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REVIEW 3 major objections 5 minor 36 references

Deep Recurrent Q-Learning Based Beam Steering Strategy for Throughput Maximization in WPCNs

T0 review · 3 major / 5 minor · reviewed 2026-08-01 · deepseek-v4-flash

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Pith's one-line read The paper claims that an energy transmitter can learn to maximize throughput in a battery-free network solely from coarse slot outcomes, reaching 75-80% of an all-knowing oracle's performance and up to 68% beyond non-learning baselines.

desk verdict Genuinely new WPCN MAC idea, but the P_th reference is undefined and the stated parameters contradict the reported simulations, so the headline numbers aren't reproducible. read the letter →

arxiv 2607.17838 v1 pith:H4BEMSHZ submitted 2026-07-20 eess.SP

Samannaya Adhikari , Navchetan Awasthi , Siddhartha Sarma This is my paper
classification eess.SP
keywords wirelesspoweredcommunicationnetworksenergybeamformingslottedALOHAdeeprecurrentQ-networkpartialobservabilitybattery-freedevicesmediumaccesscontrolthroughputmaximization
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

This paper aims to show that a wireless powered communication network can be scheduled without any feedback from its battery-free devices. The energy transmitter/access point steers a narrow energy beam across predefined directions, and by watching only whether each slot was idle, a success, or a collision, it learns which beam to use next. The authors model the problem as a partially observable Markov decision process and solve it with an action-specific deep recurrent Q-network whose hidden state accumulates the history of beams and outcomes. In simulation the learned policy raises throughput by up to 68% over round-robin and random beam selection and reaches 75-80% of an oracle policy that knows every device's charge and channel. If the approach transfers to hardware, it would make medium access control for energy-harvesting IoT networks nearly overhead-free.

What carries the argument

The central object is the ADRQN (action-specific deep recurrent Q-network), a recurrent Q-network whose input is the previous beam index and the current ternary slot outcome, each encoded as a one-hot vector and embedded, then processed by a multi-layer RNN or LSTM. The hidden state accumulates the interaction history and acts as a learned substitute for the unobservable joint charge state. The mechanism is completed by the power threshold P_th, which admits only devices whose received power exceeds it to contend; because the slot outcome is then a deterministic function of beam and state, the history of outcomes carries enough information for the agent to learn to steer.

What would settle it

A hardware experiment or simulation in which identical charge states and beam directions do not always produce the same slot outcome would falsify the deterministic-state premise and with it the claim that the ternary outcome stream alone is sufficient; for instance, replacing the deterministic P_th rule with probabilistic channel access should erode the reported throughput gains.

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Extended reading notes

Core claim

The paper's central claim is that beam direction can serve as a medium-access-control knob: by charging different spatial clusters at different rates, the energy transmitter indirectly controls which battery-free devices become eligible to transmit and when. Because a device transmits exactly when its stored charge reaches a threshold and the received power from the current beam exceeds P_th, the slot-level ternary observation is a deterministic function of the hidden joint charge state and the chosen beam. The authors therefore cast beam steering as a POMDP and use an ADRQN—an action-specific deep recurrent Q-network—to compress the history of beam choices and idle/success/collision outcome

Load-bearing premise

The whole learning signal rests on the assumption that a battery-free device transmits exactly when its capacitor is full and the beam's received power exceeds a threshold, so each slot outcome is a clean readout of charging state; probabilistic or imperfect device behavior would break this.

Editorial extensions

If this is right

  • Battery-free devices can stay fully passive: no channel estimation, charge reporting, or state tracking is needed, so harvested energy is spent on data rather than protocol overhead.
  • Beam direction becomes a scheduling knob: by choosing which spatial region charges fastest, the energy transmitter shapes when devices contend without any explicit reservation.
  • The idle/success/collision history is a sufficient signal for the transmitter to infer hidden charging dynamics, as shown by recurrent architectures outperforming fixed-window DQN and by throughput increasing with history length.
  • The power threshold P_th lets the network trade idle slots for collisions, with throughput peaking at an intermediate setting (P_th = -12 dB in the simulations).
  • The gap to the oracle (roughly 20-25% throughput) is the measurable price of partial observability under this protocol.

Reading between the lines

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

  • Editorial inference: if the deterministic transmission rule survives on real hardware, the same outcome-only feedback would likely generalize to multiple energy transmitters and heterogeneous capacitor sizes, since those changes would show up as different charging rhythms in the same ternary history.
  • Editorial inference: P_th is tuned manually in this paper; a natural extension would be to let the agent co-learn the threshold, turning the idle/collision trade-off into part of the policy rather than an external choice.
  • Editorial inference: the paper itself acknowledges that fairness is not explicitly guaranteed, which suggests that outcome-only beam steering may need an auxiliary signal or a modified objective before it can serve applications with age-of-information or per-device throughput constraints.
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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 / 5 minor

Summary. The paper considers a wireless powered communication network (WPCN) in which an energy transmitter/access point (ET/AP) steers a multi-antenna energy beam among predefined directions to regulate the charging of battery-free energy harvesting devices that transmit under slotted ALOHA. The authors formulate the joint beam-steering and random-access problem as a POMDP, and propose an Action-specific Deep Recurrent Q-Network (ADRQN) that learns the steering policy from only the ternary slot outcome (idle/success/collision), without channel estimation, charge-level reporting, or device-state tracking. A model-predictive oracle policy with full state knowledge is designed as a benchmark. Numerical results report up to 68% throughput improvement over round-robin and random-selection baselines, and 75-80% of the oracle throughput.

Significance. The proposed control loop—regulating access by steering the energy beam based only on slot outcomes—is an interesting and potentially low-overhead approach for WPCNs. The POMDP formulation, the recurrent architecture, and the comparison with an informed benchmark are appropriate first steps, and the paper addresses a real gap in the literature on MAC protocols for energy-harvesting networks. However, the quantitative claims are not currently reproducible: the power-threshold definition is inconsistent with the stated channel model and parameters, the statistical reliability of the simulation results is not documented, and the 'oracle' is a finite-horizon heuristic rather than a proven upper bound. If these issues are resolved, the work would be a useful contribution to the field.

major comments (3)
  1. [III-B/C/E, Table I, Figs. 4-5, 12] The reference for P_th is inconsistent. Eq. (4) defines P_{r,i}=P_T |g_i^H w(φ_s)|^2. With Table I (P_T=1 W, σ_l^2=10^-2, K=6 dB, M=5), the expected value for a beam-aligned device is E|g_i^H w|^2 = σ_l^2 (K M + 1)/(K+1) ≈ 0.042 W ≈ -13.8 dBW. This is below the stated P_th = -12 dB (≈0.063 W), so condition (8) would be met only by rare favorable fades. Yet Fig. 9 reports ~1330 non-idle slots at N=125 for M=5 and Fig. 12 locates the throughput peak at P_th=-12 dB. Figs. 4/5 instead draw the -12 dB line on a 'Normalized Power' array-factor plot, which is a different reference. The manuscript never defines the reference used in the simulations. Either Eq. (8)/Table I/Fig. 12 are evaluated with an absolute threshold, in which case the M=5 results are physically implausible, or the simulation used a normalized threshold, in which case the absolute received-power model of Section III is not wh
  2. [VI-A, Figs. 6, 9, 10] The number of independent device placements is not stated ('a few independent experiments'), and no error bars, confidence intervals, or per-placement results are reported. Fig. 8 shows that baseline throughput is strongly placement-dependent (e.g., RS gives 0.4992 in setup 1 but 0.2703 in setup 2), so the variance across placements is material. The central claim of a 68% improvement over round-robin/random at N=50 (Fig. 6) and the scaling curves in Figs. 9-10 need to be accompanied by a specification of the number of configurations and a measure of dispersion; otherwise the comparisons cannot be assessed statistically.
  3. [V, Eq. (32), VI-C] The oracle is not an upper bound. It selects actions via an exhaustive search over a finite horizon k=5 with heuristic tie-breaking (near-threshold parameter ξ), and no argument is given that this equals the optimal fully-observable policy. Calling it a 'performance ceiling' and interpreting the ADRQN-oracle gap as 'the cost of partial observability' is therefore overstated. A finite-horizon informed heuristic is a useful benchmark, but the paper should either demonstrate insensitivity to k and ξ (or provide a bound on the suboptimality), or soften the 'upper bound' language so that the 75-80% figure is understood as a fraction of a specific benchmark policy.
minor comments (5)
  1. [IV-C2, Eq. (25)] The state dimension is stated as H(S+4), but each history step comprises an S-dimensional one-hot action and a 3-dimensional one-hot observation; the dimension should be H(S+3) unless an additional field is included. Please correct the formula.
  2. [III-E] Calling the devices 'entirely passive' is imprecise: condition (8) requires each BEHD to measure instantaneous received power and compare it with P_th, in addition to charge-threshold detection. Clarify what hardware/energy cost this entails, or state explicitly that this is an idealized model.
  3. [VI-B, Figs. 4-5, Fig. 12] The normalization of the power axis in Figs. 4-5 is not defined, and Fig. 12 states 'P_th (in dB relative to P_T)'. These two conventions are inconsistent with the threshold in Table I. Unify the reference point throughout.
  4. [V-A] The tie-break parameter ξ=0.85 appears without explanation; give the intuition for the 'fewest devices with charge exceeding ξ·Q_th' criterion.
  5. [Overall] Releasing the simulation code and seeds would materially improve reproducibility of the numerical claims, especially since no code is currently available.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the central claims are empirical and the only tuned parameter (P_th) is disclosed as a design choice.

full rationale

The paper's core contribution is an empirical learning-based beam-steering policy evaluated in simulation against round-robin, random selection, and an oracle. The claimed throughput improvements are measured outcomes of a trained policy, not values derived from or equivalent to fitted parameters. The power threshold P_th is explicitly described as 'just a design parameter and tuned empirically' (Section III-E) and its effect is shown as a trade-off sweep in Fig. 12; this is standard hyperparameter selection, not a hidden fit renamed as a prediction. The self-citation [8] is used only to motivate the architectural change from a feedforward DQN to a recurrent model and is not load-bearing evidence for the main result. The deterministic mapping from state/beam to slot outcome is a modeling assumption of the system, not a circular derivation. The reported '75-80% of oracle' is a direct simulation comparison, and the oracle itself is a benchmark, not an input to training. The apparent inconsistency in the P_th reference (absolute dB vs normalized array-factor dB) is a correctness/reproducibility concern, not a circularity concern. No equation is shown to reduce to its own input by construction, and no fitted parameter is presented as an independent prediction. Therefore the derivation chain is self-contained in the sense relevant to circularity analysis.

Assumptions & free parameters 4 free parameters · 9 assumptions · 0 invented entities

The physical model borrows standard channel/EH/charging models from cited literature; the main hand-fitted degrees of freedom are P_th, beam directions, oracle horizon, and RL hyperparameters. The central claim does not introduce new physical entities.

free parameters (4)
  • P_th (received-power eligibility threshold) = -12 dB relative to P_T
    Described as a contention-control knob and 'tuned empirically' (Sec III-E); Fig 12 sweeps P_th and shows success ratio peaks at -12 dB, which is used in all main experiments. This is a hand-fitted environment parameter, not learned by the agent.
  • Beam direction set Φ = M=5: {60°,170°,330°}; M=8: {30°,60°,180°,300°,330°}
    Chosen via the array-factor coverage criterion in Sec VI-B to cover 360° with minimal overlap; affects spatial scheduling and results, and is not derived from the optimization problem.
  • Oracle planning horizon k and tie-break parameter ξ = k=5, ξ=0.85
    Set in Sec VI-A for the finite-horizon oracle search; the '75-80% of oracle' figure depends on these choices.
  • RL discount factor γ = 0.95
    Listed in Table I; chosen without sensitivity analysis; balances short- vs long-term reward surrogate.
assumptions (9)
  • domain assumption Rician fading channel model with K = 6 dB and path loss σ_l^2 = 10^-2 (Eq. 2-3).
    Imported from [31]; not validated against measurement in this paper.
  • domain assumption Logistic nonlinear energy harvesting function with Γ=0.024 W, α=150, β=0.014 (Eq. 5).
    Taken from [32]; the nonlinear saturation behavior is load-bearing for charge dynamics.
  • domain assumption Capacitor charging model Eq. (6)-(7) with R=100 Ω, C=1 mF.
    Taken from [33], a self-cited paper by one of the authors; no external replication is provided.
  • domain assumption All BEHDs are at approximately equal distance from the ET/AP; locations are unknown to the ET/AP.
    Justified in Section III as typical single-floor IoT; removes distance-dependent path loss variation.
  • domain assumption Out-of-band WET/WIT architecture with separate frequency bands.
    Standard in WPCN literature [14],[30]; sidesteps self-interference in the model.
  • domain assumption Block fading with coherence time T_c=1 s and slot δt=0.1 s, so channel is constant over 10 slots.
    Defines the temporal horizon over which the deterministic-outcome mapping holds.
  • domain assumption Device transmission is deterministic: a BEHD transmits iff Q_i ≥ Q_th and P_r,i > P_th (Eq. 8).
    The central observable-signal assumption; if real hardware deviates, the RL agent's memory cannot infer charging state cleanly.
  • domain assumption Slotted ALOHA: any eligible device transmits; slot outcome is idle/success/collision by count of eligible devices.
    Defines the ternary observation space; no backoff or probabilistic access is modeled.
  • ad hoc to paper The oracle assumes channel realizations are constant over its k=5 planning horizon within a coherence block.
    Simplifies the exhaustive search; not guaranteed to hold across coherence-block boundaries, so the oracle may over- or under-estimate the true upper bound.

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Cite this review

Pith. "Pith review of Deep Recurrent Q-Learning Based Beam Steering Strategy for Throughput Maximization in WPCNs." pith.science (2026). https://pith.science/paper/H4BEMSHZ

@misc{pith2026260717838,
  author       = {Pith},
  title        = {Pith review of: Deep Recurrent Q-Learning Based Beam Steering Strategy for Throughput Maximization in WPCNs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/H4BEMSHZ}},
  note         = {Machine review of arXiv:2607.17838}
}
read the original abstract

In wireless powered communication networks, medium access control protocols for devices using the harvest-then-transmit strategy must be distributed, low-overhead, and capable of handling irregular and infrequent data transmissions to ensure efficient energy utilisation. However, most existing protocols fail to meet one or more of those requirements, leading to wasted scarce harvested energy. We address this by identifying beam steering as a potential mechanism to regulate the charging rate of energy harvesting devices and thus control their access to the shared wireless medium. After formulating a joint problem of energy beam steering and slotted ALOHA-based random access, we leverage a deep learning framework based on an action-specific deep recurrent Q-Network (ADRQN) to learn a beam-steering policy only from the macro-level ternary slot outcomes, namely, idle, success and collision. Additionally, we design an oracle policy with global knowledge of the network to benchmark our proposed blind adaptive beam-steering approach. The numerical results demonstrate that our approach achieves up to 68\% increase in throughput compared to non-learning schemes, while also reaching 75-80\% of the oracle policy's performance, all without requiring channel estimation, charge-level reporting, or device-state tracking.

Figures

Figures reproduced from arXiv: 2607.17838 by the authors.

Figure 1
Figure 1. Pictorial depiction of the considered WPCN, where the BEHDs distributed around the ET/AP receive an energy signal in the downlink [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Slot timing diagram for the slotted ALOHA protocol, showing the charging period, the waiting time after the upper charge threshold [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Architecture of the proposed ADRQN agent. The action and observation encoders produce embeddings that are concatenated and [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: Beam coverage for M = 5 antennas with S = 3 steering directions {ϕs} = {60◦ , 170◦ , 330◦ }. The shaded regions indicate angular ranges where the normalised received power exceeds the threshold Pth = −12 dB. angles {ϕs} S s=1 is chosen such that, for a given number of …
Figure 5
Figure 5. Figure 5: Beam coverage for M = 8 antennas with S = 5 steering directions {ϕs} = {30◦ , 60◦ , 180◦ , 300◦ , 330◦ }. The shaded regions indicate angular ranges where the normalised received power exceeds the threshold Pth = −12 dB. C. Performance Analysis [PITH_FULL_IMAGE:figure…
Figure 6
Figure 6. Figure 6: Average throughput (averaged over all device placement configurations for the last 100 episodes) versus number of BEHDs for the [PITH_FULL_IMAGE:figures/full_fig_p016_6.png]
Figure 7
Figure 7. Figure 7: Average throughput (averaged over all device placement configurations for the last 100 episodes) versus history/sequence length [PITH_FULL_IMAGE:figures/full_fig_p017_7.png]
Figure 8
Figure 8. Figure 8: Two representative device placement configurations for [PITH_FULL_IMAGE:figures/full_fig_p018_8.png]
Figure 9
Figure 9. Figure 9: Average number of idle slots (averaged across all device placements for last 100 episodes) versus network size for [PITH_FULL_IMAGE:figures/full_fig_p019_9.png]
Figure 10
Figure 10. Figure 10: Average number of collision slots (averaged across all device placements for last 100 episodes) versus network size for [PITH_FULL_IMAGE:figures/full_fig_p020_10.png]
Figure 11
Figure 11. Figure 11: Convergence of the proposed ADRQN and LSTM-ADRQN ( [PITH_FULL_IMAGE:figures/full_fig_p021_11.png]
Figure 12
Figure 12. Figure 12: Normalised idle, success, and collision slot ratios versus the power threshold [PITH_FULL_IMAGE:figures/full_fig_p022_12.png]

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Reference graph

Works this paper leans on

36 extracted references · 1 linked inside Pith

  1. [1]

    Toward 1G Mobile Power Networks: RF, Signal, and System Designs to Make Smart Objects Autonomous,

    B. Clerckx, A. Costanzo, A. Georgiadis, and N. B. Carvalho, “Toward 1G Mobile Power Networks: RF, Signal, and System Designs to Make Smart Objects Autonomous,”IEEE Microwave Magazine, vol. 19, no. 6, pp. 69–82, 2018

    2018

  2. [2]

    A comprehensive survey on RF energy harvesting: Applications and performance determinants,

    H. H. R. Sherazi, D. Zorbas, and B. O’Flynn, “A comprehensive survey on RF energy harvesting: Applications and performance determinants,”Sensors, vol. 22, no. 8, p. 2990, 2022. 21 0 200 400 600 800 Episode 0.2 0.3 0.4 0.5 0.6 0.7 0.8Average Throughput H=200 LSTM-ADRQN H=200 Proposed-ADRQN Oracle Policy Fig. 11. Convergence of the proposed ADRQN and LSTM-A...

    2022

  3. [3]

    Medium access control protocols for wireless sensor networks with energy harvesting,

    F. Iannello, O. Simeone, and U. Spagnolini, “Medium access control protocols for wireless sensor networks with energy harvesting,” IEEE Transactions on Communications, vol. 60, no. 5, pp. 1381–1389, 2012

    2012

  4. [4]

    Deep Reinforcement Learning for 5G Networks: Joint Beamforming, Power Control, and Interference Coordination,

    F. B. Mismar, B. L. Evans, and A. Alkhateeb, “Deep Reinforcement Learning for 5G Networks: Joint Beamforming, Power Control, and Interference Coordination,”IEEE Transactions on Communications, vol. 68, no. 3, pp. 1581–1592, March 2020

    2020

  5. [5]

    Deep Reinforcement Learning Based Blind mmWave MIMO Beam Alignment,

    V . Raj, N. Nayak, and S. Kalyani, “Deep Reinforcement Learning Based Blind mmWave MIMO Beam Alignment,”IEEE Transactions on Wireless Communications, vol. 21, no. 10, pp. 8772–8785, October 2022

    2022

  6. [6]

    Fast mmWave Beam Alignment via Correlated Bandit Learning,

    W. Wu, N. Cheng, N. Zhang, P. Yang, W. Zhuang, and X. S. Shen, “Fast mmWave Beam Alignment via Correlated Bandit Learning,” IEEE Transactions on Wireless Communications, vol. 18, no. 12, pp. 5894–5908, December 2019

    2019

  7. [7]

    Deep Recurrent Q-Network Methods for mmWave Beam Tracking Systems,

    J. Park, S. Hwang, H. Lee, and I. Lee, “Deep Recurrent Q-Network Methods for mmWave Beam Tracking Systems,”IEEE Transactions on Vehicular Technology, vol. 71, no. 12, pp. 13 429–13 434, December 2022

    2022

  8. [8]

    Adaptive beam steering in wpcns under slotted aloha via deep q-learning,

    S. Adhikari and S. Sarma, “Adaptive beam steering in wpcns under slotted aloha via deep q-learning,” in2026 18th International Conference on COMmunication Systems and NETworks (COMSNETS). IEEE, 2026, pp. 228–236

    2026

Show all 36 references
  1. [9]

    On Improving Deep Reinforcement Learning for POMDPs,

    P. Zhu, X. Li, P. Poupart, and G. Miao, “On Improving Deep Reinforcement Learning for POMDPs,”arXiv preprint arXiv:1704.07978, 2017

    2017 arXiv

  2. [10]

    Throughput Maximization in Wireless Powered Communication Networks,

    H. Ju and R. Zhang, “Throughput Maximization in Wireless Powered Communication Networks,”IEEE Transactions on Wireless Communications, vol. 13, no. 1, pp. 418–428, January 2014

    2014

  3. [11]

    Multi-Antenna Wireless Powered Communication with Energy Beamforming,

    L. Liu, R. Zhang, and K.-C. Chua, “Multi-Antenna Wireless Powered Communication with Energy Beamforming,”IEEE Transactions on Communications, vol. 62, no. 12, pp. 4349–4361, December 2014

    2014

  4. [12]

    Optimal resource allocation in backscatter assisted wpcn with practical energy harvesting model,

    P. Ramezani and A. Jamalipour, “Optimal resource allocation in backscatter assisted wpcn with practical energy harvesting model,” IEEE Transactions on Vehicular Technology, vol. 68, no. 12, pp. 12 406–12 410, 2019

    2019

  5. [13]

    On throughput maximization of time division multiple access with energy harvesting users,

    J. Liu, H. Dai, and W. Chen, “On throughput maximization of time division multiple access with energy harvesting users,”IEEE Transactions on Vehicular Technology, vol. 65, no. 4, pp. 2457–2470, 2015

    2015

  6. [14]

    Residual energy estimation-based mac protocol for wireless powered sensor networks,

    S.-B. Lee, J.-H. Kwon, and E.-J. Kim, “Residual energy estimation-based mac protocol for wireless powered sensor networks,”Sensors, vol. 21, no. 22, p. 7617, 2021

    2021

  7. [15]

    Optimal resource allocation in full-duplex wireless-powered communication network,

    H. Ju and R. Zhang, “Optimal resource allocation in full-duplex wireless-powered communication network,”IEEE Transactions on Communications, vol. 62, no. 10, pp. 3528–3540, 2014

    2014

  8. [16]

    Throughput Optimization for Massive MIMO Systems Powered by Wireless Energy Transfer,

    G. Yang, C. K. Ho, R. Zhang, and Y . L. Guan, “Throughput Optimization for Massive MIMO Systems Powered by Wireless Energy Transfer,”IEEE Journal on Selected Areas in Communications, vol. 33, no. 8, pp. 1640–1650, 2015. 22 -inf -30 -22 -18 -12 -10 -6 P_th (in dB) 0.0 0.2 0.4 0...

    2015

  9. [17]

    On-demand energy transfer and energy-aware polling-based mac for wireless powered sensor networks,

    M. Li, C.-C. Fang, and H.-W. Ferng, “On-demand energy transfer and energy-aware polling-based mac for wireless powered sensor networks,”Sensors, vol. 22, no. 7, p. 2476, 2022

    2022

  10. [18]

    Odmac: An on-demand mac protocol for energy harvesting-wireless sensor networks,

    X. Fafoutis and N. Dragoni, “Odmac: An on-demand mac protocol for energy harvesting-wireless sensor networks,” inProceedings of the 8th ACM Symposium on Performance evaluation of wireless ad hoc, sensor, and ubiquitous networks, 2011, pp. 49–56

    2011

  11. [19]

    Eri-mac: An energy-harvested receiver-initiated mac protocol for wireless sensor networks,

    K. Nguyen, V .-H. Nguyen, D.-D. Le, Y . Ji, D. A. Duong, and S. Yamada, “Eri-mac: An energy-harvested receiver-initiated mac protocol for wireless sensor networks,”International Journal of Distributed Sensor Networks, vol. 10, no. 5, p. 514169, 2014

    2014

  12. [20]

    Markov chain performance model for ieee 802.11 devices with energy harvesting source,

    G. Yang, G.-Y . Lin, and H.-Y . Wei, “Markov chain performance model for ieee 802.11 devices with energy harvesting source,” in2012 IEEE global communications conference (GLOBECOM). IEEE, 2012, pp. 5212–5217

    2012

  13. [21]

    RF-MAC: A Medium Access Control Protocol for Re-Chargeable Sensor Networks Powered by Wireless Energy Harvesting,

    M. Y . Naderi, P. Nintanavongsa, and K. R. Chowdhury, “RF-MAC: A Medium Access Control Protocol for Re-Chargeable Sensor Networks Powered by Wireless Energy Harvesting,”IEEE Transactions on Wireless Communications, vol. 13, no. 7, pp. 3926–3937, 2014

    2014

  14. [22]

    Slotted ALOHA for Wireless Powered Communication Networks,

    H.-H. Choi and W. Shin, “Slotted ALOHA for Wireless Powered Communication Networks,”IEEE Access, vol. 6, pp. 53 342–53 355, 2018

    2018

  15. [23]

    Harvest-or-access: Slotted ALOHA for Wireless Powered Communication Networks,

    H.-H. Choiet al., “Harvest-or-access: Slotted ALOHA for Wireless Powered Communication Networks,”IEEE Transactions on Vehicular Technology, vol. 68, no. 11, pp. 11 394–11 398, 2019

    2019

  16. [24]

    Exploring hybrid active and passive multiple access via slotted aloha-driven backscatter communications,

    B. Gu, D. Li, H. Xie, K. Yu, Q. Guan, and Y . Xu, “Exploring hybrid active and passive multiple access via slotted aloha-driven backscatter communications,”IEEE Transactions on Cognitive Communications and Networking, vol. 11, no. 5, pp. 3317–3332, 2025

    2025

  17. [25]

    Reinforcement Learning for Scheduling Wireless Powered Sensor Communications,

    K. Liet al., “Reinforcement Learning for Scheduling Wireless Powered Sensor Communications,”IEEE Transactions on Green Communications and Networking, vol. 3, no. 2, pp. 264–274, 2018

    2018

  18. [26]

    Reinforcement Learning Based Adaptive Resource Allocation for Wireless Powered Communication Systems,

    J.-M. Kang, “Reinforcement Learning Based Adaptive Resource Allocation for Wireless Powered Communication Systems,”IEEE Communications Letters, vol. 24, no. 8, pp. 1752–1756, 2020

    2020

  19. [27]

    Multi-Agent Deep Reinforcement Learning for Distributed Resource Management in Wirelessly Powered Commu- nication Networks,

    S. Hwanget al., “Multi-Agent Deep Reinforcement Learning for Distributed Resource Management in Wirelessly Powered Commu- nication Networks,”IEEE Transactions on Vehicular Technology, vol. 69, no. 11, pp. 14 055–14 060, 2020

    2020

  20. [28]

    Distributed power control for large energy harvesting networks: A multi-agent deep reinforcement learning approach,

    M. K. Sharma, A. Zappone, M. Assaad, M. Debbah, and S. Vassilaras, “Distributed power control for large energy harvesting networks: A multi-agent deep reinforcement learning approach,”IEEE Transactions on Cognitive Communications and Networking, vol. 5, no. 4, pp. 1140–1154, 2019. 23

    2019

  21. [29]

    Long-term throughput maximization in wireless powered communication networks: A multitask drl approach,

    A. Ahmadian, W. Shin, and H. Park, “Long-term throughput maximization in wireless powered communication networks: A multitask drl approach,”IEEE Internet of Things Journal, vol. 11, no. 11, pp. 19 616–19 631, 2024

    2024

  22. [30]

    Wireless Networks with RF Energy Harvesting: A Contemporary Survey,

    X. Lu, P. Wang, D. Niyato, D. I. Kim, and Z. Han, “Wireless Networks with RF Energy Harvesting: A Contemporary Survey,”IEEE Commun. Surveys Tuts., vol. 17, no. 2, pp. 757–789, 2015

    2015

  23. [31]

    Reconfigurable Intelligent Surface Assisted Multi-Carrier Wireless Systems for Doubly Selective High-Mobility Ricean Channels,

    C. Xu, J. An, T. Bai, L. Xiang, S. Sugiura, R. G. Maunder, L.-L. Yang, and L. Hanzo, “Reconfigurable Intelligent Surface Assisted Multi-Carrier Wireless Systems for Doubly Selective High-Mobility Ricean Channels,”IEEE Transactions on Vehicular Technology, vol. 71, no. 4, pp. 4...

    2022

  24. [32]

    Power Allocation and Scheduling for SWIPT Systems with Non-Linear Energy Harvesting Model,

    E. Boshkovska, R. Morsi, D. W. K. Ng, and R. Schober, “Power Allocation and Scheduling for SWIPT Systems with Non-Linear Energy Harvesting Model,” in2016 IEEE Int. Conf. Commun. (ICC). IEEE, 2016, pp. 1–6

    2016

  25. [33]

    Characterisation of the Time-to-Recharge of Battery-Free RF Energy Harvesting Devices in Wireless Powered Communi- cation Networks,

    S. Sarma, “Characterisation of the Time-to-Recharge of Battery-Free RF Energy Harvesting Devices in Wireless Powered Communi- cation Networks,” inProc. Nat. Commun. Conf. (NCC), 2025

    2025

  26. [34]

    Recurrent Experience Replay in Distributed Reinforcement Learning,

    S. Kapturowski, G. Ostrovski, J. Quan, R. Munos, and W. Dabney, “Recurrent Experience Replay in Distributed Reinforcement Learning,” inInternational Conference on Learning Representations (ICLR), 2018

    2018

  27. [35]

    Long Short-Term Memory,

    S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,”Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997

    1997

  28. [36]

    Human-Level Control Through Deep Reinforcement Learning,

    V . Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis, “Human-Level Control Through Deep...

    2015

Pith tools

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