pith. sign in

arxiv: 2606.29269 · v1 · pith:QRMWPTCSnew · submitted 2026-06-28 · 💻 cs.IT · eess.SP· math.IT

Proportional-Fair Joint User Grouping and Power Allocation for Uplink NOMA-ISAC

Yaxuan Luo This is my paper

Pith reviewed 2026-06-30 02:44 UTC · model grok-4.3

classification 💻 cs.IT eess.SPmath.IT
keywords NOMA-ISACproportional fair schedulinguser groupingpower allocationuplinkfairnessresource allocationintegrated sensing and communication
0
0 comments X

The pith

A proportional-fair method for user grouping and power allocation raises long-term fairness in uplink NOMA-ISAC with only modest sum-rate cost.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper targets long-term fairness in uplink NOMA-ISAC, where sum-rate maximization tends to starve weak users over time. It introduces PF-JUGPA, which first pre-selects users with a metric that mixes current rate estimates and historical averages, then solves a weighted sum-rate maximization whose weights are set inversely to each user's past service. Simulations show this raises the Jain fairness index and weak-user rates relative to pure sum-rate and round-robin baselines while incurring only a small total-rate penalty. The work therefore claims that folding historical service into both selection and allocation produces a workable balance among throughput, equity, and sensing performance.

Core claim

PF-JUGPA first pre-selects users via a PF metric combining instantaneous rate proxies and historical averages, then performs fairness-aware grouping and power allocation by maximizing a weighted sum rate whose weights are inversely proportional to historical service rates. Simulation results show that PF-JUGPA significantly improves the Jain fairness index and weak-user average rates with only a modest sum-rate loss compared to sum-rate-oriented and round-robin baselines.

What carries the argument

PF-JUGPA two-stage procedure: PF-metric user pre-selection followed by weighted sum-rate maximization with weights inversely proportional to historical service rates.

If this is right

  • Incorporating historical service rates into both scheduling and resource allocation improves the Jain fairness index.
  • Weak-user average rates increase while total sum rate declines only modestly.
  • The same weighting produces an effective throughput-fairness-sensing tradeoff in uplink NOMA-ISAC.
  • The two-stage structure outperforms both sum-rate maximization and round-robin baselines on equity metrics.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The historical averaging window may need adaptation when user mobility increases, otherwise the fairness weights could become stale.
  • Because power is allocated under fairness weights, sensing accuracy could shift if the ISAC waveform design is sensitive to the resulting power profile.
  • Similar history-based weighting might prevent starvation in other multi-user ISAC or NOMA settings beyond the uplink case examined here.

Load-bearing premise

The assumption that a two-stage procedure of PF pre-selection followed by weighted sum-rate maximization produces stable long-term fairness when channel statistics, mobility patterns, and sensing requirements differ from the simulation setup.

What would settle it

A simulation or field test in which channel fading statistics or user mobility patterns are altered substantially from the paper's setup and the fairness gains over baselines disappear or reverse.

Figures

Figures reproduced from arXiv: 2606.29269 by Yaxuan Luo.

Figure 1
Figure 1. Figure 1: Sum rate versus pc,max. selected users. 2) RR+OA: Users are scheduled in a round￾robin manner, and then resource allocation is performed over the scheduled set. 3) PF+Fixed: PF-based pre-scheduling is adopted, while fixed user grouping and equal power allo￾cation are used in the second stage. 4) PF-JUGPA: The proposed scheme, which combines PF-based pre-scheduling with fairness-aware joint user grouping an… view at source ↗
Figure 3
Figure 3. Figure 3: Bottom-20% user rate versus ϕmin. when higher sensing quality is required. Together with the communication and fairness results discussed above, [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
read the original abstract

This letter addresses long-term fairness in uplink non-orthogonal multiple access integrated sensing and communication (NOMA-ISAC) systems. Existing resource allocation schemes that maximize instantaneous sum rate often favor strong users, leaving historically underserved users with poor long-term throughput. We propose PF-JUGPA, a proportional-fair scheduling based joint user grouping and power allocation method. PF-JUGPA first pre-selects users via a PF metric combining instantaneous rate proxies and historical averages, then performs fairness-aware grouping and power allocation by maximizing a weighted sum rate whose weights are inversely proportional to historical service rates. Simulation results show that PF-JUGPA significantly improves the Jain fairness index and weak-user average rates with only a modest sum-rate loss compared to sum-rate-oriented and round-robin baselines. The findings confirm that embedding long-term service history into both scheduling and resource allocation yields an effective throughput--fairness--sensing tradeoff in uplink NOMA-ISAC.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes PF-JUGPA, a two-stage proportional-fair method for joint user grouping and power allocation in uplink NOMA-ISAC systems. The first stage pre-selects users using a PF metric that incorporates instantaneous rate proxies and historical averages. The second stage performs grouping and power allocation by maximizing a weighted sum-rate objective with weights inversely proportional to historical service rates. The central claim, supported by simulations, is that this approach improves the Jain fairness index and weak-user average rates compared to sum-rate maximization and round-robin baselines, at the cost of a modest sum-rate reduction, while maintaining a throughput-fairness-sensing tradeoff.

Significance. If the simulation results prove robust, the work provides a concrete heuristic for embedding long-term fairness considerations into resource allocation for NOMA-ISAC, addressing a practical issue in systems where instantaneous optimization disadvantages weak users. This could be relevant for designing fair and efficient integrated sensing-communication networks.

major comments (2)
  1. [Abstract and simulation results] Abstract and simulation results section: the reported improvements in Jain fairness index and weak-user average rates supply no information on simulation parameters, number of Monte-Carlo runs, statistical significance, or sensitivity to the historical averaging window. This information is load-bearing for verifying that the central empirical claim is robust rather than an artifact of the fixed setup.
  2. [Method description] Method description (two-stage PF procedure): the weighting uses historical rates updated outside each optimization step, yet no convergence analysis or sensitivity study is supplied to show that the fairness gains remain stable when channel statistics, mobility patterns, or sensing requirements differ from the simulated ensemble. This assumption underpins the long-term fairness claim.
minor comments (1)
  1. Provide explicit definitions and update equations for the PF metric and historical rate averaging in the main text to support reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive comments. The points raised are valid and will help improve the clarity and robustness of the empirical claims. We address each major comment below and indicate the planned revisions.

read point-by-point responses

  1. Referee: [Abstract and simulation results] Abstract and simulation results section: the reported improvements in Jain fairness index and weak-user average rates supply no information on simulation parameters, number of Monte-Carlo runs, statistical significance, or sensitivity to the historical averaging window. This information is load-bearing for verifying that the central empirical claim is robust rather than an artifact of the fixed setup.

    Authors: We agree that the simulation results section lacks sufficient detail on these aspects. In the revised version we will explicitly report the full set of simulation parameters, the number of Monte-Carlo runs, any statistical significance measures used, and a sensitivity analysis with respect to the historical averaging window length. These additions will be placed in the simulation results section to allow readers to assess robustness. revision: yes

  2. Referee: [Method description] Method description (two-stage PF procedure): the weighting uses historical rates updated outside each optimization step, yet no convergence analysis or sensitivity study is supplied to show that the fairness gains remain stable when channel statistics, mobility patterns, or sensing requirements differ from the simulated ensemble. This assumption underpins the long-term fairness claim.

    Authors: PF-JUGPA is presented as a practical heuristic rather than a provably convergent iterative algorithm; the historical-rate weights are computed from long-term averages and held fixed during each per-slot optimization. We acknowledge that a dedicated sensitivity study across varied channel statistics, mobility, and sensing constraints would strengthen the long-term fairness claim. In revision we will add a brief sensitivity discussion and, space permitting, additional simulation curves for different averaging windows and mobility levels. A formal convergence analysis is not applicable to the non-iterative two-stage procedure and will not be added. revision: partial

Circularity Check

0 steps flagged

No significant circularity in PF-JUGPA heuristic

full rationale

The paper proposes an explicit two-stage heuristic (PF-metric pre-selection followed by weighted sum-rate maximization using historical rates) whose purpose is to enforce long-term fairness by design. Fairness metrics are evaluated via simulation against independent baselines rather than derived or fitted from the same quantities. No self-citations, uniqueness theorems, ansatzes, or renamings of known results are invoked in a load-bearing way; the central claims rest on empirical comparison, not reduction to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review is based solely on the abstract; no explicit free parameters, axioms, or invented entities are stated. The method implicitly assumes standard wireless models (perfect CSI, additive white Gaussian noise, etc.) that are not enumerated.

pith-pipeline@v0.9.1-grok · 5689 in / 1129 out tokens · 33208 ms · 2026-06-30T02:44:19.283058+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

10 extracted references · 2 canonical work pages

  1. [1]

    Integrated sensing and communications: Toward dual- functional wireless networks for 6G and beyond,

    F. Liu et al., “Integrated sensing and communications: Toward dual- functional wireless networks for 6G and beyond,”IEEE J. Sel. Areas Commun., vol. 40, no. 6, pp. 1728–1767, Jun. 2022

    2022

  2. [2]

    A survey on fundamental limits of integrated sensing and communication,

    A. Liu et al., “A survey on fundamental limits of integrated sensing and communication,”IEEE Commun. Surveys Tuts., vol. 24, no. 2, pp. 994–1034, 2nd Quart. 2022

    2022

  3. [3]

    Sensing as a service in 6G perceptive networks: A unified framework for ISAC resource allocation,

    F. Dong, F. Liu, Y . Cui, W. Wang, K. Han, and Z. Wang, “Sensing as a service in 6G perceptive networks: A unified framework for ISAC resource allocation,”IEEE Trans. Wireless Commun., vol. 22, no. 5, pp. 3522–3536, May 2023

    2023

  4. [4]

    A survey on non-orthogonal multiple access for 5G networks: Research challenges and future trends,

    Z. Ding, X. Lei, G. K. Karagiannidis, R. Schober, J. Yuan, and V . K. Bhargava, “A survey on non-orthogonal multiple access for 5G networks: Research challenges and future trends,”IEEE J. Sel. Areas Commun., vol. 35, no. 10, pp. 2181–2195, Oct. 2017

    2017

  5. [5]

    On the study of non-orthogonal multiple access (NOMA)-assisted integrated sensing and communication (ISAC),

    L. Sun, Z. Zhao, S. Wang, Z. Ding, and M. Peng, “On the study of non-orthogonal multiple access (NOMA)-assisted integrated sensing and communication (ISAC),”IEEE Trans. Commun., vol. 72, no. 11, pp. 7278–7293, Nov. 2024

    2024

  6. [6]

    NOMA for integrat- ing sensing and communications towards 6G: A multiple access perspective,

    Z. Wang, Y . Liu, X. Mu, and Z. Ding, “NOMA for integrat- ing sensing and communications towards 6G: A multiple access perspective,”IEEE Wireless Commun., early access, 2023, doi: 10.1109/MWC.015.2200559

    work page doi:10.1109/mwc.015.2200559 2023

  7. [7]

    Rendezvous of ISAC and NOMA: Progress and prospects of next-generation multiple access,

    A. Nasser, A. Abdallah, A. Celik, and A. M. Eltawil, “Rendezvous of ISAC and NOMA: Progress and prospects of next-generation multiple access,”IEEE Commun. Standards Mag., vol. 8, no. 2, pp. 44–51, Jun. 2024

    2024

  8. [8]

    Joint user grouping and power allocation for uplink NOMA-ISAC systems,

    H. Liu, E. Alsusa, and A. Al-Dweik, “Joint user grouping and power allocation for uplink NOMA-ISAC systems,”TechRxiv, Aug. 2025, doi: 10.36227/techrxiv.175624042.25456081/v1

    work page doi:10.36227/techrxiv.175624042.25456081/v1 2025

  9. [9]

    Multi-user proportional fair scheduling for uplink non-orthogonal multiple access (NOMA),

    M. Mehrnoush, S. Roy, and V . Sathya, “Multi-user proportional fair scheduling for uplink non-orthogonal multiple access (NOMA),” in Proc. IEEE VTC-Spring, Jun. 2018, pp. 1–5

    2018

  10. [10]

    Maximizing the value of service provisioning in multi-user ISAC systems through fairness guaranteed collaborative resource allocation,

    B. Li, X. Wang, and F. Fang, “Maximizing the value of service provisioning in multi-user ISAC systems through fairness guaranteed collaborative resource allocation,”IEEE J. Sel. Areas Commun., vol. 42, no. 9, pp. 2243–2258, Sep. 2024

    2024