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REVIEW 5 major objections 5 minor 40 references

Optimizing Connectivity and Scheduling of Near/Far Field Users in Massive MIMO NOMA System

T0 review · 5 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This paper claims that clustering users into near-field and far-field groups and serving them with cognitive-NOMA or NOMA-inspired beamforming improves connectivity, sum rate, and energy efficiency in a massive MIMO-NOMA downlink.

desk verdict Incremental near/far NOMA-MIMO scheduling paper whose central claim of beating random beamforming is never actually shown, and whose near-field winner flips between the results and the conclusion. read the letter →

arxiv 2505.23259 v1 pith:GWUCXJQ2 submitted 2025-05-29 eess.SY cs.SY

Ziad Qais Al-Abbasi This is my paper
classification eess.SYcs.SY
keywords massiveMIMONOMAnear-fieldcommunicationsfar-fieldbeamforminguserclusteringschedulingenergyefficiency
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 argues that grouping users into separate near-field and far-field clusters, then serving each cluster with a dedicated beamforming scheme, improves connectivity, sum rate, and energy efficiency in a downlink massive MIMO-NOMA system. It proposes a cognitive-NOMA beamformer that prioritizes near-field users and a NOMA-inspired beamformer built from interference-adjusted channel vectors, and it claims both beat the CSI-free random beamforming baseline from the literature. It also proposes priority and dynamic scheduling for near-field users, fairness-based and joint scheduling for far-field users, and a gradual, fairness-oriented power-allocation algorithm. If these results hold, clustering plus tailored beamforming is a practical recipe for dense 6G-style user loads, with the caveat that the claimed gains assume accurate CSI at the base station.

What carries the argument

The load-bearing object is the cognitive-NOMA beamforming construction: near-field beams are built from an interference-adjusted vector, and far-field beams are obtained by projecting into the null space of the near-field channel space, so the two user groups share radio resources without interfering. This rests on a Rayleigh-distance classification that splits users into near-field (spherical-wavefront) and far-field (path-loss) regimes. The fairness-gradual (FG) power allocation then reallocates power in steps according to each user's achieved throughput while keeping total and per-user power constraints. Scheduling choice is the third component: priority versus dynamic for near-field users, fairness versus joint for far-field users.

What would settle it

Run the same comparison against random beamforming with bounded channel-estimation error, sweeping SNRs and antenna counts; if the proposed schemes' advantage over random beamforming vanishes, the central claim fails.

Watch

Extended reading notes

Core claim

The paper's central claim is that the near/far-field divide in a massive MIMO-NOMA downlink should be managed explicitly: users are separated by the Rayleigh distance, and near-field clusters are served with strongly focused beams while far-field clusters receive beams formed in the orthogonal complement of the near-field space. The cognitive-NOMA beamformer does exactly this: it prioritizes near-field users and lets far-field users occupy the spatial resources left over, which cancels cross-field interference under NOMA. The NOMA-inspired beamformer instead derives each user's beam from an interference-adjusted vector inspired by the SINR expression. In the reported simulations, both beamformers outperform random beamforming, dynamic scheduling outperforms priority scheduling for near-field users, joint scheduling outperforms fairness scheduling for far-field users, and clustering improves connectivity, sum rate, and energy efficiency relative to no clustering.

Load-bearing premise

Accurate channel state information for all users is available at the base station through feedback, and the Rayleigh-fading plus spherical-wavefront channel model faithfully represents the real propagation environment.

Editorial extensions

If this is right

  • Dense near/far-field deployments can be served by clustering users first and then applying a dedicated beamformer per cluster, rather than treating the whole cell with one NOMA strategy.
  • The cognitive-NOMA scheme offers a concrete interference-management rule: near-field beams take priority, far-field beams are placed in the null space of near-field channels, so cross-field interference is suppressed while NOMA shares resources.
  • In the paper's simulations, dynamic scheduling is the better choice for near-field users and joint scheduling is the better choice for far-field users.
  • The fairness-gradual power allocation provides a low-complexity, fairness-oriented alternative to optimal power allocation that still supports higher energy efficiency and sum rate.

Reading between the lines

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

  • Beyond the paper: the proposed beamformers' reliance on feedback CSI means their advantage over random beamforming is likely to shrink under imperfect or delayed channel estimates; a natural next test is robust beamforming with explicit estimation-error models.
  • Beyond the paper: the hybrid clustering step (spectral clustering refined by DBSCAN) is described conceptually but not parameterized; a direct extension would quantify how cluster count and density thresholds change the connectivity gain.
  • Beyond the paper: the same near/far clustering plus null-space projection recipe might transfer to cell-free massive MIMO or RIS-assisted networks, where the near/far disparity is equally pronounced.
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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 / 5 minor

Summary. The manuscript considers a downlink massive MIMO-NOMA system serving near-field and far-field users. It proposes separate clustering for the two user groups via a hybrid spectral/DBSCAN approach, two beamforming schemes (cognitive-NOMA and NOMA-inspired), several scheduling policies (priority, dynamic, joint, and fairness-based), and a fairness-gradual (FG) power-allocation heuristic. The central claims are that the two proposed beamforming schemes outperform random beamforming and that user clustering improves connectivity, sum rate, and energy efficiency. These claims are supported only by narrative descriptions of simulation figures in Sections 6.1-6.3 and are reiterated in the abstract and Section 7.

Significance. The problem is practically motivated, and the paper contains a concrete algorithmic idea: combining spectral clustering with DBSCAN for user grouping and a gradual, throughput-weighted power-allocation rule. The FG allocation is a reasonable fairness-oriented heuristic, and the paper makes an honest attempt to connect scheduling with near/far-field propagation. If the claimed gains over a genuine random-beamforming baseline and over no-clustering operation were supported by reproducible simulation evidence, the contribution would be an incremental but useful engineering result. As submitted, however, the empirical evidence is not in a verifiable form: no parameter table, no statistical error characterization, no numerical results, and no random-beamforming or optimal-power-allocation baseline appears anywhere in the reported figures or text. The paper also offers no analytical performance bounds or proofs, so the central claims rest entirely on the simulations, which are not adequately documented. The significance of the result can therefore not currently be assessed.

major comments (5)
  1. [Abstract, Sections 6.1-6.3, Section 7] The abstract and conclusion assert superiority over random beamforming, but Sections 6.1-6.3 report only comparisons between the two proposed beamforming schemes and between clustering and no-clustering cases. No random-beamforming curve, table, or numerical result is presented in the manuscript, despite Section 7 stating that 'a comparison against random beamforming is included.' Similarly, Section 2 promises a comparison with an optimal power-allocation strategy, but such a baseline is absent from all reported results. These missing baselines are load-bearing because the paper's central claim is explicitly the superiority of both proposed schemes over random beamforming.
  2. [Section 6.1 vs Section 7] Section 6.1 states that 'the cognitive-NOMA beamforming shows a higher sum rate for the near filed users than the case of NOMA inspired beamforming scheme,' while Section 7 states that 'The near field users result also showed better results using NOMA inspired beamforming as compared to the cognitive-NOMA beamforming.' These statements are mutually contradictory. The contradiction matters because Section 6.3 justifies adopting cognitive-NOMA for the clustering experiments on the basis of the earlier comparison; the inconsistency therefore propagates into the clustering conclusions and makes the reported winner unreliable.
  3. [Section 4.1, Eqs. (9)-(12)] The NOMA-inspired beamforming vector is not defined against an independent criterion. In Eq. (10), the quantity g_i is formed from the same channel inner products and interference-plus-noise terms that appear in the SINR expression (4), so the beamformer is constructed to magnify the very metric used to evaluate it. This is not full circularity because no parameter is fitted, but it means the comparison does not test a neutral design principle. Furthermore, the notation in Eqs. (9)-(12) is left underspecified: the roles of h_i, h_k, D, inv(.), the SIC/CSI imperfection parameter, and the normalization are not fully defined, so the schemes are not implementable from the text. The authors should state an explicit beamforming objective or an independent comparison protocol and fully define all quantities.
  4. [Section 6, Figures 1-10] The simulation methodology is not reproducible. Section 6 states that parameters were chosen to reflect typical 6G conditions, but no parameter table or list is given; the number of users, transmit SNR/power ranges, number of Monte Carlo runs, cluster sizes, the FG gradual weight ℓ, and the DBSCAN/spectral-clustering hyperparameters (epsilon, minimum samples, number of spectral clusters) are all absent. No error bars, confidence intervals, or statistical tests are reported, and the results are described only narratively from the figures. This makes it impossible to verify the claimed trends or to evaluate whether the differences are significant. The Data Availability Statement's assertion that 'no new data was generated or analyzed' is also inconsistent with the simulation results reported in this section.
  5. [Equation (1) and near/far classification] The near/far-field classification is foundational to the entire system model, yet Eq. (1) is typeset as unreadable fragments and the reference Rayleigh-distance threshold used for classification is not stated in a usable form. Because all subsequent results depend on which users are assigned to the near field and far field, this omission makes the system model itself incompletely specified and prevents replication of the user-classification step.
minor comments (5)
  1. [Section 6.1] The dynamic-scheduling SINR calculation is said to use Eq. (10), but the relevant expression is Eq. (18); this cross-reference error should be corrected.
  2. [Figures 3-4 placement] The caption for Fig. 4 appears before the caption for Fig. 3 in the text, which makes the discussion of the figures difficult to follow.
  3. [Table II (FG algorithm)] The gradual power-update rule in Table II is written as an incomplete expression; the assignment, the stopping criterion, and the normalization step need to be stated as complete equations.
  4. [Notation throughout] Several symbols are used without definition or with inconsistent notation, including P_total, P_f, P_n, P_u^t, K, B, alpha, sigma^2, and the indices in Eqs. (2) and (6); a unified notation table is needed.
  5. [Section 7] The sentence comparing the two beamforming schemes states that 'the latter requires less complex mathematical operations than the latter,' which is garbled and does not clearly indicate which scheme is more complex.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the proposed beamforming, scheduling, clustering, and power-allocation schemes are evaluated through their own channel models without parameter fitting or self-citation load-bearing, although the claimed random-beamforming baseline is absent from the reported simulations.

full rationale

The derivation chain is self-contained. Section 4.1 defines the NOMA-inspired beamformer w_n = g_n / ||g_n|| with g_n built from interference-plus-noise-scaled channel terms (Eqs. 9-10), which is an SINR-maximizing design rule: the objective (SINR in Eq. 4) is used to construct the beamformer. This is standard optimization, not a fitted parameter or a renamed result. No parameter in Eqs. (9)-(14) is fitted to the simulated curves, and the proposed schemes are compared against each other and (claimedly) against random beamforming from the literature (Refs. 22-25), which is independent external work. The FG power allocation (Table II) is an iterative proportional-fairness rule whose inputs are per-user rates and whose output is a power vector; evaluating that output with the same rate expression is self-consistent rather than circular. The clustering comparison (Section 6.3, Figs. 5-10) uses the same cognitive-NOMA beamforming for both clustered and unclustered cases, so the comparison isolates the clustering variable. Self-citations (Refs. 8, 28, 30) appear only in the related-work survey and do not carry any load-bearing uniqueness or correctness claim. A genuine weakness is that Section 6 does not actually plot or tabulate a random-beamforming curve despite the abstract and conclusion claiming such a comparison; that is an unsupported empirical claim, not a circularity, and does not affect the circularity score under the rubric.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper's central claims rest on a small set of domain assumptions and on several unspecified tuning parameters. The beamforming equations are heuristics built from the same SINR expressions used for evaluation; no independent benchmark or code is provided. No new physical entities are introduced.

free parameters (3)
  • Gradual weight parameter l = unspecified
    Used in the FG power allocation update in Table II; no value or sensitivity analysis is reported, and the reported sum rate and energy efficiency depend on it.
  • Reference Rayleigh distance threshold = unspecified
    Defines the near/far split in Section 3; the simulation choice of this threshold determines which users are clustered as near or far, but its value is not given.
  • Clustering hyperparameters (spectral cluster count, DBSCAN epsilon and minimum samples) = unspecified
    The hybrid clustering in Section 5.1 is described without parameter values, so the clustering comparison cannot be reproduced or checked for tuning bias.
assumptions (4)
  • domain assumption Perfect or near-perfect CSI at the base station for all users
    Section 4.1 states the beamforming expression depends on CSI availability, assumed known through feedback; the null-space projection in equations (11)-(14) relies on accurate CSI to cancel near/far interference.
  • domain assumption Rayleigh flat fading and spherical-wavefront near-field channel model
    Section 3 assumes Rayleigh flat fading for all users and spherical propagation for near-field users; the reported SINR and beamforming results depend on these channel models.
  • domain assumption Successive interference cancellation at NOMA receivers
    The SINR expressions in equations (4), (7), and (17)-(20) assume SIC ordering and cancellation; no finite-SIC error model is analyzed beyond a placeholder in equation (12).
  • standard math Standard matrix operations and spectral/DBSCAN clustering algorithms work as assumed
    The null-space projection and hybrid clustering are invoked without proof; these are standard tools, so this axiom is reasonable.

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Cite this review

Pith. "Pith review of Optimizing Connectivity and Scheduling of Near/Far Field Users in Massive MIMO NOMA System." pith.science (2026). https://pith.science/paper/GWUCXJQ2

@misc{pith2026250523259,
  author       = {Pith},
  title        = {Pith review of: Optimizing Connectivity and Scheduling of Near/Far Field Users in Massive MIMO NOMA System},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GWUCXJQ2}},
  note         = {Machine review of arXiv:2505.23259}
}
read the original abstract

It is envisioned that the next generations of wireless communication environment will be characterized with dense traffic demand due to the prediction that there will be large numbers of active users. Hence, it is important to find a solution to deal with such dense numbers of users. This paper investigates optimizing the connectivity and users scheduling to improve the performance of near and far field users in a downlink, multiuser, massive MIMO-NOMA system. For the considered system model, combining NOMA side by side with massive MIMO offers a great opportunity to exploit the available radio resources and boost the overall system efficiency. The paper proposes separate clustering of near field users and far field users. It also proposes using a beamforming scheme to separately serve the users within each cluster. However, NOMA is proposed to be applied among all users to boost resource sharing. In particular, a cognitive-NOMA beamforming scheme and NOMA themed beamforming are proposed to serve the users within each cluster, and they are compared against random beamforming from literature. Simulation results show that both of the proposed beamforming schemes proved their superiority as compared to random beamforming. Several scheduling techniques were also considered in this paper to examine possible solutions for boosting the system performance considered, namely, priority, joint, dynamic, and fairness-based scheduling techniques for both near field and far field users. The paper also proposes a suboptimal, fairness aiming and gradual allocation approach for allocating the transmission power among the users. The results show that user-clustering offers better connectivity and scheduling performance than the case where no clustering is applied.

Figures

Figures reproduced from arXiv: 2505.23259 by the authors.

Figure 2
Figure 2. The achieved energy efficiency of near field users with the proposed FG power allocation, for the priority and dynamic scheduling cases using both proposed beamforming schemes [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗
Figure 3
Figure 3. The achieved sum rate of far field users with the proposed FG power allocation, for the fairness based and joint scheduling cases using both proposed beamforming schemes [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
Figure 4
Figure 4. The achieved energy efficiency of far field users with the proposed FG power allocation, for the fairness based and joint scheduling cases using both proposed beamforming schemes. This is evidence that the proposed NOMA beamforming scheme could be adopted for far field users and the proposed cognitive-NOMA scheme to be adopted for near field users. 6.3 Simulation Results Part 3: The Proposed cognitive￾NOMA beamformi… view at source ↗

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