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arxiv: 2605.23366 · v1 · pith:SGESCC6Unew · submitted 2026-05-22 · 📡 eess.SP

Stochastic Geometry Analysis of Uplink CUMA-Enabled Cellular Networks

Chenguang Rao , Kai-Kit Wong , Xusheng Zhu , Hanjiang Hong , Chan-Byoung Chae , Ross Murch This is my paper

Pith reviewed 2026-05-25 04:03 UTC · model grok-4.3

classification 📡 eess.SP
keywords uplinkCUMAstochastic geometrySIR coverage probabilitycellular networksport selectionfluid antenna systeminterference aggregation
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The pith

Uplink CUMA in multi-cell networks yields competitive or superior SIR coverage and rates under limited CSI by selecting ports on desired-link information alone.

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

The paper uses stochastic geometry to model uplink transmission in cellular networks equipped with compact ultra-massive arrays. It establishes that port selection based only on the desired user's channel state information, followed by superposition combining, produces coherent gain on the desired signal while interference from other cells remains largely non-coherent. Closed-form approximate expressions are derived for signal-to-interference ratio coverage probability, average user rate, and cell sum-rate, showing how these quantities vary with array size, port selection density, and network density. A reader would care because the scheme avoids the need for full interference channel knowledge that conventional multi-antenna methods require at scale.

Core claim

The paper derives a tight approximate expression for the SIR coverage probability of uplink CUMA-enabled multi-cell networks and characterizes the resulting average user rate and cell sum-rate. It shows that the scheme, which activates a subset of ports using only desired-link CSI and combines them by superposition, achieves performance that is competitive with and often exceeds that of conventional schemes when only practical CSI is available.

What carries the argument

The CUMA port-selection and superposition mechanism, which activates ports using only desired-link CSI to produce coherent desired-signal enhancement and largely non-coherent interference aggregation.

If this is right

  • Performance improves or degrades in quantifiable ways as the number of ports, selection ratio, and transmit power vary.
  • Average user rate and cell sum-rate exhibit specific scaling trends as base-station density increases.
  • The derived SIR coverage expression remains accurate across a range of practical network parameters, as confirmed by simulation.
  • CUMA offers a hardware-efficient uplink solution for large-scale cellular deployments where full interference CSI is unavailable.

Where Pith is reading between the lines

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

  • The same port-selection principle could be examined for downlink transmission or for scenarios with user mobility.
  • Hardware implementations could trade fewer RF chains against more ports while preserving the non-coherent interference benefit.
  • The scaling laws derived for densification could guide dimensioning rules for future ultra-dense networks.

Load-bearing premise

Inter-cell interference aggregates largely non-coherently because of the random superposition effect from port selection.

What would settle it

A measurement campaign in a multi-cell testbed that directly compares the coherence of aggregate interference under CUMA port selection against the non-coherent aggregation predicted by the model.

Figures

Figures reproduced from arXiv: 2605.23366 by Chan-Byoung Chae, Chenguang Rao, Hanjiang Hong, Kai-Kit Wong, Ross Murch, Xusheng Zhu.

Figure 1
Figure 1. Figure 1: Results for the CDF of the SIR. Λ = λ u / λ b 4 6 8 10 12 14 16 18 User average rate/(bits/s/Hz) 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Emprical results Analytical results η=2.7 η=2.4 [PITH_FULL_IMAGE:figures/full_fig_p009_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Results for the average rate vs. the BS-UE density rat [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 6
Figure 6. Figure 6: presents the cell sum rate against the FAS width W1. Similar to the case of varying N, CUMA exhibits pronounced fluctuations as W1 increases, which again stems from the os￾cillatory nature of the Bessel function in the correlation model. Compared to the variation with respect to N, the fluctuations with respect to W1 are even more significant, indicating that the aperture size has a strong impact on the ef… view at source ↗
Figure 7
Figure 7. Figure 7: shows the cell sum rate versus the path-loss exponent η. It is observed that the performance of all schemes improves FA width W1 (W1 =W2 ) 4 6 8 10 12 14 Cell sum rate/(bits/s/Hz) 0 5 10 15 CUMA ZF Full-CSI MMSE JAPBO η=2.7 η=2.4 [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
read the original abstract

Uplink cellular networks are interference-dominated but interference channel state information (CSI) is rarely available at scale. The emerging fluid antenna system (FAS) concept, which provides additional spatial degrees of freedom through multi-port reconfiguration, offers a promising alternative to CSI-intensive multi-antenna processing. Building on this concept, compact ultra-massive arrays (CUMA) exploit large-scale port selection with low implementation complexity. In each uplink transmission, CUMA activates a subset of ports based on only the desired-link CSI and combines the selected ports via simple superposition, yielding coherent enhancement of the desired user signal, while inter-cell interference aggregates largely non-coherently due to the random superposition effect. Consequently, CUMA is well suited to multi-cell uplink scenarios where CSI is limited. In this paper, we analyze uplink CUMA in multi-cell cellular networks using a stochastic geometry framework. We derive a tight approximate expression for the signal-to-interference ratio (SIR) coverage probability, and further characterize the average user rate and cell sum-rate. The analysis quantifies how key design parameters impact performance and reveals the scaling behavior with network densification. Simulation results validate the accuracy of the derived expressions and show that uplink CUMA achieves competitive, and often superior, performance relative to conventional schemes under practical CSI constraints, highlighting its potential as a low-complexity, hardware-efficient uplink solution for future large-scale cellular networks.

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 / 2 minor

Summary. The paper uses stochastic geometry to analyze uplink CUMA-enabled cellular networks, deriving a tight approximate SIR coverage probability expression along with average user rate and cell sum-rate characterizations. It concludes that CUMA achieves competitive and often superior performance relative to conventional schemes under practical CSI constraints, with analysis of design parameters and scaling with network densification, supported by simulations.

Significance. If the approximation holds, the work supplies a tractable framework for evaluating low-complexity port-selection uplink schemes in interference-limited multi-cell settings, with explicit quantification of densification scaling and limited-CSI benefits. The stochastic-geometry approach and simulation validation of expressions are strengths when the modeling assumptions are justified.

major comments (2)
  1. [Section III (SIR coverage derivation)] The derivation of the approximate SIR coverage probability (abstract and Section III) rests on the modeling choice that inter-cell interference aggregates largely non-coherently due to independent random port selection per cell. The manuscript provides no quantitative error bound or additional verification (e.g., via moment comparison or geometry-aware simulation) on residual coherence induced by port locations; this assumption is load-bearing for the Laplace-transform step and all subsequent rate comparisons.
  2. [Rate results section / figures] Table or figure reporting rate comparisons (e.g., the cell sum-rate results): the claimed superiority of CUMA is shown only under the non-coherent interference model; without a sensitivity check on coherence, the performance advantage cannot be considered robust.
minor comments (2)
  1. [Abstract] The abstract refers to a 'tight approximate expression' without citing the specific equation or theorem number where the approximation is stated and its tightness is quantified.
  2. [System model] Notation for port-selection probability and superposition weights should be introduced once with a clear reference to the system model equation.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major comment below and indicate the planned revisions.

read point-by-point responses

  1. Referee: [Section III (SIR coverage derivation)] The derivation of the approximate SIR coverage probability (abstract and Section III) rests on the modeling choice that inter-cell interference aggregates largely non-coherently due to independent random port selection per cell. The manuscript provides no quantitative error bound or additional verification (e.g., via moment comparison or geometry-aware simulation) on residual coherence induced by port locations; this assumption is load-bearing for the Laplace-transform step and all subsequent rate comparisons.

    Authors: The non-coherent aggregation follows directly from independent random port selection across cells, which randomizes the phases of interfering signals and justifies the Laplace-transform approach in Section III. The manuscript validates the resulting approximation through Monte Carlo simulations in Section IV that show close agreement with the derived SIR coverage probability. We acknowledge that an explicit quantitative bound on residual coherence is not provided. In the revision we will add a paragraph in Section III discussing the conditions under which the assumption holds and include supplementary simulation results that compare the model against controlled partial-coherence cases to quantify the approximation error. revision: partial

  2. Referee: [Rate results section / figures] Table or figure reporting rate comparisons (e.g., the cell sum-rate results): the claimed superiority of CUMA is shown only under the non-coherent interference model; without a sensitivity check on coherence, the performance advantage cannot be considered robust.

    Authors: The rate comparisons in the current figures and tables are obtained under the non-coherent model used throughout the analysis. To strengthen the claims, the revised manuscript will incorporate an additional sensitivity figure that evaluates cell sum-rate under varying levels of interference coherence, thereby confirming whether the reported performance advantages persist. revision: yes

Circularity Check

0 steps flagged

No circularity: standard stochastic geometry derivation with independent simulation validation

full rationale

The paper applies the established stochastic geometry framework to derive an approximate SIR coverage probability expression under the stated non-coherent interference model, then characterizes rates and scaling. No quoted equations or steps reduce by construction to fitted parameters, self-definitions, or load-bearing self-citations; the central claims rest on the modeling assumptions and are checked against independent Monte Carlo simulations rather than being tautological. This is the typical non-circular use of the framework.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review yields limited visibility into exact modeling choices; standard stochastic geometry point process assumptions appear to be the main background reliance.

axioms (1)
  • domain assumption Base stations distributed according to a homogeneous Poisson point process
    Standard modeling choice invoked for cellular network analysis in stochastic geometry.

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Reference graph

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