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REVIEW 5 major objections 7 minor 2 cited by

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

T0 review · 5 major / 7 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read A UAV swarm can serve a moving ground user by acting as one virtual antenna array, and an evolutionary multi-objective RL algorithm charts the rate-versus-energy trade-off.

desk verdict A plausible but incremental MORL-for-UAV-CB paper whose headline claim rests on single-run simulations with no variance and an unspecified IGD/HV reference; it deserves review after statistical fixes. read the letter →

arxiv 2502.05824 v1 pith:AM3S7RHX submitted 2025-02-09 cs.NE

Geng Sun , Jian Xiao , Jiahui Li , Jiacheng Wang , Jiawen Kang , Dusit Niyato , Shiwen Mao This is my paper · ORCID
classification cs.NE
keywords UAVcommunicationscollaborativebeamformingvirtualantennaarraymulti-objectivereinforcementlearningproximalpolicyoptimizationLSTMusermobilityParetofront
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

Small UAVs can pool their antennas into a virtual array and, by moving and adjusting transmit phases in real time, keep a high-rate link to a ground user who moves unpredictably. This paper tries to establish that this joint control task — choosing each UAV's trajectory and excitation current weight at every time slot — should be treated as an NP-hard, long-term multi-objective problem that trades total achievable rate against flight energy, and that an evolutionary deep reinforcement-learning algorithm can solve it online. The proposed method, EMOPPO-VLH, extends PPO with a vectorized value function, LSTM memory for the user's Gauss–Markov motion and time-varying Rician channels, and a hyper-sphere-based task-selection rule that keeps the Pareto set diverse. If the reported simulations are right, an operator can pick from this Pareto set a policy matching the current preference for throughput versus battery life, and update the policy set when the environment changes.

What carries the argument

The central object is the UAV-enabled virtual antenna array (UVAA), whose array factor $AF[t](\theta,\varphi)=\sum_{i=1}^N I_i[t]e^{jk_c(x_i^U[t]\sin\theta\cos\varphi+y_i^U[t]\sin\theta\sin\varphi+z_i^U[t]\cos\theta)}$ determines the beamforming gain toward the mobile user; every rate calculation flows through this quantity. The algorithmic machinery is the MOMDP formulation with vectorized reward $r[t]=(R_{UM}[t], -\epsilon_1 E[t])$ and a vectorized value function, extended with LSTM layers in the actor and critic to capture temporal dependencies in user movement and channel fading, and with a hyper-sphere-based task-selection step that biases training toward sparse regions of the objective space so the final Pareto set is diverse.

What would settle it

Run a field trial with 8 rotary-wing UAVs and a GPS-tracked mobile user under the paper's Rician-and-interference parameters, then compare measured total achievable rate and total energy consumption to the simulated Pareto front; if the realized points are dominated by a baseline policy or lie far outside the front, the central claim fails.

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Extended reading notes

Core claim

The paper claims that a swarm of rotary-wing UAVs can maintain a reliable high-rate downlink to a terrestrial user who moves randomly by treating the whole swarm as a UAV-enabled virtual antenna array (UVAA) and optimizing, at every time slot, each UAV's horizontal direction, horizontal and vertical flight distances, and excitation current weight. It argues that the resulting long-term problem—maximize total achievable rate $\sum_t R_{UM}[t]$ while minimizing total flight energy $\sum_t \sum_i E_i[t]$—is NP-hard and too dynamic for offline methods, so it converts it into a multi-objective Markov decision process and solves it with EMOPPO-VLH. EMOPPO-VLH evolves a population of PPO policies whose value functions are vectorized to handle two rewards, whose actor and critic networks use LSTM layers to remember user and channel history, and whose hyper-sphere-based task selection keeps the approximate Pareto set well spread. In simulations with 8 and 16 UAVs, the paper reports that EMOPPO-VLH attains the smallest inverted generational distance and the largest hypervolume among the compared algorithms, together with the best total achievable rate and energy consumption.

Load-bearing premise

The load-bearing premise is that the mathematical simulation — Rician fading, Gauss–Markov user movement, the UAV propulsion model, and the array-factor equations — is faithful enough that a policy trained in it transfers to real hardware; if it does not, the claimed advantages are unverified.

Editorial extensions

If this is right

  • Operators get a Pareto set of control policies from one training run, so the rate-versus-energy preference can be changed at deployment time without retraining.
  • The same formulation covers users whose motion has memory; the LSTM layer is what lets the policy exploit the Gauss–Markov correlation in user movement.
  • The approach scales from a small 8-UAV swarm to a large 16-UAV swarm while keeping the best IGD and hypervolume values among the compared baselines.
  • Because actions are expressed as directions, distances, and excitation current weights, the trained policies map directly to UAV control commands and can run on a central controller using only position information.

Reading between the lines

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

  • Not tested in the paper is the step from simulation to hardware; if that transfer holds, the same training-and-policy-set workflow could replace offline weighted-sum planning in field UAV deployments, but a field trial is what would establish it.
  • The MOMDP and task-selection machinery are built for a single mobile user; an extension the paper leaves implicit is several users, where the state and reward vectors would grow and the UVAA would need to split its mainlobe or time-share users.
  • A testable prediction follows from the Gauss–Markov assumption: the performance gap between EMOPPO-VLH and a memoryless version should widen as the memory level $\alpha_g$ increases, which would isolate how much of the gain comes from the LSTM.
  • The same evolutionary multi-objective RL structure could transfer to other aerial trade-offs, such as sensing-versus-communication or coverage-versus-jamming, because the vectorized-value-function and population-evolution design is not beamforming-specific.
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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

5 major / 7 minor

Summary. The paper studies a UAV swarm that forms a virtual antenna array (UVAA) via collaborative beamforming to serve a terrestrial mobile user, under interference from a non-associated base station and time-varying channels. It formulates a long-term multi-objective optimization problem (MOP) with two conflicting objectives: maximizing the total achievable rate and minimizing UAV flight energy consumption. After arguing that the problem is NP-hard, the authors convert it into a multi-objective Markov decision process and propose EMOPPO-VLH, an evolutionary multi-objective PPO variant combining a vectorized value function, LSTM networks, and a hyper-sphere-based task selection mechanism. The paper reports simulations for small- and large-scale UAV swarms and claims that EMOPPO-VLH achieves the smallest IGD and largest HV values and outperforms six baseline algorithms, with additional robustness experiments summarized in appendices. The central empirical claim is that EMOPPO-VLH outperforms existing methods in both objective values and Pareto-set quality.

Significance. If the claimed performance is sustained, the work would be a useful engineering contribution to UAV-enabled collaborative beamforming with mobile users: the system model is reasonably detailed, the use of LSTM to capture user mobility is sensible, and the evolutionary task-selection idea is a plausible way to improve Pareto diversity in multi-objective reinforcement learning. The paper supplies algorithmic pseudo-code, complexity expressions, and a substantial set of scenarios, which makes the method reproducible in principle. However, the manuscript does not release code or data, and the headline quantitative claims rest entirely on simulation curves without variance information. The comparison also omits the authors' own closest prior evolutionary MORL algorithm [29], which weakens the support for the 'outperforms all other approaches' statement. The lack of seed statistics and an unspecified reference Pareto front are load-bearing for the main claims, so the paper needs a revision that strengthens the empirical evidence rather than a purely editorial fix.

major comments (5)
  1. [Sec. 6.4, Figs. 5-8] The central claim that EMOPPO-VLH 'can achieve the smallest IGD values and the largest HV values across all scenarios' and 'outperforms all other optimization approaches across both scales' is supported only by single convergence curves and bar charts without error bars, and the number of random seeds is not reported. EMOPPO-VLH is an on-policy PPO variant, a class known to have high run-to-run variance; without multiple seeds and variance or significance statistics, the reported margins over EPPO-GRU and ETD3 cannot be distinguished from seed noise. Please rerun all compared algorithms with multiple seeds and report mean and standard deviation for f1, f2, IGD, and HV, and state the seed count explicitly.
  2. [Sec. 6.2 and Sec. 6.4] The IGD and HV metrics require a reference Pareto front and a reference point, but the manuscript does not specify how the reference set is constructed or how the reference point is chosen. If the reference front is the union of all obtained fronts of all algorithms, then the hyper-sphere-based diversity mechanism could inflate IGD/HV values by construction, because a more spread-out set of solutions would appear better even if it does not dominate the true Pareto front. Please state the reference-front construction, the normalization procedure, and the reference-point selection, and verify that the reported IGD/HV improvements are not an artifact of the metric setup.
  3. [Sec. 4.2, Eq. (16)] The NP-hardness argument is not rigorous. The authors reduce to minimizing -R_UM over excitation current weights with 0 ≤ I_i ≤ 1 and a single constraint g(X') < N, and then assert this is a nonlinear knapsack problem that is NP-hard. This is not a valid reduction: no known NP-hard problem is mapped to this instance, and a continuous linear knapsack with one constraint is solvable in polynomial time. The statement that the original problem is 'more complex than f1'' does not establish NP-hardness. Please replace this subsection with a proper complexity argument or soften the claim to 'computationally challenging' with supporting justification.
  4. [Sec. 6.3 and Sec. 6.4] The experimental comparison does not include the closest prior algorithm, namely the authors' own evolutionary multi-objective DRL method [29], which the paper explicitly describes as the basis for EMOPPO-VLH. Since the contribution is framed as an improvement over [29] for a different scenario, omitting it from the baselines makes it impossible to verify the key novelty claim. Please add [29] as a baseline (adapted to the current setting with a clear description of the adaptation) or justify its exclusion in the text.
  5. [Sec. 5.5.1] The statement that 'the policy trained in this environment can be effectively applied to real-world scenarios' is asserted without validation. The simulation environment uses Rician fading, a Gauss-Markov user mobility model, a specific UAV energy model, and beamforming-gain formulas; no real-world measurements, channel traces, or hardware tests are provided. If simulation-to-real transferability is part of the contribution, it needs evidence or should be explicitly demoted to an assumption and limitation, rather than presented as a conclusion.
minor comments (7)
  1. [Sec. 1, contribution bullet] The contribution bullet refers to the proposed algorithm as 'MOPPO-PLE', but the algorithm is otherwise named EMOPPO-VLH throughout the paper; please correct the typo and keep the name consistent.
  2. [Sec. 6.4] The text compares 'EMOPPO-VLH with LSTM' with 'EMOPPO-GRU', but the baseline introduced in Sec. 6.3 is called 'EPPO with GRU'; the naming is inconsistent and should be unified.
  3. [Sec. 5.5.2 and Algorithm 4] Algorithm 4 uses the parameter name 'k' in its input list while the surrounding text defines 'kcan'; please align these notations.
  4. [Algorithms 3 and 4] The reference point Zref is used in Algorithm 3 and in the text, but its value or construction is never specified in the main text; please define it or point to a precise appendix section.
  5. [Sec. 6.4] The sentence stating that EMOPPO-VLH is better than 'the other five algorithms' is inaccurate because six baselines are listed (MOEA/D, MOPSO, EDDPG, ETD3, EPPO, and EPPO with GRU).
  6. [Eq. (4)] In the Rician fading PDF, the symbol Ω_UM is used both as the random variable and as its mean normalization constant, which is confusing; please use a separate notation for the mean, such as E[Ω].
  7. [Secs. 6.2, 6.5, and 7] The paper repeatedly refers to Appendix B, Appendix C, and Appendix D, but the provided arXiv version does not include these appendices; please ensure that the supplementary material is available to reviewers or summarize the key results in the main text.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the claimed contributions are algorithmic designs evaluated in the paper's own simulation; self-citations are contextual and not load-bearing.

full rationale

The paper's derivation chain is not circular. The system model (Eqs. (1)-(10)) defines physical quantities such as array factor, channel gain, SINR, rate, and UAV energy consumption from stated external models (Rician fading, Gauss-Markov mobility, rotary-wing propulsion). The MOP (Eqs. (13)-(15)) is directly constructed from these quantities as objectives and constraints, and the MOMDP formulation (Sec. 5.2) maps the same decision variables and objectives into state, action, and vector reward. The proposed EMOPPO-VLH (Algorithms 1-4) is fully specified in the paper, so its validity does not depend on an unverified prior theorem. References [11], [29], [52], and [58] are self-citations, but they provide model constants, a predecessor algorithm, and standard Pareto-dominance notation; none of these citations is invoked as the justification for the central performance claim. The Sec. 6.4 superiority claim rests on simulations against reimplemented baselines, not on a fitted parameter later renamed as a prediction. The lack of error bars, unreported random seed counts, the absent [29] baseline, and the unspecified IGD/HV reference-front construction are statistical and benchmarking weaknesses, which under Hard Rule 5 are correctness risks rather than circularity. No equation-level reduction of a claimed result to its own inputs is present.

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

The central claim rests on hand-chosen simulation parameters and standard channel and mobility assumptions, not on a new physical principle.

free parameters (5)
  • Reward penalty coefficients epsilon_1, epsilon_2, epsilon_3 = not specified
    Introduced in Eq. (19) to scale rewards when UAVs leave the area or collide; performance depends on their values and no sensitivity analysis is given.
  • Gauss-Markov memory parameter alpha_g = not specified in Sec. 6.1
    Controls temporal correlation of user mobility in Eq. (11); the paper does not report the value used or test sensitivity.
  • Evolutionary and RL hyperparameters = n=15, Gmax=100, nwarm=60, nevo=10, Bnum=50, Bsize=2, learning rate 0.0001, gamma 0.99, clip 0.2
    Chosen by hand in Sec. 6.1; the central performance comparison uses these values without ablation or tuning analysis.
  • Network sizes = LSTM 128 units, three FC layers of 256 units
    Network architecture chosen in Sec. 6.1; no ablation shows robustness to architecture changes.
  • Hyper-sphere selection constant c = not specified
    Used in Algorithm 4 as selection probability c/N_i; the value of c is not given and its effect is not studied.
assumptions (5)
  • domain assumption Array factor and beamforming gain equations from [30], [11] are correct and applicable to UAV swarms.
    Used as the physical basis for rate computation in Sec. 3.2.
  • domain assumption Rician fading with unit power and path-loss exponent 2 models the air-to-ground channel.
    Adopted in Sec. 3.2.2 without empirical validation.
  • domain assumption Gauss-Markov process describes realistic user mobility.
    Introduced in Sec. 3.4 as a 'memory-based random walk'; no real trajectory data support it.
  • standard math PPO clipped objective and LSTM gating equations from prior literature are standard and correctly applied.
    Used in Sec. 5.3 and 5.4.1.
  • ad hoc to paper The performance buffer strategy and hyper-sphere selection improve Pareto diversity.
    Introduced in Sec. 5.4.2 with only visual examples; no theoretical guarantee or extensive ablation.

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Cite this review

Pith. "Pith review of Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning." pith.science (2026). https://pith.science/paper/AM3S7RHX

@misc{pith2026250205824,
  author       = {Pith},
  title        = {Pith review of: Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AM3S7RHX}},
  note         = {Machine review of arXiv:2502.05824}
}
read the original abstract

Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances.

Figures

Figures reproduced from arXiv: 2502.05824 by the authors.

Figure 1
Figure 1. A UAV-enabled A2G communication system, where [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. The algorithmic framework of EMOPPO-VLH is initiated with a warm-up stage, designed to generate a high-quality [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. An illustrative example of performance buffer and [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: The schematic map illustrates the simulation setup. [PITH_FULL_IMAGE:figures/full_fig_p014_4.png]
Figure 5
Figure 5. Figure 5: Optimization results obtained by various algorithms Fig. 5. Optimization results obtained by various algorithm thllli() fbtid bdiff Fig. 5. Optimization results obtained by various algorithms hlll() fbd bdff [PITH_FULL_IMAGE:figures/full_fig_p015_5.png]
Figure 7
Figure 7. Figure 7: Comparison of convergences of the proposed algo￾rithms improved by LSTM and GRU, as well as the other baseline algorithms based on IGD metrics. in dynamic settings. Notably, MOEAs often invest signif￾icant computational effort in generating non-dominated policies, maki…
Figure 8
Figure 8. Figure 8: Comparison of convergences of the proposed algo [PITH_FULL_IMAGE:figures/full_fig_p016_8.png]

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Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.