AoI-Sensitive Data Forwarding with Distributed Beamforming in UAV-Assisted IoT

Geng Sun; Guixia Liu; Jiacheng Wang; Jiahui Li; Victor C.M. Leung; Zemin Sun; Zifan Lang

REVIEW 4 major objections 6 minor 1 cited by

AoI-Sensitive Data Forwarding with Distributed Beamforming in UAV-Assisted IoT

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper claims that a modified SAC algorithm with temporal sequence input, LNGRU, and attention achieves the lowest AoI and competitive energy consumption in a distributed-beamforming UAV relay network.

desk verdict Plausible DRL recipe undermined by an AoI metric that is really a fluid proxy; empirical claims cannot be assessed without code and parameters. read the letter →

arxiv 2502.09038 v1 pith:NQA4Y5IJ submitted 2025-02-13 cs.AI

Zifan Lang , Guixia Liu , Geng Sun , Jiahui Li , Zemin Sun , Jiacheng Wang , Victor C.M. Leung This is my paper · ORCID

classification cs.AI

keywords ageofinformationUAV-assistedIoTdistributedbeamformingvirtualantennaarraydeepreinforcementlearningsoftactor-critictrajectoryoptimizationdataforwarding

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 tries to establish that a UAV-assisted IoT relay can keep sensor data fresh without excessive flight energy by forwarding data from hovering UAVs with distributed beamforming, instead of flying back and forth to the base station. To make that work, the authors write a joint optimization over UAV trajectories and communication schedules that minimizes time-averaged age of information (AoI) plus UAV energy, note that the problem is non-convex and highly dynamic, and solve it with a modified soft actor-critic algorithm called SAC-TLA. SAC-TLA feeds a temporal sequence of states into a layer-normalized gated recurrent unit with a global soft attention mechanism, and the simulations compare it with TD3, PPO, TQC, and standard SAC. The reported result is that SAC-TLA converges fastest and reaches the lowest AoI while keeping energy consumption competitive. If true, this gives a practical recipe for AoI-sensitive UAV relaying in remote IoT settings.

What carries the argument

The machinery is SAC-TLA, a modified soft actor-critic whose policy network processes a window of past states through LNGRU cells — gated recurrent units with layer normalization applied before the gated computations — and then applies a global soft attention mechanism $\alpha_t=\exp(h_t^\top W_a h_c)/\sum_{t'}\exp(h_{t'}^\top W_a h_c)$ to the hidden states, focusing the agent on the sensors with the highest AoI. Dynamic proximity-based action mapping (DPAM) removes discrete sensor-association decisions by making them deterministic functions of which sensors lie inside each UAV's communication radius, so the learner only outputs continuous UAV movements. The physical layer that makes the scenario work is the virtual antenna array: distributed beamforming lets the UAVs coherently combine signals at the base station, giving the A2G rate $R_{\mathrm{BS}}(t)=B\log_2(1+\gamma_{\mathrm{SNR}}(t))$, and this extended range is what lets the UAVs relay from hover rather than flying frequently.

What would settle it

Re-run the same five algorithms with a physical-layer model that includes per-UAV carrier phase offsets or a finite A2A broadcast capacity, so the forwarded fraction is not the idealized $\min\{S_{\mathrm{G2A}},S_{\mathrm{A2G}}\}/S_c$; if SAC-TLA no longer holds the lowest time-averaged AoI, the reported advantage depends on the idealized distributed-beamforming assumptions rather than on the learning architecture alone.

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Extended reading notes

Core claim

On its own terms, the paper establishes that combining distributed-beamforming relays with an architecture-enhanced off-policy reinforcement learner gives the best AoI-energy balance among the tested methods in a simulated UAV-assisted IoT network. The algorithmic claim is that SAC-TLA — soft actor-critic with temporal sequence state input, layer-normalized gated recurrent units (LNGRU), and a global soft attention mechanism — achieves lower time-averaged AoI than TD3, PPO, TQC, and standard SAC, with competitive energy consumption, faster convergence, and greater training stability. The system-level claim underneath is that the UAVs, acting as a virtual antenna array through distributed beamforming, can reach the remote base station from their hovering positions, which reduces the flight frequency that would otherwise inflate AoI. The evaluation rests on the AoI update $A_i(t+1)=(1-Q(t))(A_i(t)+1)$ for a served sensor and $A_i(t)+1$ otherwise, where $Q(t)=\min\{S_{\mathrm{G2A}},S_{\mathrm{A2G}}\}/S_c$ is the fraction of all collected data the array can forward to the base station in a time slot.

Load-bearing premise

The load-bearing premise is that the A2A broadcast reliably gives every UAV all data collected by every other UAV and that the UAVs form a perfectly phase-aligned virtual antenna array, so that each time slot the system forwards exactly the smaller of the collected data volume and the array's downlink capacity; if either piece fails, the AoI update rule used to train the policy no longer describes the physical network.

Editorial extensions

If this is right

Under the modeled assumptions, distributed beamforming lets a UAV swarm relay to the base station while hovering, so UAVs fly less often and the age of collected data grows more slowly than in fly-back-and-forth designs.
The joint trajectory-and-communication problem can be solved in real time by a single DRL policy, with DPAM converting sensor-association choices into deterministic functions of UAV positions.
The AoI-energy tradeoff has an adjustable operating point: SAC-TLA sits at the low-AoI end and TD3 at the low-energy end, so a network operator can choose the policy whose balance fits the application.
Faster convergence and flatter training curves imply SAC-TLA needs fewer episodes to reach a usable policy, reducing the computational cost of adapting when the IoT deployment changes.

Reading between the lines

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

A direct extension the authors leave implicit is that the temporal-sequence, LNGRU, and attention modifications are generic and could be grafted onto TD3, PPO, or TQC; an ablation that swaps the underlying algorithm while keeping the three components would quantify how much of the gain comes from the architecture rather than from SAC itself.
The global forwarding fraction $Q(t)$ compresses every sensor's success into a single number, so average AoI can hide large asymmetries between sensors; a per-sensor or tail-AoI metric would test whether the policy is also fair.
Because the VAA downlink rate depends on coherently adding amplitudes, phase misalignment is the most direct threat to the modeled AoI advantage; simulating with nonzero phase-error variance would turn the qualitative robustness claim into a quantitative curve.

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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

Quick take: this is a plausible but thinly supported DRL recipe paper. The model combines distributed beamforming with UAV trajectory planning, which is a sensible way to reduce flight delays, and SAC-TLA (temporal sequence + LNGRU + attention) is a reasonable engineering extension of SAC. The paper is readable and the problem setup is clear.

The main problem is the AoI model. Eq. (5) is not age of information. Standard AoI resets to the age of the delivered packet on a successful update; here it multiplies every sensor's age by (1-Q(t)) whenever the sensor is served, with Q(t) a global fraction of data forwarded. When Q=0.5, the fixed point is 0.5, while true Bernoulli-delivery AoI has mean 2. So the quantity minimized in Eq. (7) and plotted in Fig. 4(b) is a fluid proxy, not the AoI promised in the title and abstract. That is load-bearing: the headline comparison is on this metric.

Second, the MDP simplification changes the problem. DPAM removes the binary schedule variables from the action space, so the agent only controls UAV movement. The paper never reformulates the optimization problem (7) accordingly. This might be fine as a heuristic, but it leaves a mismatch between the stated problem and what is actually solved.

Third, the evaluation is under-reported: no parameter values, no error bars, no code, and the beamforming rate relies on the authors' own prior models without independent validation. The training curves and scatter plot cannot be independently checked.

What the paper does well: it identifies a real issue (flight-induced AoI), and the architectural modifications to SAC are clearly motivated. If the AoI model were corrected to actual packet-level freshness, and the experiments were made reproducible, this could be a useful incremental contribution. As it stands, the central performance claim is not supported by the evidence presented.

I would send this to peer review rather than desk reject, because the underlying idea is plausible and the flaws are fixable. But a referee should demand a corrected metric, a revised problem statement, and full experimental details. I would not cite it in its current form.

Referee Report

4 major / 6 minor

Summary. The paper studies a UAV-assisted IoT data-forwarding system in which a swarm of UAVs collects sensor data, broadcasts it among themselves, and relays it to a remote base station using distributed beamforming. The authors formulate a joint optimization problem that minimizes time-averaged age of information (AoI) and UAV energy consumption by choosing UAV trajectories and communication schedules. Because the problem is non-convex and dynamic, they reformulate it as an MDP and propose SAC-TLA, a modified soft actor-critic algorithm that combines temporal sequence input, layer-normalized gated recurrent units, and a global soft attention mechanism. The paper reports simulation results comparing SAC-TLA with TD3, PPO, TQC, and SAC, and claims that SAC-TLA achieves the lowest AoI while maintaining competitive energy consumption.

Significance. The paper addresses a practically relevant problem, and the proposed algorithmic combination (SAC plus temporal sequence input, LNGRU, and attention) is a plausible way to handle partially observed dynamics in UAV trajectory optimization. The DPAM simplification of discrete scheduling decisions is a useful idea for avoiding mixed discrete-continuous action spaces. However, the central quantitative claims rest on a nonstandard AoI model and on an MDP simplification that is not shown to be equivalent to the original optimization problem; the experimental section also lacks the parameter settings and statistical reporting needed for reproducibility. If the AoI model and system assumptions were validated, the proposed SAC-TLA would be a reasonable baseline for trajectory-only resource allocation in UAV-assisted IoT, but as written the paper does not establish that the reported gains correspond to actual data freshness.

major comments (4)

[Section II-C, Eq. (5)] The quantity called AoI is not the age of information. Standard discrete-time AoI increments by one when no new packet is delivered and resets to the age of the delivered packet (at least one) on success. Eq. (5) instead applies the global forwarding fraction Q(t) as a multiplicative factor to the age of every served sensor: when Q(t)=1 the AoI resets to 0, and when Q(t)<1 it scales down fractionally even if no fresh packet from that sensor is delivered. For a Bernoulli forwarding process with success probability Q, the fixed point of Eq. (5) is (1-Q)/Q, whereas the stationary mean of standard AoI is 1/Q; the two differ by a factor of two when Q=0.5. Because Eq. (7) and Fig. 4(b) are evaluated with this proxy, the headline claim that SAC-TLA minimizes AoI is not established, and the reported optimization may reward schedules that would perform poorly under a faithful AoI metric.
[Section III-A2 and III-A3] The MDP action space contains only the continuous movements {a_x^j(t), a_y^j(t)}, and the DPAM mechanism deterministically maps trajectory choices to communication decisions by checking whether sensors are within the communication radius. This removes the binary scheduling variables beta_{i,j}(t) from the learning problem, so the MDP no longer optimizes the problem in Eq. (7), which includes scheduling constraints (7b) and (7d). The paper does not show that DPAM preserves feasibility or optimality with respect to the original problem, nor does it discuss how the trajectory-only policy relates to the required schedule. Consequently, the simulation results validate a different, simplified problem from the one formulated in Section II.
[Section IV] The simulation section does not report any parameter values: environment constants a and b, channel parameters rho0 and alpha, noise power sigma^2, bandwidths B and B_j, transmit powers, communication radius used by DPAM, UAV propulsion and speed parameters, reward weights rho1, rho2, rho3, penalty p_j, training hyperparameters, episode length, or sequence length n. No error bars, confidence intervals, or number of random seeds are given, and Fig. 4(a) appears to show single training curves without a legend. As a result, the claims of faster convergence, better stability, and a superior AoI-energy trade-off are not reproducible, and the statement that SAC-TLA maintains 'competitive energy consumption' is not quantified.
[Section II-B2 and II-B3] The model assumes that all data collected from sensors can be reliably broadcast among the UAVs within each time slot and that the UAVs form a perfectly phase-aligned virtual antenna array whose SNR is given by Eq. (4) using the formula from reference [5]. These assumptions are asserted without physical-layer derivation, simulation at waveform level, or sensitivity analysis for phase errors and channel estimation errors. Since these assumptions directly determine the forwarding fraction Q(t) and the claimed communication-range extension, the quantitative AoI improvements could be optimistic.

minor comments (6)

[Notation] The symbol Q is overloaded: in Eq. (7), Q denotes the set of UAV positions, while in Eq. (5) and the text, Q(t) denotes the data forwarding fraction. Please use distinct symbols to avoid confusion.
[References] The benchmark algorithms TD3, PPO, TQC, and SAC are all cited to a survey [8]; the original papers for these algorithms should be cited instead.
[Section III-C4] The sentence 'the actor network combines LNGRU and the attention mechanism' appears twice consecutively; one occurrence should be removed.
[Algorithm 1] Line 11 says 'Select action a_t with exploration noise,' but SAC normally samples actions from a stochastic policy rather than adding exploration noise to a deterministic action; please clarify the action-selection procedure.
[Typos] There are several typographical issues, including 'UA V' with an internal space in the author line and throughout, 'muti-access' in Section II-A, 'characteristize' in Section II, and 'ro reduce' in Section IV.
[Fig. 4] Fig. 4(a) appears to lack a legend identifying the curves, and neither panel reports error bars or confidence intervals; please add these to support the stability and convergence claims.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reward is the optimization objective by standard RL construction, and the cited VAA/beamforming model is an externally falsifiable modeling assumption rather than a predicted output.

full rationale

The paper's derivation chain is internally consistent without being circular. The optimization objective (Eq. 7) is defined directly from the AoI dynamics (Eq. 5) and the UAV energy model (Sec. II-D), and the SAC-TLA reward (Eq. 8) is a negatively weighted version of exactly that objective. Training a DRL agent to minimize the metric that is later reported is standard optimization practice: the benchmark algorithms are trained on the same reward, and the comparison in Fig. 4 is an empirical simulation result, not an identity forced by construction. The A2G SNR formula in Eq. (4) and the virtual-antenna-array capacity assumption are cited to the authors' prior works [4], [5] and are load-bearing for the system model; however, they are explicit physical-layer modeling assumptions (LoS channel, coherent VAA) that are externally falsifiable and are not derived from this paper's own conclusions, so citing them does not make the paper's AoI/energy claims true by definition. No fitted parameter is later renamed as a prediction, no uniqueness theorem is invoked to forbid alternative designs, and no known result is merely relabeled. The non-standard AoI update in Eq. (5) (fractional scaling rather than reset to packet age) is a modeling-validity concern, not a circularity concern.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

All numerical claims emerge from a simulation whose physical-layer and DRL details are mostly unpinned. I list the hand-set parameters and modeling assumptions that the AoI and energy numbers depend on, plus the DPAM mapping that changes the optimization problem.

free parameters (6)

Reward weights rho1, rho2, rho3 = not reported
In Eq. (8), these normalize AoI, energy, and coverage terms; hand-tuned values change the learned trade-off and are not listed.
Out-of-bounds penalty p_j = not reported
In Eq. (8), p_j shapes exploration, but its magnitude is unspecified.
DPAM communication radius = not reported
In Sec. III-A3, SNs within this radius are scheduled deterministically; the radius is never specified and determines which data are collected.
Channel and environment constants (a, b, rho0, alpha, sigma^2, bandwidths) = not reported
Sec. II-B lists formulas but no numeric values; all final AoI and energy numbers depend on them.
UAV propulsion power P_UAV(v) and speed profile v = not reported
Sec. II-D uses the model from [6], but neither v nor the power constants appear in the paper, so energy numbers are not reproducible.
Training hyperparameters (learning rate, batch size, entropy target, sequence length n) = not reported
Algorithm 1 omits all training hyperparameters; convergence speed claims depend on them.

assumptions (4)

domain assumption Global AoI reset fraction Q(t) applies identically to every sensor whose UAV communicated (Eq. 5), even though Q(t) is the total forwarded data fraction.
This couples all sensors into one aggregate reset; no packet-level or per-sensor model justifies it.
domain assumption All data collected from SNs can be reliably broadcast to the UAV swarm within every time slot.
Sec. II-B2 asserts this because inter-UAV distances are small, which removes A2A outage from the model.
domain assumption UAVs form a coherent virtual antenna array with perfect phase alignment for A2G beamforming.
Sec. II-B3 writes the SNR as a coherent sum of amplitudes; synchronization and phase errors are ignored.
ad hoc to paper DPAM deterministically maps trajectory choices to a feasible communication schedule.
Sec. III-A3 replaces beta actions with a distance-based rule; no proof shows this covers the original feasible set or preserves optimality.

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Cite this review

Pith. "Pith review of AoI-Sensitive Data Forwarding with Distributed Beamforming in UAV-Assisted IoT." pith.science (2026). https://pith.science/paper/NQA4Y5IJ

@misc{pith2026250209038,
  author       = {Pith},
  title        = {Pith review of: AoI-Sensitive Data Forwarding with Distributed Beamforming in UAV-Assisted IoT},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NQA4Y5IJ}},
  note         = {Machine review of arXiv:2502.09038}
}

read the original abstract

This paper proposes a UAV-assisted forwarding system based on distributed beamforming to enhance age of information (AoI) in Internet of Things (IoT). Specifically, UAVs collect and relay data between sensor nodes (SNs) and the remote base station (BS). However, flight delays increase the AoI and degrade the network performance. To mitigate this, we adopt distributed beamforming to extend the communication range, reduce the flight frequency and ensure the continuous data relay and efficient energy utilization. Then, we formulate an optimization problem to minimize AoI and UAV energy consumption, by jointly optimizing the UAV trajectories and communication schedules. The problem is non-convex and with high dynamic, and thus we propose a deep reinforcement learning (DRL)-based algorithm to solve the problem, thereby enhancing the stability and accelerate convergence speed. Simulation results show that the proposed algorithm effectively addresses the problem and outperforms other benchmark algorithms.

Figures

Figures reproduced from arXiv: 2502.09038 by the authors.

**Figure 3.** The framework of the SAC-TLA algorithm. each state individually, temporal sequence input allows the algorithm to leverage temporal information from multiple time steps. Specifically, the environment generates a sequence of states {st, st−1, ..., st−n} as the input to actor and critic networks, which enables the agents to better capture dependencies and patterns in the environment to improve policy performance ultim… view at source ↗

**Figure 4.** Simulation results. (a) Cumulative rewards training curve. (b) The optimization objective values of SAC-TLA, TQC, [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

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

Multi-AAV-enabled Distributed Beamforming in Low-Altitude Wireless Networking for AoI-Sensitive IoT Data Forwarding
cs.NI 2025-09 conditional novelty 4.0 of 10

An enhanced SAC algorithm with temporal sequence input, layer-normalized GRU, and squeeze-excitation blocks jointly optimizes drone trajectories and communication schedules for distributed-beamforming drone relays, re...

Reference graph

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [5]

J. Li, G. Sun, Q. Wu, D. Niyato, J. Kang, A. Jamalipour, and V. C. M. Leung, ``Collaborative ground-space communications via evolutionary multi-objective deep reinforcement learning,'' CoRR, vol. abs/2404.07450, 2024

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Chang, J

S. Chang, J. Kuo, M. Kao, B. Chen, and Q. Wang, ``Near-optimal UAV deployment for delay-bounded data collection in iot networks,'' in Proc. IEEE INFOCOM , 2024, pp. 111--120

work page 2024

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Y. Long, W. Zhang, S. Gong, X. Luo, and D. Niyato, `` A o I -aware scheduling and trajectory optimization for multi- UAV -assisted wireless networks,'' in Proc. IEEE GLOBECOM , 2022, pp. 2163--2168

work page 2022

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J. Li, G. Sun, L. Duan, and Q. Wu, ``Multi-objective optimization for UAV swarm-assisted I o T with virtual antenna arrays,'' IEEE Trans. Mob. Comput. , vol. 23, no. 5, pp. 4890--4907, 2024

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work page 2024

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