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REVIEW 3 major objections 6 minor 1 cited by

Digital Twin-based SIM Communication and Flight Control for Advanced Air Mobility

T0 review · 3 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A digital twin of a stacked intelligent metasurface can jointly optimize beamforming and eVTOL flight control inside prescribed corridors, yielding an 8.3% rate gain and a 10% corridor-deviation cut.

desk verdict The SIM+CPF integration is plausible, but Eq. (10) uses the wrong channel in the interference term, invalidating every reported gain. read the letter →

arxiv 2501.01837 v1 pith:FNINERYV submitted 2025-01-03 cs.NI eess.SP

Kai Xiong , Zhen Chen , Juefei Xie , Supeng Leng , Chau Yuen This is my paper · ORCID
classification cs.NIeess.SP
keywords AdvancedAirMobilityeVTOLStackedIntelligentMetasurfaceDigitalTwinCompositePotentialFieldDeepQ-Networkair-groundcommunicationflightcorridor
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

The paper sets out to show that a ground station can jointly handle two problems that are usually treated separately: keeping an air-ground beam locked onto moving eVTOLs and keeping those eVTOLs inside mandated flight corridors. It does this by running both optimizations inside a digital twin, with one twin for the stacked intelligent metasurface (SIM) and one for the eVTOL platoon. The SIM twin tunes transmission power and layer-wise phase shifts, while the eVTOL twin tunes the weights of a composite potential field that steers each aircraft; the twins iterate until a shared rate-and-safety objective converges. If the proposal is right, eVTOLs can get higher-rate links to air traffic control without leaving their corridors and without heavy onboard computation. The paper reports an 8.3% transmission-rate improvement over a potential-field flight-control baseline and a 10% reduction in distance deviation from the prescribed corridor compared to predetermined optimization.

What carries the argument

The load-bearing object is the SIM beamforming matrix $G[n] = \Psi^L[n] W^L \Psi^{L-1}[n] \cdots \Psi^2[n] W^2 \Psi^1[n]$, built from per-layer phase-shift matrices $\Psi^l[n]=\mathrm{diag}(e^{j\theta^l_1[n]},\dots,e^{j\theta^l_K[n]})$ and Rayleigh–Sommerfeld transmission matrices $W^l$; the communication objective rides on $R_m[n]=\log(1+\mathrm{SINR}_m[n])$ with the SINR defined in Eq. (10). On the flight side, the Composite Potential Field sums a target field, a separation field, and a communication field, and its coefficients $\{k_{\rm tar},k_{\rm sep},k_{\rm com}\}$ are chosen by DQN so that acceleration satisfies $a_i[n]=-\nabla(F_i^{\rm tar}+F_i^{\rm sep}+F_i^{\rm com})$. Block coordinate descent couples the two sides: power and phase are updated with trajectories fixed, then trajectories are updated with the SIM settings fixed, and the DT synchronization loop sends only the three coefficients and current positions back and forth.

What would settle it

Repeat the Section V simulations with the interference term in Eq. (10) rewritten as $\sum_{m'\neq m} |h_m^H G w_{m'}^1|^2 p_{m'}$, keeping all other parameters fixed, and check whether the sum rate and the 8.3% figure survive; also log the positions $\{q_m[n]\}$ produced by the DQN policy in the five-obstacle corridor and test whether any point lies outside $R_{\rm cor}$.

Watch

Extended reading notes

Core claim

The paper's central claim is that the joint optimization problem $P1$ — maximize the sum of SINR-based rates over SIM power allocation $P$, phase-shift matrix $\Psi$, and eVTOL trajectories $Q$ under power, velocity, corridor, and endpoint constraints — can be solved to a useful approximation by alternating two digital twins. The SIM digital twin updates $P$ with a fractional-programming proximal method and updates $\Psi$ with iterative gradient ascent; the eVTOL digital twin uses a DQN to adjust the composite potential-field hyperparameters $\{k_{\rm tar}, k_{\rm sep}, k_{\rm com}\}$, and the resulting trajectory feeds back into the next SIM update. The authors assert that this two-twin loop dynamically beam-tracks eVTOLs within the corridor and quantify the payoff as an 8.3% higher transmission rate than a potential-field benchmark and a 10% smaller mean corridor deviation than a predetermined (no-sync) optimization.

Load-bearing premise

The numerical results depend on the SINR formula in Eq. (10) being a correct description of how the SIM separates one eVTOL's signal from interference, and on the DQN-tuned potential field actually keeping eVTOLs inside the corridor $R_{\rm cor}$; if either of those premises fails, the reported 8.3% and 10% improvements no longer follow.

Editorial extensions

If this is right

  • Corridor compliance and beam tracking stop being competing objectives: the same loop that keeps eVTOLs in the corridor also steers them toward positions with better SINR.
  • Joint power-and-phase optimization matters much more than power alone; the paper's power-only case drives the transmission rate near zero, so the phase degrees of freedom are the main source of the SIM gain.
  • Periodic DT synchronization (the paper tests zero, two, and four syncs over 30 slots) reduces mean distance deviation from the corridor, and the benefit grows when more obstacles disturb the trajectory.
  • Because the physical eVTOL receives only the three potential-field coefficients rather than a full trajectory, the scheme points toward low-overhead synchronization for onboard-constrained aircraft.

Reading between the lines

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

  • If the SINR interference term is corrected to use the desired eVTOL's channel, $h_m^H G w_{m'}^1$, in place of the interfering eVTOL's channel, $h_{m'}^H G w_{m'}^1$, the numerical rates in the evaluations would likely change; the claimed 8.3% gain would need to be re-derived, and the SIM-versus-MIMO ordering might shift.
  • The corridor guarantee is currently implicit: the DQN reward contains no explicit penalty for leaving $R_{\rm cor}$, so the claim that C5 is satisfied rests on the learned policy. A direct corridor term in the reward would make the guarantee testable and would likely be needed before deployment.
  • An untested extension suggested by the architecture is replacing the fixed 'one antenna per eVTOL' assumption with user scheduling or non-orthogonal multiple access, which would determine whether the beam-tracking gain scales to platoons larger than the number of transmit chains.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. The paper proposes a digital-twin-based framework for joint SIM communication and eVTOL flight control in prescribed air corridors. The ATCo station hosts two digital twins: a SIM DT that optimizes downlink transmission power and phase shifts via fractional programming and gradient ascent, and an eVTOL DT that uses a DQN to tune hyperparameters of a composite potential field for target tracking, separation, and communication connectivity. The two DTs iterate to produce SIM parameters and potential-field coefficients that are synchronized to the physical system. Simulation results are reported for transmission rate and corridor-deviation metrics, including a claimed 8.3% improvement in transmission rate over a potential-field benchmark and a 10% reduction in corridor deviation compared with predetermined optimization.

Significance. The proposed architecture is timely and the BCD decomposition is well structured: offloading iterative communication and control optimization to ground-based DTs and transmitting only potential-field coefficients is a practical way to reduce onboard computation and synchronization overhead. The paper also avoids the circularity concern that sometimes arises in DT/RL evaluation: the DQN optimizes a reward that includes the same communication rate used for evaluation, but this is ordinary objective-reward alignment rather than tautology. However, the central quantitative claims cannot be accepted because the SINR model in Eq. (10) is physically incorrect and the corridor constraint C5 is not enforced by the proposed controller; all simulation numbers and comparisons currently rest on these invalid foundations.

major comments (3)
  1. [Section III-B, Eq. (10)] The SINR is inconsistent with the received-signal model in Eq. (9). From Eq. (9), the interference at user m from eVTOL m' is |h_m^H[n]G[n]w^1_{m'}|^2 p_{m'}[n], because the interference propagates through the channel of the desired user m, not through the channel of the interfering user m'. The expression in Eq. (10) instead places |h_{m'}^H[n]G[n]w^1_{m'}|^2 p_{m'}[n] in the denominator. This index error propagates into the Lagrangian and quadratic transformations of Eqs. (14)-(15), the auxiliary-variable and power updates of Eqs. (18)-(22), the phase-gradient computation of Eqs. (24)-(27), and the DQN reward of Eq. (40). Consequently, every reported transmission rate, reward signal, convergence curve, and the claimed 8.3% improvement is computed from a physically incorrect model. The authors should correct the subscript in Eq. (10), propagate the correction through all dependent equations, and rerun all simulations.
  2. [Section IV-B, Eqs. (31)-(40)] Constraint C5 (q_m[n] in R_cor) is only stated; it is not enforced by the CPF algorithm or the DQN reward. The potential fields in Eqs. (32)-(36) contain target, separation, and communication terms only, with no corridor-boundary repulsion or projection onto R_cor, and the reward in Eq. (40) has no corridor-violation penalty. Therefore the statements in Section V that the proposed method maintains trajectories inside the corridor (Figs. 5, 8, 9, 11) are unsupported by the presented mechanism, and the claimed 10% reduction in corridor deviation cannot be tied to satisfaction of C5. The authors should add an explicit corridor-constraint term or projection step, or provide direct measurements of constraint satisfaction along with violation counts.
  3. [Section V, Figs. 4-11] The quantitative claims rest entirely on point estimates from what appears to be a single run. The DQN training involves random initialization and epsilon-greedy exploration, yet no results report multiple seeds, error bars, or confidence intervals. The headline differences are small (8.3% in Fig. 10 and 10% in Fig. 11), so without variance information it is impossible to judge whether these differences are statistically meaningful. Please report means and standard deviations over multiple independent runs for the transmission-rate and corridor-deviation metrics.
minor comments (6)
  1. [Section I, contributions list] The third contribution bullet is duplicated verbatim; two consecutive bullets begin with 'A DT-based optimization framework is proposed...' and contain essentially the same text. Please merge or delete one.
  2. [Eq. (11) and Figs. 4, 6-7] The logarithm base in Eq. (11) is unspecified, and the figures label the y-axis in 'bps'. Please specify whether rates are in nats/s/Hz or bits/s/Hz and adjust the units and axis labels accordingly.
  3. [Eq. (47)] The MIMO baseline uses a scalar channel h_m[n]^2, while h_m[n] is defined as a vector elsewhere in the paper. Please define the equivalent scalar channel model for the baseline so that the comparison is unambiguous.
  4. [Algorithm 2, lines 4-6] The backtracking loop is unclear: the count update 'count = count x step' appears inside the while body, and the line 'According to Eq. (30)' is not integrated into the pseudocode flow. Please rewrite the pseudocode so that the step-size update and the phase update are unambiguous.
  5. [Notation throughout Section III] The symbol N is used both for the set of time slots and for the set of neighboring eVTOLs, for example in Eq. (38). Please use distinct notation for these two sets.
  6. [References [15] and [22]] References [15] and [22] appear to be the same paper (Q. Wu and R. Zhang, 'Beamforming optimization for wireless network aided by intelligent reflecting surface with discrete phase shifts'). Please consolidate the duplicate citation.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the DT-based SIM/CPF optimization is a self-contained simulation study; the only self-citations are minor and not load-bearing.

full rationale

No circular step is established under the required evidence standard. The paper's central claims (8.3% rate improvement and 10% corridor-deviation reduction) are simulation outcomes of the proposed algorithm compared against its own baselines within the same system model; they are not fitted constants and do not reduce to the model inputs by construction. The DQN in Alg. 3 optimizes a reward (Eq. 40) that includes the SIM rate, and the evaluation uses the same rate expression, but this is standard closed-loop optimization rather than circular prediction. The self-citation in Section III.A ('The Digital Twin (DT) modeling framework in this paper builds upon our previous work [21]') is a modeling provenance statement and is not load-bearing: the DT synchronization procedure, BCD updates, and CPF/DQN implementation are specified in this paper and evaluated independently. The citation of [14] for the DRL approach is likewise nonessential because the DQN machinery is standard and separately referenced. Eq. (10) does contain a plausible physical-modeling error (the interference term is written with h_{m'} instead of h_m), and constraint C5 is not explicitly penalized in the DQN reward; however, both are correctness/validity concerns, not instances where the claimed result is equivalent to an input by definition or by self-citation. Therefore no significant circularity is found.

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

The central claim rests on the SIM propagation model inherited from prior work, a mis-specified Rician channel, an unenforced corridor constraint, and several hand-picked reward and algorithm parameters. No new physical entities are introduced.

free parameters (5)
  • Reward weighting factors α1, α2, β = not specified in paper
    In Eq. (40), they balance target, separation, and communication rewards; no sensitivity analysis is provided.
  • Separation and communication thresholds dsep, d_eVTOL_com, d_eVTOL_max = not specified
    They define the active regions of F_sep and F_com in Eqs. (34) and (36).
  • Collision penalty constants c1, c2 = not specified
    They shape the collision risk term S_m in Eq. (31).
  • DQN action step size 0.06 = 0.06
    Ad hoc step for hyperparameter changes in Eq. (39).
  • Algorithm 2 step size and thresholds ξ, ε_thr, f, step = not fully specified
    They affect convergence of the gradient ascent phase optimization.
assumptions (4)
  • domain assumption Rayleigh-Sommerfeld diffraction model for inter-layer SIM propagation, Eq. (5)
    Physical model taken from prior SIM literature; assumed valid for the AAM scenario.
  • domain assumption Rician channel with no NLOS component, Eq. (7), setting h_bar=1
    The channel is modeled as deterministic distance-dependent path loss scaled by sqrt(kappa/(kappa+1)); the Rician factor is not applied to a random component.
  • ad hoc to paper The corridor constraint q_m[n] in R_cor is satisfied by the CPF+DQN even though no corridor penalty appears in the reward
    The DQN reward Eq. (40) contains no corridor adherence term; the paper assumes the learned behavior keeps eVTOLs inside the corridor.
  • domain assumption The DT synchronization overhead is ignored in the rate comparison
    Higher synchronization frequency improves trajectory but also consumes air-ground resources; the paper does not model this cost.

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Cite this review

Pith. "Pith review of Digital Twin-based SIM Communication and Flight Control for Advanced Air Mobility." pith.science (2026). https://pith.science/paper/FNINERYV

@misc{pith2026250101837,
  author       = {Pith},
  title        = {Pith review of: Digital Twin-based SIM Communication and Flight Control for Advanced Air Mobility},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FNINERYV}},
  note         = {Machine review of arXiv:2501.01837}
}
read the original abstract

Electric Vertical Take-off and Landing vehicles (eVTOLs) are driving Advanced Air Mobility (AAM) toward transforming urban transportation by extending travel from congested ground networks to low-altitude airspace. This transition promises to reduce traffic congestion and significantly shorten commute times. To ensure aviation safety, eVTOLs must fly within prescribed flight corridors. These corridors are managed by ground-based Air Traffic Control (ATCo) stations, which oversee air-ground communication and flight scheduling. However, one critical challenge remains: the lack of high rate air-ground communication and safe flight planning within these corridors. The introduction of 6G-oriented Stacked Intelligent Metasurface (SIM) technology presents a high rate communication solution. With advanced phase-shifting capabilities, SIM enables precise wireless signal control and supports beam-tracking communication with eVTOLs. Leveraging this technology, we propose a Composite Potential Field (CPF) approach. This method dynamically integrates target, separation, and communication fields to optimize both SIM communication efficiency and flight safety. Simulation results validate the effectiveness of this DT-based approach. Compared to the potential field flight control benchmark, it improves the transmission rate by 8.3\%. Additionally, it reduces flight distance deviation from the prescribed corridor by 10\% compared to predetermined optimization methods.

Figures

Figures reproduced from arXiv: 2501.01837 by the authors.

Figure 1
Figure 1. DT-based SIM communication and eVTOL flight Optimization. these issues by optimizing eVTOL flight planning and user association to maximize throughput. Zeng et al. [13] pro￾posed circular eVTOL trajectories to improve energy effi￾ciency by accounting for both propulsion energy consumption and throughput. However, these studies largely ignore the constraints of prescribed air corridors mandated by aviation authoritie… view at source ↗
Figure 2
Figure 2. Flowchart for joint optimization of communication and flight in DT synchronization. As illustrated in [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. The illustration of the proposed DQN structure where y[n] is the target Q-value. a ′ denotes the action that maximizes the Q-value at the next state s[n+ 1]. The primary Q-network is trained using the following loss function, i.e., the mean squared error between the predicted and target Q-values: L(θ) = E(s[n],a[n],r[n],s[n+1])∼D h (y[n] − Qprim(s[n], a[n]; Υ))2 i , (43) where D is the experience replay buffer. whic… view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Transmission rate comparison of different SIM communication optimizations [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]
Figure 5
Figure 5. Figure 5: Mean distance deviation of the eVTOL with the proposed DQN-based flight control As shown in [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 6
Figure 6. Figure 6: compares the transmission rates over time for three eVTOLs, using SIM-based and MIMO-based ATCo stations. The SIM-based station includes five cases with varying config￾urations. For the MIMO-based ATCo station, the transmission capacity is given by: X N n=1 X M m=1 log…
Figure 7
Figure 7. Figure 7: Transmission rate v.s. different SIM configurations [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]
Figure 8
Figure 8. Figure 8: DQN-based CPF method can maintain the eVTOL trajectory in the corridor Different flight control schemes 0 0.5 1 1.5 2 2.5 3 3.5 4 Total Transmission Rate Without DT Y 3.5657 DT Scheme Y 3.5881 Without Comm Y 3.3874 (a) Transmission rate compar￾ison (b) DT-based eVTOL T…
Figure 10
Figure 10. Figure 10: Different flight control schemes with 5 obstacles ri = α1vtar(1 + α2di,tar) + P j̸=i, di,j<dsep  βvsep di,j  . Fig. 9a shows that the proposed DT scheme achieves the highest transmission rate among the three approaches. Conversely, the scheme without communication o…
Figure 9
Figure 9. Figure 9: Transmission rate with different flight control schemes [PITH_FULL_IMAGE:figures/full_fig_p013_9.png]
Figure 11
Figure 11. Figure 11: Impact of DT synchronization in a corridor with three obstacles gap between the DT-based scheme and the predetermined scheme widens. This is because DT synchronization can revise deviations caused by obstacles. As the number of obstacles increases, the frequency of tr…

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Stacked Intelligent Metasurfaces-Aided eVTOL Delay Sensitive Communications

    cs.NI 2025-07 conditional novelty 4.0 of 10

    For SIM-aided eVTOL air-to-air links, the paper derives a probabilistic end-to-end delay bound via stochastic network calculus, and a BCD-SDR algorithm that raises simulated transmission rate by 51.47% over an AO baseline.

Reference graph

Works this paper leans on

31 extracted references · 26 canonical work pages · cited by 1 Pith paper

  1. [21]

    A digital-twin-empowered lightweight model-sharing scheme for multirobot systems,

    K. Xiong, Z. Wang, S. Leng, and J. He, “A digital-twin-empowered lightweight model-sharing scheme for multirobot systems,” IEEE Inter- net of Things Journal , vol. 10, no. 19, pp. 17 231–17 242, 2023

    work page 2023

  2. [1]

    Advanced air mobility (aam) implementation plan,

    “Advanced air mobility (aam) implementation plan,” Airport noise report, vol. 35, no. 35 App., pp. 1–40, 2023

    work page 2023

  3. [2]

    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

    work page 2021

  4. [3]

    Optimization-based autonomous air traffic control for airspace capacity improvement,

    B. Baspinar, H. Balakrishnan, and E. Koyuncu, “Optimization-based autonomous air traffic control for airspace capacity improvement,” IEEE Transactions on Aerospace and Electronic Systems , vol. 56, no. 6, pp. 4814–4830, 2020

    work page 2020

  5. [4]

    Advanced Air Mobility (AAM) Im- plementation Plan ,

    Federal Aviation Administration , “Advanced Air Mobility (AAM) Im- plementation Plan ,” 2023, https://www.faa.gov/sites/faa.gov/files/AAM- I28-Implementation-Plan.pdf, Last accessed on 2024-11-25

    work page 2023

  6. [5]

    Holographic mimo surfaces for 6g wireless networks: Opportunities, challenges, and trends,

    C. Huang, S. Hu, G. C. Alexandropoulos, A. Zappone, C. Yuen, R. Zhang, M. D. Renzo, and M. Debbah, “Holographic mimo surfaces for 6g wireless networks: Opportunities, challenges, and trends,” IEEE Wireless Communications, vol. 27, no. 5, pp. 118–125, 2020

    work page 2020

  7. [6]

    Stacked intelligent metasurface enabled leo satellite communications relying on statistical csi,

    S. Lin, J. An, L. Gan, M. Debbah, and C. Yuen, “Stacked intelligent metasurface enabled leo satellite communications relying on statistical csi,” IEEE Wireless Communications Letters , vol. 13, no. 5, pp. 1295– 1299, 2024

    2024

  8. [7]

    R. D. Windhorst, T. A. Lauderdale, A. V . Sadovsky, J. Phillips, and Y .-C. Chu, Strategic and Tactical Functions in an Autonomous Air Traffic Management System . [Online]. Available: https://arc.aiaa.org/doi/abs/10.2514/6.2021-2355

    work page doi:10.2514/6.2021-2355

Show all 31 references
  1. [8]

    Cooperative obstacle avoidance using bidirectional artificial potential fields,

    D. McIntyre, W. Naeem, and X. Xu, “Cooperative obstacle avoidance using bidirectional artificial potential fields,” in 2016 UKACC 11th International Conference on Control (CONTROL) , 2016, pp. 1–6

    2016

  2. [9]

    Collision-free path planning for uavs using efficient artificial potential field algorithm,

    P. K. Selvam, G. Raja et al. , “Collision-free path planning for uavs using efficient artificial potential field algorithm,” in 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), 2021, pp. 1–5

    2021

  3. [10]

    Digital twin: Mitigating unpredictable, unde- sirable emergent behavior in complex systems,

    M. Grieves and J. Vickers, “Digital twin: Mitigating unpredictable, unde- sirable emergent behavior in complex systems,” Springer International Publishing, 2017

    2017

  4. [11]

    Secure uav communication networks over 5g,

    B. Li, Z. Fei, Y . Zhang, and M. Guizani, “Secure uav communication networks over 5g,” IEEE Wireless Communications , vol. 26, no. 5, pp. 114–120, 2019

    2019

  5. [12]

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

    Q. Wu, Y . Zeng, and R. Zhang, “Joint trajectory and communication design for multi-uav enabled wireless networks,” IEEE Transactions on Wireless Communications, vol. 17, no. 3, pp. 2109–2121, 2018

    2018

  6. [13]

    Energy-efficient uav communication with tra- jectory optimization,

    Y . Zeng and R. Zhang, “Energy-efficient uav communication with tra- jectory optimization,” IEEE Transactions on Wireless Communications , vol. 16, no. 6, pp. 3747–3760, 2017

    2017

  7. [14]

    Multi-hop ris-empowered terahertz commu- nications: A drl-based hybrid beamforming design,

    C. Huang, Z. Yang, G. C. Alexandropoulos, K. Xiong, L. Wei, C. Yuen, Z. Zhang, and M. Debbah, “Multi-hop ris-empowered terahertz commu- nications: A drl-based hybrid beamforming design,” IEEE Journal on Selected Areas in Communications, vol. 39, no. 6, pp. 1663–1677, 2021

    2021

  8. [15]

    Beamforming optimization for wireless network aided by intelligent reflecting surface with discrete phase shifts,

    Q. Wu and R. Zhang, “Beamforming optimization for wireless network aided by intelligent reflecting surface with discrete phase shifts,” IEEE Transactions on Communications, vol. 68, no. 3, pp. 1838–1851, 2020. 15

    2020

  9. [16]

    Stacked intelligent metasurfaces for multiuser beamforming in the wave domain,

    J. An, M. Di Renzo, M. Debbah, and C. Yuen, “Stacked intelligent metasurfaces for multiuser beamforming in the wave domain,” in ICC 2023 - IEEE International Conference on Communications , 2023, pp. 2834–2839

    2023

  10. [17]

    A survey on digital twin: Definitions, characteristics, applications, and design implications,

    B. R. Barricelli and E. Casiraghi, “A survey on digital twin: Definitions, characteristics, applications, and design implications,” IEEE Access, pp. 167 653–167 671, 2019

    2019

  11. [18]

    A survey on the necessity for a digital twin of production in the automotive industry,

    F. Biesinger, B. Kraß, and M. Weyrich, “A survey on the necessity for a digital twin of production in the automotive industry,” in 2019 23rd International Conference on Mechatronics Technology (ICMT) , 2019, pp. 1–8

    2019

  12. [19]

    Automatic flight plan creation tool with digital twin for monitoring forest fire on mountain trail through uav,

    C.-H. Bae, J.-W. Jeong, and S. Lee, “Automatic flight plan creation tool with digital twin for monitoring forest fire on mountain trail through uav,” in ISE@APSEC, 2022

    2022

  13. [20]

    Multi-uav cooperative search based on reinforcement learning with a digital twin driven training framework,

    G. Shen, L. Lei, X. Zhang, Z. Li, S. Cai, and L. Zhang, “Multi-uav cooperative search based on reinforcement learning with a digital twin driven training framework,”IEEE Transactions on Vehicular Technology, pp. 8354–8368, 2023

    2023

  14. [22]

    Beamforming optimization for wireless network aided by intelligent reflecting surface with discrete phase shifts,

    Q. Wu and R. Zhang, “Beamforming optimization for wireless network aided by intelligent reflecting surface with discrete phase shifts,” IEEE Transactions on Communications, vol. 68, no. 3, pp. 1838–1851, 2020

    2020

  15. [23]

    A programmable diffractive deep neural network based on a digital-coding metasurface array,

    C. Liu, Q. Ma, Z. Luo, Q. Hong, Q. Xiao, H. C. Zhang, L. Miao, W. Yu, Q. Cheng, and L. Li, “A programmable diffractive deep neural network based on a digital-coding metasurface array,” Nature Electronics, vol. 5, pp. 113–122, 2022

    2022

  16. [24]

    Stacked intelligent metasurfaces for efficient holographic mimo communications in 6g,

    J. An, C. Xu, D. W. K. Ng, G. C. Alexandropoulos, C. Huang, C. Yuen, and L. Hanzo, “Stacked intelligent metasurfaces for efficient holographic mimo communications in 6g,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 8, pp. 2380–2396, 2023

    2023

  17. [25]

    All-optical machine learning using diffractive deep neural networks,

    X. Lin, Y . Rivenson, N. T. Yardimci, M. Veli, Y . Luo, M. Jarrahi, and A. Ozcan, “All-optical machine learning using diffractive deep neural networks,” Science, vol. 361, pp. 1004–1008, 2018

    2018

  18. [26]

    Block coordinate descent algorithms for auxiliary-function-based independent vector extraction,

    R. Ikeshita, T. Nakatani, and S. Araki, “Block coordinate descent algorithms for auxiliary-function-based independent vector extraction,” IEEE Transactions on Signal Processing , 2021

    2021

  19. [27]

    Fractional programming for communication systems,

    K. Shen and W. Yu, “Fractional programming for communication systems,” IEEE Transactions on Signal Processing , pp. 1–1, 2018

    2018

  20. [28]

    Kkt conditions, first-order and second-order optimization, and distributed optimization: Tutorial and survey,

    B. Ghojogh, A. Ghodsi, F. Karray, and M. Crowley, “Kkt conditions, first-order and second-order optimization, and distributed optimization: Tutorial and survey,” 2021. [Online]. Available: https://arxiv.org/abs/2110.01858

    2021 arXiv

  21. [29]

    Intelligent reflecting surface-assisted mu-miso systems with imperfect hardware: Channel estimation, beamforming design,

    A. Papazafeiropoulos, C. Pan, P. Kourtessis, S. Chatzinotas, and J. M. Senior, “Intelligent reflecting surface-assisted mu-miso systems with imperfect hardware: Channel estimation, beamforming design,” 2021

    2021

  22. [30]

    Multi uav cluster control method based on virtual core in improved artificial potential field,

    E. Wu, Y . Sun et al., “Multi uav cluster control method based on virtual core in improved artificial potential field,” IEEE Access , vol. 8, pp. 131 647–131 661, 2020

    2020

  23. [31]

    UA V- Assisted Wireless Cooperative Communication and Coded Caching: A Multiagent Two-Timescale DRL Approach,

    B. Tian, L. Wang, L. Xu, W. Pan, H. Wu, L. Li, and Z. Han, “UA V- Assisted Wireless Cooperative Communication and Coded Caching: A Multiagent Two-Timescale DRL Approach,” IEEE Transactions on Mobile Computing, vol. 23, no. 5, pp. 4389–4404, 2024

    2024

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.