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arxiv: 2606.01941 · v2 · pith:DH4DT7IOnew · submitted 2026-06-01 · 📡 eess.SY · cs.SY· eess.SP

Secure RSMA-based Visible Light Networks under Spatial Correlation

Hung K. Hoang , Chuyen T. Nguyen , Thang K. Nguyen , Thanh V. Pham , Anh T. Pham This is my paper

Pith reviewed 2026-06-28 13:51 UTC · model grok-4.3

classification 📡 eess.SY cs.SYeess.SP
keywords RSMAvisible light communicationspatial correlationsecrecy sum rateclustering strategyinternal eavesdroppingoptimization
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The pith

A channel similarity reduction clustering strategy overcomes the secrecy performance ceiling caused by high spatial correlation in RSMA-based visible light networks.

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

This paper studies the secrecy sum rate of rate-splitting multiple access visible light communication systems that face internal eavesdroppers among legitimate users. It formulates a non-convex optimization problem to maximize the secrecy sum rate and solves it using the convex-concave procedure and semidefinite relaxation. The work centers on channel similarity as a metric for spatial correlation and its degrading effect on performance. To address high correlation, the authors introduce a clustering strategy that minimizes channel similarity and thereby restores degrees of freedom. Numerical results indicate that this clustering approach outperforms existing baselines across different correlation levels.

Core claim

The authors establish that the proposed channel similarity reduction clustering strategy significantly outperforms existing baselines, effectively overcoming the secrecy performance ceiling caused by high spatial correlation.

What carries the argument

The channel similarity reduction (CSR) clustering strategy, which groups users to minimize channel similarity and restores the system's degrees of freedom lost to spatial correlation.

If this is right

  • The CCCP and SDR algorithms produce effective solutions to the non-convex secrecy sum rate maximization problem.
  • Minimizing channel similarity through clustering restores degrees of freedom and raises achievable secrecy sum rate under high spatial correlation.
  • The CSR-clustering strategy removes the performance ceiling that high spatial correlation otherwise imposes.
  • Performance advantages hold across various levels of channel similarity in numerical evaluations.

Where Pith is reading between the lines

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

  • Similar clustering based on channel similarity could extend to other multi-user visible light or RF systems facing correlation-induced secrecy limits.
  • Testing the approach on hardware prototypes would check whether numerical gains translate to real VLC deployments.
  • Incorporating external eavesdroppers into the model could show whether the clustering retains its advantage or requires adjustment.

Load-bearing premise

The non-convex optimization problem can be reliably solved via CCCP and SDR without the solutions deviating substantially from the true optimum, and the channel similarity metric fully captures the secrecy impact of spatial correlation.

What would settle it

If exhaustive search on small instances shows the CCCP or SDR solutions achieve substantially lower secrecy sum rate than the true optimum, or if measured high-correlation VLC channels show no SSR gain from the clustering, the central performance claims would not hold.

Figures

Figures reproduced from arXiv: 2606.01941 by Anh T. Pham, Chuyen T. Nguyen, Hung K. Hoang, Thang K. Nguyen, Thanh V. Pham.

Figure 1
Figure 1. Figure 1: Schematic diagram of MU-MISO RSMA-based VLC system. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Different layouts of LED transmitters. For random precoder initialization, the CCCP approach exhibits slightly faster convergence than the CCCP-SDR method in the small-scale system (NT = 4). However, this trend reverses as the system scales up to NT = 16. Specifically, to reach a relative error of 10−3 , both algorithms require approximately 7–8 iterations for NT = 4, while for larger systems (NT = 9, 16),… view at source ↗
Figure 3
Figure 3. Figure 3: Convergence behaviors of proposed algorithms for different numbers of [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figure 6
Figure 6. Figure 6: SSR versus Average Optical Power and CS levels. [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 5
Figure 5. Figure 5: SSR and its rate components versus the number of [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: SSR and CS versus receiver FOV for different [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Pareto front of NSGA-II and exhaustive search. [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗
Figure 10
Figure 10. Figure 10: SSR versus Average Optical Power for different [PITH_FULL_IMAGE:figures/full_fig_p013_10.png] view at source ↗
read the original abstract

This paper investigates the secrecy sum rate (SSR) of rate-splitting multiple access (RSMA)-based visible light communication (VLC) systems considering internal eavesdropping, where legitimate users may intercept private data intended for others. We formulate an optimization problem to maximize the SSR of the system, which is inherently non-convex due to the complex coupling of the objective function and constraints. To this end, two different approaches based on the convex-concave procedure (CCCP) and semidefinite relaxation (SDR) are leveraged to solve the non-convex parameterized problem. A central focus of this work is the investigation of channel similarity (CS), which serves as a metric for quantifying spatial correlation, and its impact on SSR performance. To mitigate the performance degradation caused by high spatial correlation, we propose a channel similarity reduction (CSR) clustering strategy that proactively minimizes CS to restore the system's degrees of freedom (DoF). Numerical results are provided to demonstrate the performance of the two proposed algorithms under various levels of CS. More importantly, the findings reveal that our proposed CSR-clustering strategy significantly outperforms existing baselines, effectively overcoming the secrecy performance ceiling caused by high spatial correlation.

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 studies secrecy sum rate (SSR) maximization in RSMA-based VLC systems with internal eavesdropping and spatial correlation. It formulates a non-convex SSR optimization problem, solves it with CCCP and SDR, introduces a channel similarity reduction (CSR) clustering strategy to counteract high spatial correlation, and reports numerical results claiming that CSR-clustering significantly outperforms baselines and overcomes the secrecy performance ceiling.

Significance. If the numerical gains are shown to be robust to solver artifacts, the work would offer a concrete clustering method for restoring DoF in correlated VLC channels, which is relevant for secure indoor networks. The combination of RSMA with explicit spatial-correlation handling is a timely direction, but the significance hinges on whether the reported SSR improvements reflect true system behavior rather than properties of the local solvers.

major comments (2)
  1. [Optimization and solution approach] The non-convex SSR maximization (formulated after the system model) is solved exclusively via CCCP and SDR; no duality gap bounds, small-instance exhaustive-search comparisons, or convergence analysis to global optimality are supplied. Because the central claim—that CSR-clustering yields higher SSR than baselines under high CS—rests entirely on these numerical values, the absence of optimality-gap control is load-bearing.
  2. [Channel similarity and clustering strategy] The channel-similarity metric used to drive CSR clustering is asserted to fully capture the secrecy impact of spatial correlation, yet the paper provides no sensitivity study showing that alternative correlation measures (e.g., mutual information or chordal distance) produce materially different clustering decisions or SSR rankings.
minor comments (2)
  1. [Abstract] The abstract states that “two different approaches based on CCCP and SDR” are used, but the text does not clarify whether both are applied to the same clustering formulation or whether one is reserved for the clustered case.
  2. [Numerical results] Figure captions and axis labels for the SSR-versus-CS curves should explicitly state the number of Monte-Carlo realizations and whether error bars represent standard deviation or 95 % confidence intervals.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and outline revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Optimization and solution approach] The non-convex SSR maximization (formulated after the system model) is solved exclusively via CCCP and SDR; no duality gap bounds, small-instance exhaustive-search comparisons, or convergence analysis to global optimality are supplied. Because the central claim—that CSR-clustering yields higher SSR than baselines under high CS—rests entirely on these numerical values, the absence of optimality-gap control is load-bearing.

    Authors: We acknowledge that the absence of global optimality guarantees is a limitation for the central numerical claims. Deriving tight duality gap bounds for this problem is intractable given the non-convex structure. However, we will add a convergence analysis for the CCCP procedure and exhaustive-search validation on small-scale instances (e.g., 2-3 users) in the revision. Relative SSR gains of CSR clustering remain consistent across random initializations and both solvers, indicating that the reported improvements are not artifacts of a single local solution. revision: partial

  2. Referee: [Channel similarity and clustering strategy] The channel-similarity metric used to drive CSR clustering is asserted to fully capture the secrecy impact of spatial correlation, yet the paper provides no sensitivity study showing that alternative correlation measures (e.g., mutual information or chordal distance) produce materially different clustering decisions or SSR rankings.

    Authors: The chosen channel similarity metric is derived directly from the VLC channel model and its effect on the secrecy rate expressions under RSMA. To address the concern, we will include a sensitivity study in the revised manuscript comparing clustering decisions and SSR rankings under chordal distance and mutual-information-based alternatives, confirming that CSR clustering yields comparable or superior performance. revision: yes

Circularity Check

0 steps flagged

No circularity: optimization solved by standard methods; performance evaluated via external numerical simulation.

full rationale

The paper formulates a non-convex SSR maximization problem, applies standard CCCP and SDR solvers, defines channel similarity as an input metric, and proposes CSR clustering whose efficacy is assessed through numerical results against baselines. No equations, fitted parameters, or self-citations are shown to reduce any claimed performance gain to a definitional identity or input by construction. The derivation chain remains self-contained against the simulation benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no information on free parameters, background axioms, or new postulated entities.

pith-pipeline@v0.9.1-grok · 5754 in / 999 out tokens · 28685 ms · 2026-06-28T13:51:09.744962+00:00 · methodology

discussion (0)

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