration.

Load-bearing premise

The whole pipeline rests on the surrogate reward in Eq. (10), whose three weights are set without a derivation linking them to the original MINLP objective; if those weights are chosen poorly, the route the tree search returns is not optimal for P1.

Editorial extensions

If this is right

If the algorithm works as claimed, eVTOL operators can pre-plan an offload route before takeoff and re-plan it in flight without needing an exhaustive optimizer, because the MCTS search stays tractable as the number of base stations grows.
In resource-scarce networks the method's advantage appears as higher task-completion probability (up to 17 percent); in resource-rich networks the advantage shifts to lower energy use, since it visits fewer base stations than exhaustive routing.
The MAB estimation stage means the planner does not depend on a specific spectrum-availability distribution; the paper reports consistent task-completion behavior when availability follows a Poisson rather than a Gaussian law.
In denser networks the gap over the epsilon-greedy baseline widens, suggesting the method scales better than fixed-random exploration as the search space grows.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Because Eq. (10) leaves the weights $c_1, c_2, c_3$ unspecified, a natural extension is to tune them against a small set of validated scenarios or derive them from the KKT conditions of a Lagrangian relaxation of P1; the paper gives no procedure for setting them.
Since the bandit and the tree search share the same UCB statistic, the framework extends naturally to multiple competing swarms by treating other swarms as additional primary users, an extension the paper names as future work.
The 60 percent energy-efficiency figure is a simulation outcome for specific counts of base stations; a hardware-in-the-loop test on a small eVTOL testbed comparing measured battery draw and completed offloads would tell whether the gain survives real propulsion and communication dynamics.
The re-planning on failure behavior is effectively online planning under model misspecification; bounding the regret of the MCTS planner against a clairvoyant offline optimizer with biased resource estimates would give the method a firmer theoretical footing than the reported simulations.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The problem is real, but the paper's central claim is not supported by what is actually implemented. Algorithm 2, the thing that produces the reported results, is not a Monte Carlo tree search. It is two nested UCB loops: pick the best time phase by UCB, then pick the best BS by UCB, and repeat after failures. There is no node expansion, no rollout, no backpropagation. The four MCTS phases described in Section IV-B never appear in the pseudocode. So the 60%/17% gains and the regret plots validate an iterative UCB scheduler, not the proposed MCTS. This is a load-bearing mismatch, not a typo.

What the paper does well: the system model is coherent. Formulating the joint trajectory, BS selection, and offloading time problem for eVTOL swarms under time-varying CR resources is a legitimate extension of the UAV-offloading literature. The MAB-based pre-learning of spectrum availability is a sensible idea, and comparing against TSP and epsilon-greedy is a reasonable first benchmark set.

The soft spots go beyond the algorithm mismatch. Equation (10) introduces a weighted surrogate reward with no derivation linking it to the MINLP objective P1; the weights c1, c2, c3 are unspecified. The simulation section lacks error bars, gives no energy or velocity parameters, and the text conflicts with the table on time units (40-70 seconds in Table II versus '20 minutes' in the text). The abstract's claim of up to 60% energy improvement is not clearly supported by the aggregate figures. Figure 8 attributes the regret gap to backpropagation, but Algorithm 2 has no such mechanism.

Who is this for? Readers working on eVTOL offloading in cognitive radio networks might find the problem formulation and the UCB heuristic useful as a starting point. But as a validation of MCTS-based joint optimization, the paper fails. The authors need to either implement real MCTS or relabel Algorithm 2 as an iterative UCB scheduler, add reproducibility details, and re-evaluate. I would not send this to peer review in its current form; it would be a waste of reviewer time. A competent referee would hit the same wall within minutes. The underlying problem is worth another shot after a major rewrite.

Referee Report

3 major / 8 minor

Summary. The manuscript proposes a joint trajectory and task-offloading optimization framework for eVTOL swarms in cognitive radio networks. The authors formulate a mixed-integer nonlinear program (P1) that minimizes a weighted combination of energy consumption and the negative task-completion probability, subject to time, kinematic, and battery constraints. The proposed solution has two stages: a Multi-Armed Bandit (MAB) model with UCB-based selection to estimate time-varying BS spectrum availability, and a Monte Carlo Tree Search (MCTS) algorithm to choose BSs and access time windows, with adaptive re-planning on access failure. Numerical comparisons against TSP and epsilon-greedy baselines report up to 60% energy-consumption improvement and 17% higher task-completion probability, and the paper includes additional regret comparisons and a robustness check under a Poisson resource distribution.

Significance. The problem addressed is timely and practically relevant: eVTOL task offloading under dynamic BS spectrum and CPU availability, with energy and time constraints, is an important AAM challenge. The re-planning mechanism for access failures is well motivated, and the inclusion of a Poisson-distribution robustness check is a useful attempt to go beyond the Gaussian assumption. If the MCTS-based joint optimization were actually implemented and validated as described, the reported gains would be significant. However, the manuscript in its current form does not deliver an executable MCTS algorithm, does not justify the surrogate reward used by the decision rule, and does not provide enough reproducibility detail (no code, no values for weighting coefficients or Gaussian parameters, no trial counts) to substantiate the numerical claims.

major comments (3)

[Section IV-B, Algorithm 2] Algorithm 2 does not implement the Monte Carlo Tree Search described in the prose. Lines 5-18 of the pseudocode compute UCB values for time phases and BSs, select the argmax of each, and on failure deselect and repeat; there is no expansion of a tree node, no simulation or rollout to a terminal state, and no backpropagation of a value along a visited path. The four MCTS steps listed in Section IV-B (selection, expansion, simulation, backpropagation) are absent from the executable specification, and Example 1's node counts are not produced by any equation in the paper. Consequently, Figures 5-8 and Tables III-IV validate an iterative UCB scheduler, not the proposed MCTS method, and the claim in Section V-E that backpropagation explains the regret gap between UCT and the proposed method has no algorithmic basis.
[Section IV-B, Lemma 4, Eq. (10)-(11)] The surrogate reward in Eq. (10) is introduced without any derivation connecting it to P1. Since θ'(a) is maximized, the term +c3 W_i rewards higher energy consumption unless c3 is negative, yet no sign constraints on c1, c2, c3 are given anywhere, and no values or sensitivity analysis are reported. Moreover, the Remark following Definition 1 states that the mean CPU cycle duration L_cp can only be obtained when the swarm is already connected to a BS, which makes it infeasible to evaluate \L_cp(t_d) in Eq. (10) for unvisited BSs during decision-making. Step (a) in Eq. (11) also equates hovering time to \L_cp(t_d) and omits the communication time T_com, contradicting constraint C2. As a result, the paper provides no valid argument that maximizing Eq. (10), with any choice of weights, solves or approximately solves the stated MINLP P1.
[Section IV-A, Algorithm 1] Algorithm 1 does not match the UCB solution of Lemma 3. The pseudocode uses an epsilon-greedy style comparison ("if random number ≤ ε"), refers to the undefined "parameters of greedy algorithm", and only loosely says to "Calculate each station's UCB value", without ever computing the index in Eq. (7). It also includes the unexplained step "Formulate a tree structure". Since Algorithm 1 is the stated source of the estimated spectrum availability \P_sp(t_d) used by the subsequent decision-making stage, this mismatch undermines the MAB estimation component of the proposed framework.

minor comments (8)

[Section V, Table II and text] The units are inconsistent: Table II lists the required CPU cycle duration in seconds (20-40), while Section V states 20 minutes, 30 minutes, and 40 minutes for the 5-BS, 7-BS, and 10-BS cases, respectively; Fig. 6 y-axis label "Energy consumption (w)" also mixes power units with energy quantities.
[Figures 8 and 9 captions] The caption of Fig. 8(c) repeats "N=5 BSs" instead of "N=10 BSs", and the caption of Fig. 9(c) has the same repetition; these should be corrected.
[Section IV-B, Lemma 4] The sign convention for the weighting coefficients is never stated; even if negative c3 is intended, its value and interpretation should be given explicitly because Eq. (10) is central to all numerical experiments.
[Section III-B and Section IV-B] The Remark after Definition 1 says CPU-cycle information is not available during pre-learning, but Eq. (10) requires \L_cp(t_d) at decision time; the paper needs to explain how this quantity is obtained or replace it with a learnable prior.
[Section V-A, Fig. 5] No statistical details are reported: there are no error bars, confidence intervals, or numbers of Monte Carlo trials, which is particularly important because some reported differences, such as the N=10 Scenarios 3-4 case, are small.
[Section V-E, Fig. 8] The comparison against "UCT" is not reproducible because no pseudocode or implementation details for UCT are given, and the claim that UCT suffers nearly 100% higher regret due to a lack of backpropagation cannot be checked from the manuscript.
[Section III-C, P1] Constraints C4 and C5 reference q[t] and v[t], but these variables are not defined in the optimization statement; the weight λ in the objective is also never assigned a value in the simulations.
[Table I and Section III] The symbol T is used both for the mission deadline in P1 and for the horizon in the MAB regret expression in Eq. (3), while T_d is listed as the number of discrete time periods and t_d is the period index; this overloaded notation should be disambiguated.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the MAB-MCTS pipeline is evaluated on independently simulated task runs and no fitted parameter is renamed as a prediction.

full rationale

The paper's derivation chain does not exhibit any reduction of a predicted quantity to a fitted input. The MAB stage (Algorithm 1) estimates Psp(td) from simulated historical access trials, and the MCTS stage consumes those estimates to choose BS-time pairs; the reported task completion probability, energy consumption, access success rates, and regret curves are then measured on separately simulated task runs with Gaussian and Poisson resource distributions, not by plugging fitted estimates back into the training data. No parameters (c1, c2, c3, ηc, ε) are fitted to reproduce the reported benchmarks; c1-c3 are left unspecified, which creates an unproven-optimality gap but not circularity. The self-citations [10], [11], and [13] by authors Qin, Xiong, and Yuen appear only as background on aerial-user coverage, task offloading, and battery-limited drones, and they are not load-bearing for the MCTS/MAB contribution. The mismatch between the four MCTS steps described in the text and the UCB-loop pseudocode in Algorithm 2, as well as the absence of a proof connecting the surrogate reward θ'(a) in Eq. (10) to the MINLP objective P1, are correctness and validity concerns rather than instances of self-definition, fitted-input-called-prediction, or self-citation load-bearing circularity.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The paper does not introduce new physical entities. The central algorithm rests on a set of domain assumptions about BS resource distributions, energy modeling, task continuity, and data availability, plus several unreported hyperparameters (lambda, c1-c3, energy constants). These choices are not backed by independent evidence and materially affect the reported performance numbers.

free parameters (4)

lambda = not reported
Weight coefficient in the P1 objective that balances energy consumption against task offloading success probability. The value is not given in Table II or the text, yet it directly affects the 'objective function' results in Fig. 7.
c1, c2, c3 = not reported
Weighting coefficients in the modified UCB expected reward (Eq. 10) that determine how the MCTS balances spectrum availability, CPU processing time, and energy. No values or tuning procedure are provided.
P_h, P_f(v), B_max = not reported
Energy model constants and battery capacity used in Eqs. (2) and (11) to compute total energy consumption and the energy constraint. These are needed for the energy plots in Fig. 6 but are absent from Table II.
Gaussian parameters (mu_i(t_d), sigma_i^2(t_d)) = not reported
The simulation assumes P_sp,i(t_d) follows a Gaussian distribution with different means and variances for each BS and time period. The specific values used to generate the scenarios are not reported.

assumptions (5)

domain assumption Spectrum availability P_sp,i(t_d) follows Gaussian distributions with different means and variances (Section III).
This is a modeling assumption for the simulation. The MAB algorithm itself does not require Gaussianity, and later a Poisson distribution is tested, so the assumption affects only the evaluation scenarios.
domain assumption The hovering time T_h(i) equals the available CPU cycle duration at the BS (Eq. 11, step (a)).
The energy calculation assumes that task offloading, processing, and result transmission fit exactly into the available CPU cycle window. This simplifies the energy model but may not hold in practice.
domain assumption Task continuity across BSs: uncompleted tasks are resumed at subsequent BSs (Section III).
The system model allows splitting a task across multiple BSs. If tasks cannot be resumed, the multi-BS offloading strategy and the entire problem formulation break down.
domain assumption A single eVTOL represents the energy consumption of the entire swarm (Section III-A).
The paper uses one eVTOL as a proxy for the swarm's energy, ignoring inter-swarm coupling or distribution effects. This simplification makes the model tractable but may not reflect real swarm behavior.
domain assumption Historical BS operation data is available from the ATC system before takeoff (Section V).
The MAB pre-learning stage relies on historical data to estimate spectrum availability. If such data is not available, the proposed method would have to rely solely on online exploration, which could reduce its advantage.

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Cite this review

Pith. "Pith review of Joint Resource Estimation and Trajectory Optimization for eVTOL-involved CR network: A Monte Carlo Tree Search-based Approach." pith.science (2026). https://pith.science/paper/4XK4RJET

@misc{pith2026250418031,
  author       = {Pith},
  title        = {Pith review of: Joint Resource Estimation and Trajectory Optimization for eVTOL-involved CR network: A Monte Carlo Tree Search-based Approach},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4XK4RJET}},
  note         = {Machine review of arXiv:2504.18031}
}

read the original abstract

Electric Vertical Take-Off and Landing (eVTOL) aircraft, pivotal to Advanced Air Mobility (AAM), are emerging as a transformative transportation paradigm with the potential to redefine urban and regional mobility. While these systems offer unprecedented efficiency in transporting people and goods, they rely heavily on computation capability, safety-critical operations such as real-time navigation, environmental sensing, and trajectory tracking--necessitating robust offboard computational support. A widely adopted solution involves offloading these tasks to terrestrial base stations (BSs) along the flight path. However, air-to-ground connectivity is often constrained by spectrum conflicts with terrestrial users, which poses a significant challenge to maintaining reliable task execution. Cognitive radio (CR) techniques offer promising capabilities for dynamic spectrum access, making them a natural fit for addressing this issue. Existing studies often overlook the time-varying nature of BS resources, such as spectrum availability and CPU cycles, which leads to inaccurate trajectory planning, suboptimal offloading success rates, excessive energy consumption, and operational delays. To address these challenges, we propose a trajectory optimization framework for eVTOL swarms that maximizes task offloading success probability while minimizing both energy consumption and resource competition (e.g., spectrum and CPU cycles) with primary terrestrial users. The proposed algorithm integrates a Multi-Armed Bandit (MAB) model to dynamically estimate BS resource availability and a Monte Carlo Tree Search (MCTS) algorithm to determine optimal offloading decisions, selecting both the BSs and access time windows that align with energy and temporal constraints.

Figures

Figures reproduced from arXiv: 2504.18031 by the authors.

**Figure 1.** Illustration of the eVTOL task offloading scenario. We consider a scenario where the eVTOL follows a predetermined trajectory, during which passengers onboard request internet access and entertainment services, as shown in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

**Figure 2.** Illustration of the CR-based eVTOL network. A. Energy Consumption Consider the onboard battery of the eVTOLs as one of the primary limitations of the system. eVTOLs rely on their internal batteries for power supply, and as a result, their flight or hovering time is constrained. The energy consumption model in this work is based on [37]. Let Ph denote the hovering-related energy consumption, which includes both the h… view at source ↗

**Figure 3.** Illustration of the trajectory optimization model. The goal of the BS temporal resource availability estimation stage is to use the MAB model to pre-learn (or estimate) the spectrum availability at each BS over different periods of the day. Based on this pre-learned information, Stage 2 employs an MCTS-based algorithm to optimize the swarm’s trajectory, considering spectrum availability, CPU cycles, remaining tasks… view at source ↗

Figures from the paper (7 more)

**Figure 4.** Figure 4: Illustration of the MCTS-based model diagram. V. NUMERICAL RESULTS In this section, we validate the proposed algorithm through simulations via MATLAB. Unless otherwise stated, the simulation parameters used in this work are provided in Table II. The simulation details…

**Figure 5.** Figure 5: Simulation results of the task completion probability, η, under varying numbers of BSs and CPU cycle durations, (a) N = 5 BSs with a required CPU cycle duration of 20 minutes, (b) N = 7 BSs with a required CPU cycle duration of 30 minutes, and (c) N = 10 BSs with a req…

**Figure 6.** Figure 6: Energy consumption comparison under different number of the accessed BSs while the network has (a) N = 5 BSs, (b) N = 7 BSs, (c) N = 10 BSs. failure probability (defined as the negative of task completion probability), obtained by summing the results from the previous…

**Figure 7.** Figure 7: Results of the objective function, P1, under three algorithms when (a) N = 5 BSs, (b) N = 7 BSs, and (c) N = 10 BSs. 0 1 2 3 4 Iterations 104 0 5 10 15 20 Accumulated Regret -greedy UCB UCT MCTS (a) 0 1 2 3 4 Iterations 104 0 5 10 15 20 25 Accumulated Regret -greedy UC…

**Figure 8.** Figure 8: Accumulative regrets under different algorithms when (a) N = 5 BSs, (b) N = 7 BSs, and (c) N = 5 BSs. E. Accumulated Regret Without Prior Estimation To further illustrate the convergence properties of the proposed algorithm, we plot the accumulated regret, as defined …

**Figure 9.** Figure 9: Illustration of the eVTOL trajectories under different algorithms v.s. the number of BSs (a) N = 5 BSs, (b) N = 7 BSs, and (c) N = 5 BSs. 1 2 3 4 0 20 40 60 80 100 120 140 160 180 [PITH_FULL_IMAGE:figures/full_fig_p013_9.png]

**Figure 10.** Figure 10: Results of the objective function, P1, N = 5 BSs, under the Poisson distribution BSs and Poisson resource distribution. The x-axis represents the four resource availability scenarios (that is same with [PITH_FULL_IMAGE:figures/full_fig_p013_10.png]

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Works this paper leans on

39 extracted references · 37 canonical work pages

[1]

NASA concept vehicles and the engineering of advanced air mobility aircraft,

W. Johnson and C. Silva, “NASA concept vehicles and the engineering of advanced air mobility aircraft,”The Aeronautical Journal, vol. 126, no. 1295, pp. 59–91, 2022

work page 2022
[2]

Advanced air mobility: An innovation for future diversified transportation and society,

J. Guo, L. Chen, L. Li, X. Na, L. Vlacic, and F.-Y . Wang, “Advanced air mobility: An innovation for future diversified transportation and society,” IEEE Transactions on Intelligent Vehicles, 2024

work page 2024
[3]

Urban air mobility: His- tory, ecosystem, market potential, and challenges,

A. P. Cohen, S. A. Shaheen, and E. M. Farrar, “Urban air mobility: His- tory, ecosystem, market potential, and challenges,”IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 9, pp. 6074–6087, 2021

work page 2021
[4]

Advanced air mobility: Demand analysis and market potential of the airport shuttle and air taxi markets,

R. Goyal, C. Reiche, C. Fernando, and A. Cohen, “Advanced air mobility: Demand analysis and market potential of the airport shuttle and air taxi markets,”Sustainability, vol. 13, no. 13, p. 7421, 2021

work page 2021
[5]

Advanced air mobility: An innova- tion for future diversified transportation and society,

J. Guo, L. Chen, L. Li, X. Naet al., “Advanced air mobility: An innova- tion for future diversified transportation and society,”IEEE Transactions on Intelligent Vehicles, vol. 9, no. 2, pp. 3106–3110, 2024

work page 2024
[6]

Utilizing evtol aircraft to alleviate traffic congestion on an arterial road,

B.-K. Xiong, R. Jiang, K. Wang, X. Tang, Y . Shang, and X. Qu, “Utilizing evtol aircraft to alleviate traffic congestion on an arterial road,”IEEE Transactions on Intelligent Transportation Systems, pp. 1– 13, 2025

work page 2025
[7]

Roadmap AAM (2021-2030),

Italian Civil Aviation Authority, “Roadmap AAM (2021-2030),” 2021, https://www.enac.gov.it/app/uploads/2024/04/02 AAM-Italian- Ecosystem-%E2%80%93-Project-overview-and-Roadmap web-1.pdf, Last accessed on 2024-11-25

2021
[8]

Au- tonomous navigation for eVTOL: Review and future perspectives,

H. Wei, B. Lou, Z. Zhang, B. Liang, F.-Y . Wang, and C. Lv, “Au- tonomous navigation for eVTOL: Review and future perspectives,”IEEE Transactions on Intelligent Vehicles, vol. 9, no. 2, pp. 4145–4171, 2024

2024

Show all 39 references

[9]

Dedicating cellular infras- tructure for aerial users: Advantages and potential impact on ground users,

L. Chen, M. A. Kishk, and M.-S. Alouini, “Dedicating cellular infras- tructure for aerial users: Advantages and potential impact on ground users,”IEEE Transactions on Wireless Communications, vol. 22, no. 4, pp. 2523–2535, 2022

2022
[10]

Coverage analysis and trajec- tory optimization for aerial users with dedicated cellular infrastructure,

Y . Qin, M. A. Kishk, and M.-S. Alouini, “Coverage analysis and trajec- tory optimization for aerial users with dedicated cellular infrastructure,” IEEE Transactions on Wireless Communications, vol. 23, no. 4, pp. 3042–3056, 2023

2023
[11]

Intelligent task offloading for heterogeneous v2x communications,

K. Xiong, S. Leng, C. Huang, C. Yuen, and Y . L. Guan, “Intelligent task offloading for heterogeneous v2x communications,”IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 4, pp. 2226–2238, 2021

2021
[12]

Machine learning predictions of lithium-ion battery state-of-health for eVTOL applications,

L. Granado, M. Ben-Marzouk, E. S. Saenz, Y . Boukal, and S. Jug ´e, “Machine learning predictions of lithium-ion battery state-of-health for eVTOL applications,”Journal of Power Sources, vol. 548, p. 232051, 2022

2022
[13]

Effects of range requirements and battery technology on electric VTOL sizing and operational performance,

M. Warren, A. Garbo, M. T. Kotwicz Herniczek, T. Hamilton, and B. German, “Effects of range requirements and battery technology on electric VTOL sizing and operational performance,” inAIAA SciTech 2019 forum, 2019, p. 0527

2019
[14]

Performance evaluation of UA V-enabled cellular networks with battery-limited drones,

Y . Qin, M. A. Kishk, and M.-S. Alouini, “Performance evaluation of UA V-enabled cellular networks with battery-limited drones,”IEEE Communications Letters, vol. 24, no. 12, pp. 2664–2668, 2020

2020
[15]

Joint mobility, communication and computation optimization for uavs in air-ground cooperative networks,

J. Zhou, D. Tian, Z. Sheng, X. Duan, and X. Shen, “Joint mobility, communication and computation optimization for uavs in air-ground cooperative networks,”IEEE Transactions on Vehicular Technology, vol. 70, no. 3, pp. 2493–2507, 2021

2021
[16]

Tilt-wing eVTOL takeoff trajectory optimization,

S. S. Chauhan and J. R. Martins, “Tilt-wing eVTOL takeoff trajectory optimization,”Journal of aircraft, vol. 57, no. 1, pp. 93–112, 2020

2020
[17]

Wind-optimal trajectories for multirotor eVTOL aircraft on uam missions,

P. Pradeep, T. A. Lauderdale, G. B. Chatterji, K. Sheth, C. F. Lai, B. Sridhar, K.-M. Edholm, and H. Erzberger, “Wind-optimal trajectories for multirotor eVTOL aircraft on uam missions,” inAiaa Aviation 2020 Forum, 2020, p. 3271

2020
[18]

Teevtol: Balancing energy and time efficiency in eVTOL aircraft path planning across city-scale wind fields,

S. Liu, S. Li, H. Li, W. Li, and J. Tan, “Teevtol: Balancing energy and time efficiency in eVTOL aircraft path planning across city-scale wind fields,”arXiv preprint arXiv:2403.14877, 2024

2024 arXiv
[19]

Energy efficiency enhanced landing strategy for manned eVTOLs using L1 adaptive control,

Z. Wang, S. Mao, Z. Gong, C. Zhang, and J. He, “Energy efficiency enhanced landing strategy for manned eVTOLs using L1 adaptive control,”Symmetry, vol. 13, no. 11, p. 2125, 2021

2021
[20]

Trajectory optimization for takeoff and landing phase of UAM considering energy and safety,

J. Park, I. Kim, J. Suk, and S. Kim, “Trajectory optimization for takeoff and landing phase of UAM considering energy and safety,”Aerospace Science and Technology, vol. 140, p. 108489, 2023

2023
[21]

Inverse machine learning prediction for optimal tilt-wing eVTOL takeoff trajectory,

S.-T. Yeh, G. Yan, and X. Du, “Inverse machine learning prediction for optimal tilt-wing eVTOL takeoff trajectory,” inAIAA AVIATION 2023 Forum, 2023, p. 3593

2023
[22]

Cognitive uav com- munication via joint maneuver and power control,

Y . Huang, W. Mei, J. Xu, L. Qiu, and R. Zhang, “Cognitive uav com- munication via joint maneuver and power control,”IEEE Transactions on Communications, vol. 67, no. 11, pp. 7872–7888, 2019

2019
[23]

Resource allocation in cognitive radio-enabled uav communication,

S. K. Nobar, M. H. Ahmed, Y . Morgan, and S. A. Mahmoud, “Resource allocation in cognitive radio-enabled uav communication,”IEEE Trans- actions on Cognitive Communications and Networking, vol. 8, no. 1, pp. 296–310, 2022

2022
[24]

Joint 3D trajectory and resource optimization for a UA V relay-assisted cognitive radio network,

Z. Wang, F. Zhou, Y . Wang, and Q. Wu, “Joint 3D trajectory and resource optimization for a UA V relay-assisted cognitive radio network,” China Communications, vol. 18, no. 6, pp. 184–200, 2021

2021
[25]

Joint optimization of trajectory and resource allocation for time- constrained UA V-enabled cognitive radio networks,

Y . Pan, X. Da, H. Hu, Y . Huang, M. Zhang, K. Cumanan, and O. A. Dobre, “Joint optimization of trajectory and resource allocation for time- constrained UA V-enabled cognitive radio networks,”IEEE Transactions on Vehicular Technology, vol. 71, no. 5, pp. 5576–5580, 2022

2022
[26]

When UA Vs meet cognitive radio: Offloading traffic under uncertain spectrum environment via deep reinforcement learning,

X. Li, S. Cheng, H. Ding, M. Pan, and N. Zhao, “When UA Vs meet cognitive radio: Offloading traffic under uncertain spectrum environment via deep reinforcement learning,”IEEE Transactions on Wireless Com- munications, vol. 22, no. 2, pp. 824–838, 2023

2023
[27]

Joint UA V trajectory and power allocation optimization for NOMA in cognitive radio network,

D. Deng and M. Zhu, “Joint UA V trajectory and power allocation optimization for NOMA in cognitive radio network,”Physical Com- munication, vol. 46, p. 101328, 2021

2021
[28]

UA V- assisted secure communications in terrestrial cognitive radio networks: Joint power control and 3D trajectory optimization,

P. X. Nguyen, V .-D. Nguyen, H. V . Nguyen, and O.-S. Shin, “UA V- assisted secure communications in terrestrial cognitive radio networks: Joint power control and 3D trajectory optimization,”IEEE Transactions on Vehicular Technology, vol. 70, no. 4, pp. 3298–3313, 2021

2021
[29]

Digital twin assisted deep reinforcement learning for computation offloading in uav systems,

K. Liang, Y . Wang, Z. Li, G. Zheng, K.-K. Wong, and C.-B. Chae, “Digital twin assisted deep reinforcement learning for computation offloading in uav systems,”IEEE Transactions on Vehicular Technology, pp. 1–6, 2025

2025
[30]

Uav-assisted target tracking and computation offloading in usv-based mec networks,

Z. Wang, J. Du, C. Jiang, Y . Ren, and X.-P. Zhang, “Uav-assisted target tracking and computation offloading in usv-based mec networks,”IEEE Transactions on Mobile Computing, vol. 23, no. 12, pp. 11 389–11 405, 2024

2024
[31]

Dronesegnet: Robust aerial semantic segmentation for uav- based iot applications,

A. S. Chakravarthy, S. Sinha, P. Narang, M. Mandal, V . Chamola, and F. R. Yu, “Dronesegnet: Robust aerial semantic segmentation for uav- based iot applications,”IEEE Transactions on Vehicular Technology, vol. 71, no. 4, pp. 4277–4286, 2022

2022
[32]

Energy efficient transmission strategy for mobile edge computing network in uav-based patrol inspection system,

D. Yang, J. Wang, F. Wu, L. Xiao, Y . Xu, and T. Zhang, “Energy efficient transmission strategy for mobile edge computing network in uav-based patrol inspection system,”IEEE Transactions on Mobile Computing, vol. 23, no. 5, pp. 5984–5998, 2024

2024
[33]

Joint multi-task offloading and resource allocation for mobile edge computing systems in satellite iot,

F. Chai, Q. Zhang, H. Yao, X. Xin, R. Gao, and M. Guizani, “Joint multi-task offloading and resource allocation for mobile edge computing systems in satellite iot,”IEEE Transactions on Vehicular Technology, vol. 72, no. 6, pp. 7783–7795, 2023

2023
[34]

Dynamic task offloading for multi-uavs in vehicular edge computing with delay guarantees: A consensus admm-based optimization,

R. Huang, W. Wen, Z. Zhou, C. Dong, C. Qiao, Z. Tian, and X. Chen, “Dynamic task offloading for multi-uavs in vehicular edge computing with delay guarantees: A consensus admm-based optimization,”IEEE Transactions on Mobile Computing, vol. 23, no. 12, pp. 13 696–13 712, 2024

2024
[35]

Two-stage deep energy optimization in irs-assisted uav-based edge computing systems,

J. Wu, Z. Yu, J. Guo, Z. Tang, T. Wang, and W. Jia, “Two-stage deep energy optimization in irs-assisted uav-based edge computing systems,” IEEE Transactions on Mobile Computing, vol. 24, no. 1, pp. 449–465, 2025

2025
[36]

Analytical evaluation of outage probabil- ity based on signal to interference ratio for gaussian-distributed users,

S. Baroudi and Y . R. Shayan, “Analytical evaluation of outage probabil- ity based on signal to interference ratio for gaussian-distributed users,” in2015 IEEE Symposium on Computers and Communication (ISCC), 2015, pp. 841–844

2015
[37]

Uav-enabled wireless power transfer with base station charging and uav power consumption,

H. Yan, Y . Chen, and S.-H. Yang, “Uav-enabled wireless power transfer with base station charging and uav power consumption,”IEEE Trans- actions on Vehicular Technology, vol. 69, no. 11, pp. 12 883–12 896, 2020

2020
[38]

Sample-efficient neural architecture search by learning actions for monte carlo tree search,

L. Wang, S. Xie, T. Li, R. Fonseca, and Y . Tian, “Sample-efficient neural architecture search by learning actions for monte carlo tree search,”IEEE 15 Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 9, pp. 5503–5515, 2022

2022
[39]

Bandit based monte-carlo planning,

L. Kocsis and C. Szepesv ´ari, “Bandit based monte-carlo planning,” inMachine Learning: ECML 2006, J. F ¨urnkranz, T. Scheffer, and M. Spiliopoulou, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006, pp. 282–293

2006

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.

[1] [1]

NASA concept vehicles and the engineering of advanced air mobility aircraft,

W. Johnson and C. Silva, “NASA concept vehicles and the engineering of advanced air mobility aircraft,”The Aeronautical Journal, vol. 126, no. 1295, pp. 59–91, 2022

work page 2022

[2] [2]

Advanced air mobility: An innovation for future diversified transportation and society,

J. Guo, L. Chen, L. Li, X. Na, L. Vlacic, and F.-Y . Wang, “Advanced air mobility: An innovation for future diversified transportation and society,” IEEE Transactions on Intelligent Vehicles, 2024

work page 2024

[3] [3]

Urban air mobility: His- tory, ecosystem, market potential, and challenges,

A. P. Cohen, S. A. Shaheen, and E. M. Farrar, “Urban air mobility: His- tory, ecosystem, market potential, and challenges,”IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 9, pp. 6074–6087, 2021

work page 2021

[4] [4]

Advanced air mobility: Demand analysis and market potential of the airport shuttle and air taxi markets,

R. Goyal, C. Reiche, C. Fernando, and A. Cohen, “Advanced air mobility: Demand analysis and market potential of the airport shuttle and air taxi markets,”Sustainability, vol. 13, no. 13, p. 7421, 2021

work page 2021

[5] [5]

Advanced air mobility: An innova- tion for future diversified transportation and society,

J. Guo, L. Chen, L. Li, X. Naet al., “Advanced air mobility: An innova- tion for future diversified transportation and society,”IEEE Transactions on Intelligent Vehicles, vol. 9, no. 2, pp. 3106–3110, 2024

work page 2024

[6] [6]

Utilizing evtol aircraft to alleviate traffic congestion on an arterial road,

B.-K. Xiong, R. Jiang, K. Wang, X. Tang, Y . Shang, and X. Qu, “Utilizing evtol aircraft to alleviate traffic congestion on an arterial road,”IEEE Transactions on Intelligent Transportation Systems, pp. 1– 13, 2025

work page 2025

[7] [7]

Roadmap AAM (2021-2030),

Italian Civil Aviation Authority, “Roadmap AAM (2021-2030),” 2021, https://www.enac.gov.it/app/uploads/2024/04/02 AAM-Italian- Ecosystem-%E2%80%93-Project-overview-and-Roadmap web-1.pdf, Last accessed on 2024-11-25

2021

[8] [8]

Au- tonomous navigation for eVTOL: Review and future perspectives,

H. Wei, B. Lou, Z. Zhang, B. Liang, F.-Y . Wang, and C. Lv, “Au- tonomous navigation for eVTOL: Review and future perspectives,”IEEE Transactions on Intelligent Vehicles, vol. 9, no. 2, pp. 4145–4171, 2024

2024