Movable Antenna-Equipped UAV for Data Collection in Backscatter Sensor Networks: A Deep Reinforcement Learning-based Approach

Boxuan Xie; Riku Jantti; Ruifan Zhu; Yu Bai; Zheng Chang

REVIEW 3 major objections 4 minor 4 cited by

Movable Antenna-Equipped UAV for Data Collection in Backscatter Sensor Networks: A Deep Reinforcement Learning-based Approach

T0 review · 3 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A movable antenna on a UAV cuts backscatter data-collection time and energy in simulations.

desk verdict A competent SAC/UAV data-collection study whose 'movable antenna' advantage rests on an unmodeled, confounded 10 dBi vs 5 dBi gain gap. read the letter →

arxiv 2411.13970 v1 pith:JETNYF6J submitted 2024-11-21 eess.SP cs.LG

Yu Bai , Boxuan Xie , Ruifan Zhu , Zheng Chang , Riku Jantti This is my paper · ORCID

classification eess.SPcs.LG

keywords movableantennabackscattercommunicationUAVtrajectoryoptimizationdeepreinforcementlearningSoftActor-Criticdatacollectiontimewirelesssensornetworks

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 tries to show that a data-collecting drone becomes faster and more energy-efficient when its antenna can be steered. Backscatter sensors communicate by reflecting a reader's radio signal, so the link works best when the drone's directional antenna points its main lobe straight at each sensor; the authors model this with elevation and azimuth angles that are computed to aim at each device. They formulate the mission as minimizing the sum of flight time, communication time, and antenna reorientation time, and solve the joint trajectory-and-pointing problem with a Soft Actor-Critic (SAC) reinforcement learning agent. In simulation with 20 sensors in a 200 m area, the steerable-antenna drone with SAC finishes in 49 seconds and flies 365 meters, versus 63.7 seconds and 447.9 meters for the same antenna with an actor-critic baseline, and the fixed-antenna drone must nearly visit each sensor. If true, this means steerable antennas, not just smarter flight paths, are a practical lever for backscatter sensor networks.

What carries the argument

The load-bearing mechanism is the movable antenna (MA), a directional antenna whose main-lobe orientation $(\theta,\phi)$ is changed in elevation and azimuth, with $\theta_k$ and $\varphi_k$ computed from the drone's position and each sensor's location via (1)-(2). This steering raises the effective channel gain in the LoS/NLoS path-loss model, allowing data collection from longer range, and the reorientation cost appears both in the objective through $t_{MA}$ and in the reward through $p_{MA}$, so the agent explicitly trades hover time against turn time. The second mechanism is Soft Actor-Critic (SAC), an off-policy maximum-entropy reinforcement learning algorithm with twin Q-networks and an adaptive entropy coefficient $\alpha$, which provides stable learning over the action space $\{a_f, a_r, \theta_{init}, \varphi_{init}\}$ that jointly controls the drone's movement and the antenna's initial pointing.

What would settle it

Run the same SAC-based simulation with the movable antenna's peak gain set equal to the fixed antenna's gain (for example, both at 5 dBi, or both at 10 dBi with the fixed antenna also directional and aimed at each sensor): if total data-collection time does not decrease when only steering freedom is added, the paper's central claim is refuted.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central discovery is that the antenna's reconfigurable orientation is what makes efficient data collection possible: the movable antenna's elevation angle $\theta_k$ and azimuth angle $\varphi_k$ are set by equations (1)-(2) to point directly at each backscatter device, increasing the reader antenna gain in the path-loss model and hence the data rate $R_k(t)=B\log_2(1+\xi_k P_c L_k(t)^{-2}/N_0)$. With that gain, the drone can hover at one position and rotate the antenna among all sensors whose received powers pass the sensitivity thresholds, collecting their data sequentially, so the trajectory problem changes from 'fly to each sensor' to 'choose hover points and initial antenna orientations.' The SAC-based policy learns this behavior, and the comparison to a fixed-position omnidirectional antenna (5 dBi) and to an actor-critic baseline is the evidence offered for the discovery. The claim is empirical rather than analytical: no performance guarantee is proven, but the simulated mission shows the mechanism working.

Load-bearing premise

The load-bearing premise is that the movable antenna's directional gain, not just its freedom to reorient, produces the improvement: the baseline fixed antenna is a 5 dBi omnidirectional antenna while the movable antenna's peak gain is left unspecified, so if both antennas had equal peak gain, the reported time and energy savings could disappear.

Editorial extensions

If this is right

If the paper is right, a single drone can serve more backscatter sensors per mission because rotating a steerable antenna replaces multiple close fly-bys.
Mission time becomes less sensitive to the number of sensors and the size of the area, since the flight component is the one that shrinks.
Energy consumption falls with flight time, and the modeled antenna reorientation energy $P_{MA}$ is small enough that the net budget still improves.
The SAC training recipe is practical: it converges in roughly 3 million steps, while the actor-critic baseline needs about 10 million and fails to converge for fixed antennas.

Reading between the lines

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

A testable implication the authors leave implicit is that the advantage may come from higher directional gain rather than from steerability itself: the fixed antenna is set to 5 dBi omnidirectional while the movable antenna's peak gain is never stated, so a matched-gain comparison is the clean way to isolate the steering benefit.
The same SAC-based positioning-and-pointing policy should transfer to any steerable antenna hardware, such as a gimballed dish or phased array, which would make the contribution a control method rather than a new antenna result.
Extending the state to include channel-state information or other drones' positions would let the approach handle multi-drone missions, where pointing decisions also manage co-channel interference.
A real-world test with backscatter tags would be needed to confirm whether the probabilistic LoS channel model overstates the gain in dense urban environments; without it, the 49-second result is a simulation benchmark rather than a measured one.

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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

Colleague,

Two things you should know before reading: (1) the DRL/SAC part is textbook-solid and the system model is clearly explained; (2) the advertised "movable antenna" benefit is not actually modeled. In the channel model, Section II-B, the reader antenna gain appears as a fixed scalar GTR (Table I: 10 dBi). It never depends on the orientation angles (θ,φ) that equations (1)-(2) are supposed to aim. Those angles only show up later in the mechanical reorientation time/power model, Eqs. (13)-(14). So Rk(t) is the same whether the main lobe points at a BD or away. The FPA baseline, by contrast, uses an omni antenna at 5 dBi. The comparison therefore tests a 10 dBi link against a 5 dBi link while attributing the difference to movability. That is a load-bearing confound.

What is genuinely there: a clean monostatic backscatter setup with UAV hovering, a legitimate simplification of the state space to azimuth+distance, and a correct SAC implementation with sensible reward shaping. For the niche of UAV-assisted backscatter collection, this is a reasonable design study. The authors are also transparent about the AC-FPA convergence failure, which is honest, though it weakens the comparison.

Soft spots beyond the gain confound: no error bars or statistical analysis across random BD placements, no MA gain value in Table I, no code/data, and the excluded AC-FPA baseline leaves only favorable comparisons. All of these are fixable. The paper would be more defensible reframed as "a high-gain directional antenna on a UAV," or the antenna pattern must be included and gains matched.

My take: the core idea is worth a referee's time only if the authors fix the antenna modeling and rerun the experiments. As-is, I would not cite the headline result, and peer review should demand a revision before acceptance.

Referee Report

3 major / 4 minor

Summary. The paper proposes a deep reinforcement learning (SAC) approach for a UAV equipped with a movable directional antenna (MA) collecting data from backscatter devices (BDs). The system model includes a probability-based LoS/NLoS path loss model, a backscatter data rate expression, a UAV propulsion and MA reorientation energy/time model, and an MDP formulation whose actions are the UAV's movement and the MA's initial orientation. Simulations compare the MA-equipped UAV using SAC against an FPA-equipped UAV and an AC baseline, reporting reductions in data collection time and energy. The central advertised mechanism is that steering the MA's main lobe toward each BD increases channel gain, thereby reducing collection time.

Significance. If the central mechanism were correctly modeled and supported, the paper would address a timely combination of movable antennas, UAV data collection, and backscatter communications, and the SAC-based formulation could be a useful baseline for future work. The paper also contributes a reasonable MDP design with a compact state space based on azimuth angles and distances. However, the manuscript as written does not establish the claimed benefit. The antenna orientation never enters the channel model, so the simulations do not actually test 'movability'; the reported gain may simply reflect a fixed 5 dB gain difference between the MA and FPA. In addition, the path loss model has a dimensional inconsistency that affects all numerical results. The paper is therefore a promising idea whose current evidence is not yet convincing.

major comments (3)

[Section II-B, Eqs. (3)-(5) and Section II-C, Eq. (10)] The MA's orientation angles (θ,φ) are defined in Eqs. (1)-(2) to point the main lobe at a BD, but the link budget in Eqs. (4)-(5) uses a fixed scalar gain GTR=10 dBi (Table I) and contains no antenna pattern or dependence on (θ,φ). Consequently, the data rate Rk(t) in Eq. (10) is identical for a correctly aimed and a misaimed antenna. The only place the angles matter is the mechanical reorientation time in Eq. (14). Since the FPA baseline is described as a 5 dBi omni-directional antenna while GTR=10 dBi, the simulation compares a 10 dBi link against a 5 dBi link, not a steerable antenna against a fixed one. This confound is load-bearing because the abstract and introduction attribute the improvement to 'precisely aiming its main lobe at each BD.' The authors must introduce a gain model that depends on the pointing error (e.g., a main-lobe pattern G(θ,φ) with a specified peak gain and beamwidth) and then compare the MA against an FPA with the same peak gain, or otherwise explicitly separate the gain advantage from the steering advantage.
[Eqs. (3)-(5) and (10)] The path loss quantities LLoS,k(t) and LNLoS,k(t) in Eqs. (4)-(5) are expressed in dB (20log10(...) plus losses in dB), and Lk(t) in Eq. (3) is a weighted sum of these dB values. However, Eq. (10) then uses Lk(t)^{-2} as if Lk(t) were a linear (dimensionless) channel power gain. Weighted averaging of dB losses is also physically invalid; the correct procedure is to average the linear losses and then convert to dB, or to compute the effective path loss in linear domain first. This inconsistency affects every numerical result in Section IV and the rate computation, so the quantitative claims about time and energy reductions are not trustworthy without correction.
[Section IV, Figs. 3-5] The simulation evidence lacks statistical support. The BDs are randomly distributed, but no error bars, confidence intervals, or multiple-seed results are reported for the time and energy comparisons in Figs. 3 and 4. The AC-FPA baseline is explicitly excluded from these figures 'due to convergence challenges,' which removes the control condition needed to show that SAC, rather than the antenna gain difference, drives the improvement. The paper should provide results averaged over many random BD deployments (with standard deviations), include the AC-FPA result where possible (or explain the failure and provide a converged FPA baseline trained with SAC), and specify the MA's peak gain and directivity so the reader can separate the antenna gain contribution from the steering contribution.

minor comments (4)

[Throughout] The paper contains several typographical and notation issues, such as the inconsistent spacing in 'UA V' (which should be 'UAV' or consistently 'UA V'), the unnumbered 'P1' in Eq. (15), and the use of 'θ' in Eq. (7) while the text refers to elevation angle without defining which angle is used in the LoS probability.
[Section III-B] The action space includes θinit(t) and φinit(t), but since the MA is always reoriented to point at each qualified BD during data collection, the initial orientation only affects the mechanical adjustment time. With no antenna pattern in the channel model, this part of the action space reduces to a time-penalty optimization; the authors should clarify what physical benefit is being optimized beyond reducing reorientation time.
[Section III-B3] The reward components pf(t), pMA(t), and pc(t) are described only qualitatively. Explicit formulas for these penalties and a discussion of how their scaling relates to the hand-tuned coefficients rbs=50 and rf=500 would improve reproducibility and help readers assess whether the learned policy is actually minimizing the true objective in (15).
[Section IV, Fig. 2] The training details are insufficient for reproduction: no neural network architectures, learning rates, batch sizes, replay buffer sizes, or entropy target are reported, and the converged SAC curves in Fig. 2 are not quantified with run-to-run variance. Please provide a hyperparameter table or a reference to a public implementation.

Circularity Check

1 steps flagged · score 6.0 of 10

MA-vs-FPA advantage reduces to a fixed 10-vs-5 dBi gain gap because MA orientation never enters the channel model.

self definitional [Section II-B, Eqs. (4)-(5); Section IV, Table I and baseline description]
"The commonly used omni-directional antenna gain of the FPA is set to 5 dBi. | GTR 10 Reader antenna gain [dBi]"

Eqs. (4)-(5) write the backscatter link path loss using a fixed reader gain GTR and BD gain GBD,k. Nowhere do the MA main-lobe angles (theta, phi) from Eqs. (1)-(2) appear in the path loss, the received signal model (8), or the rate R_k(t) in Eq. (10); the angles affect only the reorientation time tMA in Eq. (14) and the MA power in Eq. (13). Thus a correctly aimed and a misaimed antenna produce identical communication rates. The FPA baseline is assigned 5 dBi while Table I sets GTR=10 dBi for the reader, so the simulated superiority of the 'MA-equipped UAV' is exactly the 10-vs-5 dBi gain gap inserted as an input, not a consequence of steering the main lobe.

full rationale

The derivation chain for the SAC-based controller is not circular: the reward is the optimization objective, not a fitted parameter, and the SAC-vs-AC comparisons are emergent simulation outcomes with independent content. The self-citation [12] is a general survey reference and is not load-bearing. However, the paper's headline prediction that the MA-equipped UAV outperforms the FPA-equipped UAV is forced by the simulation inputs. In Eqs. (4)-(5) the reader gain GTR appears as a fixed scalar; the main-lobe angles (theta, phi) from Eqs. (1)-(2) never enter the path-loss or rate expression, so the MA's ability to aim has no effect on R_k(t). The only link-budget difference between the two simulated systems is the 10 dBi reader gain assigned to the proposed setup versus the 5 dBi omni gain assigned to the FPA baseline. Consequently, the reported time/energy reduction is equivalent, by construction, to the chosen gain gap rather than to the advertised movability mechanism. This is a partial circularity: one of the two central claims reduces to its input parameter, while the SAC contribution itself is not circular.

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

The paper rests on standard channel and backscatter models, but key parameters such as reward weights and MA power constants are hand-picked, and several DRL hyperparameters are omitted. No new physical entities are introduced.

free parameters (4)

Reward coefficients rbs, rf = rbs=50, rf=500
Hand-picked reward weights in Eq. 19 and Table I that shape the trade-off between collection reward and time penalties; no sensitivity analysis is provided.
MA power model constants Pbase, zeta, kappa = Pbase=2 W, zeta=0.05, kappa=0.03
Ad hoc constants in Eq. 13 for power per unit angular movement; chosen without empirical basis.
LoS model environment constants = rho=9.61, beta=0.16, eta_LoS=1, eta_NLoS=20
Standard urban parameters from the Al-Hourani model, but the choice of urban scenario is arbitrary and not varied.
DRL hyperparameters
Learning rates, network sizes, batch size, and replay buffer size are not reported; training is under-specified without them.

assumptions (4)

domain assumption UAV-BD communication follows the probabilistic LoS/NLoS path loss model (Eqs. 3-7) with environment constants from Al-Hourani.
The entire data rate calculation depends on this channel model, whose accuracy is not validated for the simulated scenario.
domain assumption Monostatic backscatter with hf = hb (Eq. 8 and Section II-C).
Assumes forward and backscatter channels are equal, giving the |h|^4 term in Eq. 9; requires the UAV to remain stationary during collection.
domain assumption Backscatter efficiency xi_k follows the Griffin-Durgin link budget (Eq. 8).
Standard RFID link budget model; parameter values are assumed in Table I.
domain assumption UAV propulsion power follows the rotorcraft model (Eqs. 11-12).
Standard model from UAV literature, not validated in the paper.

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Cite this review

Pith. "Pith review of Movable Antenna-Equipped UAV for Data Collection in Backscatter Sensor Networks: A Deep Reinforcement Learning-based Approach." pith.science (2026). https://pith.science/paper/JETNYF6J

@misc{pith2026241113970,
  author       = {Pith},
  title        = {Pith review of: Movable Antenna-Equipped UAV for Data Collection in Backscatter Sensor Networks: A Deep Reinforcement Learning-based Approach},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JETNYF6J}},
  note         = {Machine review of arXiv:2411.13970}
}

read the original abstract

Backscatter communication (BC) becomes a promising energy-efficient solution for future wireless sensor networks (WSNs). Unmanned aerial vehicles (UAVs) enable flexible data collection from remote backscatter devices (BDs), yet conventional UAVs rely on omni-directional fixed-position antennas (FPAs), limiting channel gain and prolonging data collection time. To address this issue, we consider equipping a UAV with a directional movable antenna (MA) with high directivity and flexibility. The MA enhances channel gain by precisely aiming its main lobe at each BD, focusing transmission power for efficient communication. Our goal is to minimize the total data collection time by jointly optimizing the UAV's trajectory and the MA's orientation. We develop a deep reinforcement learning (DRL)-based strategy using the azimuth angle and distance between the UAV and each BD to simplify the agent's observation space. To ensure stability during training, we adopt Soft Actor-Critic (SAC) algorithm that balances exploration with reward maximization for efficient and reliable learning. Simulation results demonstrate that our proposed MA-equipped UAV with SAC outperforms both FPA-equipped UAVs and other RL methods, achieving significant reductions in both data collection time and energy consumption.

Figures

Figures reproduced from arXiv: 2411.13970 by the authors.

**Figure 2.** Training convergence of SAC and AC for MA and FPA. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Data collection time and energy consumption versus number of BDs. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Data collection time and energy consumption versus target area length. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

**Figure 5.** Figure 5: UAV trajectories and hover points for different strategies and antennas. [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

Discussion (0). Continue with ORCID to comment.

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Reference graph

Works this paper leans on

16 extracted references · 12 canonical work pages · cited by 4 Pith papers

[1]

Ambient backscatter communications for future ultra-low-power ma- chine type communications: Challenges, solutions, opportunities, and future research trends,

R. Duan, X. Wang, H. Yi ˘gitler, M. U. Sheikh, R. J ¨antti, and Z. Han, “Ambient backscatter communications for future ultra-low-power ma- chine type communications: Challenges, solutions, opportunities, and future research trends,” IEEE Commun. Mag. , vol. 58, no. 2, pp. 42–47, 2020

work page 2020
[2]

Backscatter wireless commu- nications and sensing in green Internet of Things,

U. S. Toro, K. Wu, and V . C. M. Leung, “Backscatter wireless commu- nications and sensing in green Internet of Things,” IEEE Trans. Green Commun. Netw., vol. 6, no. 1, pp. 37–55, 2022

work page 2022
[3]

Energy-efficient UA V backscatter communication with joint trajectory design and resource optimization,

G. Yang, R. Dai, and Y .-C. Liang, “Energy-efficient UA V backscatter communication with joint trajectory design and resource optimization,” IEEE Trans. Wirel. Commun. , vol. 20, no. 2, pp. 926–941, 2021

work page 2021
[4]

Drone interrogation (and its low-cost alternative) in backscatter environmental sensor networks,

E. Andrianakis, G. V ougioukas, E. Giannelos, O. Giannakopoulos, G. Apostolakis, K. Skyvalakis, and A. Bletsas, “Drone interrogation (and its low-cost alternative) in backscatter environmental sensor networks,” in 2021 SpliTech, 2021, pp. 1–6

work page 2021
[5]

Joint distributed beamforming and backscattering for UA V-assisted wpsns,

Z. Mao, F. Hu, W. Wu, H. Wu, and X. Shen, “Joint distributed beamforming and backscattering for UA V-assisted wpsns,” IEEE Trans. Wirel. Commun., vol. 22, no. 3, pp. 1510–1522, 2023

work page 2023
[6]

Energy-efficient covert communications for UA V-assisted backscatter systems,

Y . Zhou, A. Al-nahari, R. J ¨antti, Z. Ma, and P. Fan, “Energy-efficient covert communications for UA V-assisted backscatter systems,” IEEE Trans. V eh. Technol., vol. 73, no. 6, pp. 9147–9152, 2024

work page 2024
[7]

Hierarchical deep reinforcement learning for backscattering data collection with multiple UA Vs,

Y . Zhang, Z. Mou, F. Gao, L. Xing, J. Jiang, and Z. Han, “Hierarchical deep reinforcement learning for backscattering data collection with multiple UA Vs,”IEEE Internet Things J. , vol. 8, no. 5, pp. 3786–3800, 2021

work page 2021
[8]

Joint optimization on trajectory, altitude, velocity, and link scheduling for minimum mission time in UA V-aided data collection,

J. Li, H. Zhao, H. Wang, F. Gu, J. Wei, H. Yin, and B. Ren, “Joint optimization on trajectory, altitude, velocity, and link scheduling for minimum mission time in UA V-aided data collection,” IEEE Internet Things J., vol. 7, no. 2, pp. 1464–1475, 2020

2020

Show all 16 references

[9]

Movable antennas for wireless commu- nication: Opportunities and challenges,

L. Zhu, W. Ma, and R. Zhang, “Movable antennas for wireless commu- nication: Opportunities and challenges,” IEEE Commun. Mag. , vol. 62, no. 6, pp. 114–120, 2024

2024
[10]

UA V-mounted movable an- tenna: Joint optimization of UA V placement and antenna configuration,

X.-W. Tang, Y . Shi, Y . Huang, and Q. Wu, “UA V-mounted movable an- tenna: Joint optimization of UA V placement and antenna configuration,” arXiv preprint arXiv:2409.02469 , 2024

2024
[11]

Modeling and performance analysis for movable antenna enabled wireless communications,

L. Zhu, W. Ma, and R. Zhang, “Modeling and performance analysis for movable antenna enabled wireless communications,” IEEE Trans. Wirel. Commun., vol. 23, no. 6, pp. 6234–6250, 2024

2024
[12]

Towards autonomous multi-UA V wireless network: A survey of reinforcement learning-based approaches,

Y . Bai, H. Zhao, X. Zhang, Z. Chang, R. J ¨antti, and K. Yang, “Towards autonomous multi-UA V wireless network: A survey of reinforcement learning-based approaches,” IEEE Commun. Surv. Tutor ., 2023

2023
[13]

AoI-energy-aware uav- assisted data collection for IoT networks: A deep reinforcement learning method,

M. Sun, X. Xu, X. Qin, and P. Zhang, “AoI-energy-aware uav- assisted data collection for IoT networks: A deep reinforcement learning method,” IEEE Internet Things J. , vol. 8, no. 24, pp. 17 275–17 289, 2021

2021
[14]

Complete link budgets for backscatter- radio and RFID systems,

J. D. Griffin and G. D. Durgin, “Complete link budgets for backscatter- radio and RFID systems,” IEEE Antennas Propag. Mag. , vol. 51, no. 2, pp. 11–25, 2009

2009
[15]

Optimal lap altitude for maximum coverage,

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

2014
[16]

Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,

T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,” in 2018 ICML , pp. 1861–1870

2018

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [1]

Ambient backscatter communications for future ultra-low-power ma- chine type communications: Challenges, solutions, opportunities, and future research trends,

R. Duan, X. Wang, H. Yi ˘gitler, M. U. Sheikh, R. J ¨antti, and Z. Han, “Ambient backscatter communications for future ultra-low-power ma- chine type communications: Challenges, solutions, opportunities, and future research trends,” IEEE Commun. Mag. , vol. 58, no. 2, pp. 42–47, 2020

work page 2020

[2] [2]

Backscatter wireless commu- nications and sensing in green Internet of Things,

U. S. Toro, K. Wu, and V . C. M. Leung, “Backscatter wireless commu- nications and sensing in green Internet of Things,” IEEE Trans. Green Commun. Netw., vol. 6, no. 1, pp. 37–55, 2022

work page 2022

[3] [3]

Energy-efficient UA V backscatter communication with joint trajectory design and resource optimization,

G. Yang, R. Dai, and Y .-C. Liang, “Energy-efficient UA V backscatter communication with joint trajectory design and resource optimization,” IEEE Trans. Wirel. Commun. , vol. 20, no. 2, pp. 926–941, 2021

work page 2021

[4] [4]

Drone interrogation (and its low-cost alternative) in backscatter environmental sensor networks,

E. Andrianakis, G. V ougioukas, E. Giannelos, O. Giannakopoulos, G. Apostolakis, K. Skyvalakis, and A. Bletsas, “Drone interrogation (and its low-cost alternative) in backscatter environmental sensor networks,” in 2021 SpliTech, 2021, pp. 1–6

work page 2021

[5] [5]

Joint distributed beamforming and backscattering for UA V-assisted wpsns,

Z. Mao, F. Hu, W. Wu, H. Wu, and X. Shen, “Joint distributed beamforming and backscattering for UA V-assisted wpsns,” IEEE Trans. Wirel. Commun., vol. 22, no. 3, pp. 1510–1522, 2023

work page 2023

[6] [6]

Energy-efficient covert communications for UA V-assisted backscatter systems,

Y . Zhou, A. Al-nahari, R. J ¨antti, Z. Ma, and P. Fan, “Energy-efficient covert communications for UA V-assisted backscatter systems,” IEEE Trans. V eh. Technol., vol. 73, no. 6, pp. 9147–9152, 2024

work page 2024

[7] [7]

Hierarchical deep reinforcement learning for backscattering data collection with multiple UA Vs,

Y . Zhang, Z. Mou, F. Gao, L. Xing, J. Jiang, and Z. Han, “Hierarchical deep reinforcement learning for backscattering data collection with multiple UA Vs,”IEEE Internet Things J. , vol. 8, no. 5, pp. 3786–3800, 2021

work page 2021

[8] [8]

Joint optimization on trajectory, altitude, velocity, and link scheduling for minimum mission time in UA V-aided data collection,

J. Li, H. Zhao, H. Wang, F. Gu, J. Wei, H. Yin, and B. Ren, “Joint optimization on trajectory, altitude, velocity, and link scheduling for minimum mission time in UA V-aided data collection,” IEEE Internet Things J., vol. 7, no. 2, pp. 1464–1475, 2020

2020