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REVIEW 3 major objections 5 minor 65 references

Robust Max-Min Fair Beamforming Design for Rate Splitting Multiple Access-aided Visible Light Communications

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This paper establishes closed-form rate lower bounds for RSMA-aided VLC under imperfect CSIT and gives a robust max-min fair beamforming algorithm that outperforms SDMA and NOMA.

desk verdict The private-stream rate bound that drives the RSMA gains is not proved—(20d) misapplies the entropy power inequality—so the paper's headline MMF comparisons are unsupported as written. read the letter →

arxiv 2411.17056 v2 pith:NZR3GBKQ submitted 2024-11-26 cs.IT eess.SPmath.IT

Zhengqing Qiu , Yijie Mao , Shuai Ma , Bruno Clerckx This is my paper · ORCID
classification cs.ITeess.SPmath.IT
keywords ratesplittingmultipleaccessvisiblelightcommunicationrobustbeamformingmax-minfairnessimperfectchannelstateinformationsemidefiniterelaxationCCCPentropypowerinequality
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 asks whether rate-splitting multiple access (RSMA), which has proven robust in radio-frequency networks, can deliver its usual gains in visible-light communication (VLC) when the transmitter's channel knowledge is imperfect. It derives closed-form lower bounds on the achievable rates of the common and private streams, using a capacity lower-bound distribution suited to intensity modulation and direct detection with peak optical, average optical, and electrical power constraints. On top of those bounds, it formulates a max-min fairness beamforming problem that maximizes the worst-case user rate, and solves it with an algorithm that combines semidefinite relaxation, CCCP, the S-lemma, and a penalty method for rank-one recovery. Numerical results report that the proposed RSMA design outperforms SDMA and NOMA in both underloaded and overloaded VLC networks, with the largest margins in overloaded and uncertain-channel regimes.

What carries the argument

The load-bearing objects are the two rate lower bounds (18) and (21). The common-stream bound (18) is obtained from the entropy power inequality on the received signal entropy and the entropy inequality on the noise-plus-interference term, followed by a closed-form capacity lower bound for the optical intensity channel with input distribution (15). The private-stream bound (21) is the analogous expression after imperfect SIC, in which the residual common-stream term proportional to the channel estimation error $\Delta h_k$ appears in the numerator and denominator. These closed forms convert an intractable mutual-information maximization into difference-of-convex constraints that the paper then handles with semidefinite relaxation, the S-lemma for the semi-infinite uncertainty constraints, CCCP linearization, and a penalty term $\rho\sum_i(\operatorname{Tr}(P_i)-\|P_i\|_2)$ to drive the relaxed matrices to rank one.

What would settle it

Take a one-user or two-user instance, choose any feasible channel-error vector from the uncertainty set, and check numerically whether expression (21) is really bounded above by the true mutual information in (20c); a single instance where the claimed inequality reverses, or where the entropy power step (20d) fails, would invalidate the bound and with it the MMF comparison.

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Extended reading notes

Core claim

The paper's central claim is that RSMA-aided VLC is not only feasible under imperfect CSIT but superior to existing SDMA and NOMA benchmarks. The supporting discovery is a pair of closed-form rate lower bounds: expression (18) for the common stream and expression (21) for the private stream after successive interference cancellation, both derived from entropy power and entropy inequalities under a truncated exponential-quadratic input distribution and three practical optical and electrical power constraints. Combining these bounds with a robustness model in which each user's channel lies in a bounded uncertainty region, the paper formulates the MMF rate maximization and provides an iterative algorithm that returns rank-one beamformers and yields a monotonically rising objective. If the bounds and algorithm are correct, the consequence is that RSMA can robustly manage multiuser interference in VLC, outperforming NOMA and SDMA in worst-case rate.

Load-bearing premise

The whole MMF optimization and the reported superiority over SDMA and NOMA rest on the validity of the private-stream rate lower bound (21); if that inequality does not actually follow from the entropy power step or is too loose, the robust design has no proven objective to maximize.

Editorial extensions

If this is right

  • In both underloaded and overloaded VLC networks, the proposed robust RSMA beamforming achieves the largest worst-case rate among the three schemes; in the numerical settings the gain reaches roughly 150% over SDMA and 185% over NOMA in the underloaded regime, and 274% over SDMA and 180% over NOMA in the overloaded regime.
  • As the user uncertainty radius grows, the MMF rate of all schemes decreases, but RSMA retains a larger advantage, indicating that the rate-splitting structure preserves robustness when CSIT is less accurate.
  • The proposed algorithm converges within a few iterations to a rank-one solution, so beamformers can be recovered by eigenvalue decomposition without Gaussian randomization.
  • The derived rate lower bound yields higher MMF rates than the commonly used $\frac{1}{2}\log_2(1+\varrho\,\mathrm{SINR})$ bounds with $\varrho=2/(\pi e)$ or $e/(2\pi)$ at the simulated SNRs.

Reading between the lines

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

  • If the private-stream lower bound (21) is accepted, the same bounding technique could be transferred to other optical multiple-access settings with bounded channel uncertainty, such as multi-cell VLC, hybrid RF/VLC, and integrated sensing and communications, where robust MMF would likely inherit the RSMA advantage.
  • A fairer benchmark comparison might re-derive the NOMA and SDMA baselines under the same imperfect-SIC assumption, since the residual common-stream interference term appears only in the RSMA model; this could make the reported gains more conservative or more pronounced depending on how the baselines are formulated.
  • A testable extension is to replace the outer entropy-power step with the exact entropy calculation for discrete VLC inputs; if the resulting bound is tighter, the MMF rates reported here are conservative and the relative gains would increase.
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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

3 major / 5 minor

Summary. The paper studies robust max-min fair (MMF) beamforming for rate-splitting multiple access (RSMA) in visible light communication (VLC) networks with imperfect channel state information at the transmitter (CSIT). It derives closed-form lower bounds on the achievable rates for the common and private streams using entropy power and entropy inequalities under a bounded-uncertainty CSIT model. These bounds are then used to formulate an MMF rate maximization problem subject to optical and electrical power constraints, which is solved by a combination of semidefinite relaxation, S-lemma, CCCP, and a penalty method for the rank-one constraint. Numerical results compare the proposed RSMA design with SDMA and NOMA in both underloaded and overloaded regimes and report MMF rate gains for RSMA.

Significance. If the derivation and optimization are correct, the paper would be the first to provide VLC-specific rate lower bounds for RSMA with imperfect CSIT and a robust beamforming algorithm under practical power constraints. The problem formulation and the algorithmic pipeline (SDR, S-lemma, CCCP, penalty) are relevant to the VLC and RSMA communities. The numerical evidence of RSMA's superiority over SDMA and NOMA, if supported, would strengthen the case for RSMA in VLC. However, the central private-stream rate bound in (21) rests on an entropy power inequality step that is not valid as written, and the optical power constraint is not faithfully preserved in the problem reformulation. These issues are load-bearing for the main claims, so the current version is not yet acceptable.

major comments (3)
  1. [II-C, Eq. (20d)] The inequality from (20c) to (20d) is not a consequence of the entropy power inequality. For the first entropy in (20c), applying the EPI to the independent summands Δh_k^T p_0 s_0, {h_k^T p_i s_i}_{i=1}^K, and n_k yields 2^{2H(·)} ≥ 2^{2H(Δh_k^T p_0 s_0)} + Σ_{i=1}^K 2^{2H(h_k^T p_i s_i)} + 2πeσ_k^2, not Σ_{i=1}^K 2^{2H(Δh_k^T p_0 s_0 + h_k^T p_i s_i + n_k)}. The variables Δh_k^T p_0 s_0 + h_k^T p_i s_i + n_k are not independent across i because they share the same residual common signal s_0 and the same noise n_k. Moreover, the second entropy in (20c) is upper bounded by (1/2) log2(2πe( |Δh_k^T p_0|^2 ε_0 + Σ_{j≠k} |h_k^T p_j|^2 ε_j + σ_k^2 )), so the two subtracted logarithms in (20d) do not follow from the displayed entropy inequality. Consequently, (21) is not established as a lower bound. Since the MMF problem P0 and the numerical comparisons in Section IV are built on (21) via (26c), the claimed superiority of RSMA over SDMA and NOMA is not supported without a corrected derivation.
  2. [II-D, Eq. (23d) and (26d)] Constraint (23d) is not equivalent to the optical power constraint (3). The original constraint (3) imposes both a lower bound, IL ≤ Σ_i A_i p_i^T e_n + b, and an upper bound, Σ_i A_i p_i^T e_n + b ≤ IH. Constraint (23d) only imposes an upper bound on Σ_i A_i p_i^T e_n using min{b−IL, IH−b} and drops the lower bound. The subsequent squared form in (26d) replaces the interval [IL−b, IH−b] by a symmetric interval of radius min{b−IL, IH−b}, which further changes the feasible set. As a result, the beamformers returned by Algorithm 1 may violate the original optical constraint (3). Please correct the constraint or justify the restriction as a conservative approximation with explicit conditions, e.g., IL ≤ b ≤ IH and b−IL = IH−b.
  3. [III-E, Convergence Analysis] The convergence claim in Section III-E is not fully justified. The argument states that the solution at iteration n is also feasible at iteration n+1, but the feasible set of problem (44) changes between iterations because the linearized constraints (38) are tied to the previous iterate and the penalty surrogate (43) is a first-order approximation of ||P_i||_2. While the first-order expansion of the convex function e^y is a global lower bound, making the linearized constraints a valid inner approximation, the penalty term F_penalty in (43) is an upper bound on the true penalty when ρ is negative. Thus the monotonic increase of t + F_penalty is not guaranteed by the stated argument. A standard SCA convergence proof with a proper surrogate function, or an explicit treatment of the varying feasible set, is needed.
minor comments (5)
  1. [Section V] The text says 'close-form expressions' but should read 'closed-form expressions'.
  2. [Section III-D] In the sentence 'we present a penalty-based method... to address the rand-one constraint,' 'rand-one' should be 'rank-one'.
  3. [Section IV] In the discussion after Fig. 8, 'severe multi-user interfernece' should be 'severe multi-user interference'.
  4. [Fig. 5 caption] The caption reads 'Numer of iterations' and should be 'Number of iterations'.
  5. [Eq. (20d)] The symbol ŷ_{k,i} is introduced after the equation in which it appears; please define it before its first use for readability.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the rate expressions are imported from published (though co-authored) theorems, and the MMF beamforming gains are produced by an optimization algorithm rather than by fitting, renaming, or self-referential definition.

full rationale

The derivation chain is not circular. The common-rate lower bound (18) and the private-rate lower bound (21) are obtained by applying the entropy power inequality to the received signal and then invoking Theorem 1 of [47] for the input distribution (15) with parameters fixed by (16); [47] is a prior published derivation whose assumptions (peak amplitude, average optical power, electrical power) are independent of the RSMA/imperfect-CSIT target and of the numerical MMF comparison. The CSIT uncertainty set (9) is likewise imported from the published model [57]. Although both citations share a co-author, they are not restatements of this paper's conclusions, and the review rules treat such independent published results as real evidence rather than circularity. The subsequent SDR/CCCP/S-lemma/penalty reformulation optimizes worst-case rate subject to constraints; the only hand-tuned quantity, the penalty coefficient ρ, controls convergence and rank recovery and is not fitted to the reported MMF rates, so no fitted input is renamed as a prediction. The numerical RSMA-versus-SDMA/NOMA comparisons are outputs of the optimized beamformers, not inputs. A separate caveat is a correctness concern rather than a circularity concern: the step from (20c) to (20d) applies the entropy power inequality to summands that share s0 and n_k and are therefore not mutually independent, so (21) may not follow as written; if (21) is invalid, the numerical performance claims are unsupported, but the derivation is not circular.

Assumptions & free parameters 1 free parameters · 6 assumptions · 0 invented entities

The paper introduces no new physical entities. It relies on standard information inequalities, a prior maximum-entropy distribution theorem, and a conventional bounded-error CSIT model. The only hand-tuned parameter is the penalty coefficient rho in the rank-one recovery stage.

free parameters (1)
  • penalty coefficient rho = -0.01 (initial)
    Chosen by hand in [-0.1, -0.01] and gradually increased to enforce the rank-one constraint; not derived from first principles.
assumptions (6)
  • standard math Entropy power inequality: 2^{2H(X+Y)} >= 2^{2H(X)} + 2^{2H(Y)} for independent X,Y
    Used in (14) and (20d) to lower-bound the entropy of sums of independent signal and noise terms.
  • standard math Entropy inequality: H(Z) <= 1/2 log2(2*pi*e*Var(Z))
    Used to upper-bound the conditional entropy terms in (14) and (20d).
  • domain assumption Theorem 1 of [47]: the distribution f(s) = e^{-1-alpha-beta*s-gamma*s^2} on [-A,A] maximizes entropy under peak and variance constraints, yielding 2^{2H(s)} = e^{1+2(alpha+gamma*epsilon)}
    Adopted from prior work by co-author Ma et al. to compute the entropy terms tau_i in (18) and (21); equations (16) for alpha, beta, gamma are not re-derived.
  • standard math S-lemma (Boyd and Vandenberghe)
    Used in Section III-B to convert semi-infinite robust constraints (34) into finite LMIs (37).
  • domain assumption Bounded CSIT error model: h_k = h_hat_k + Delta h_k with Delta h_k^T Delta h_k <= v_k
    Assumed in (8)-(9) based on user activity area; the S-lemma transformation depends on the ball form of the uncertainty set.
  • domain assumption LOS-only Lambertian channel model
    The channel in (4) neglects NLOS diffuse reflection, following [51]-[54]; the uncertainty region derivation depends on this model.

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Cite this review

Pith. "Pith review of Robust Max-Min Fair Beamforming Design for Rate Splitting Multiple Access-aided Visible Light Communications." pith.science (2026). https://pith.science/paper/NZR3GBKQ

@misc{pith2026241117056,
  author       = {Pith},
  title        = {Pith review of: Robust Max-Min Fair Beamforming Design for Rate Splitting Multiple Access-aided Visible Light Communications},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NZR3GBKQ}},
  note         = {Machine review of arXiv:2411.17056}
}
read the original abstract

This paper addresses the robust beamforming design for rate splitting multiple access (RSMA)-aided visible light communication (VLC) networks with imperfect channel state information at the transmitter (CSIT). In particular, we first derive the theoretical lower bound for the channel capacity of RSMA-aided VLC networks. Then we investigate the beamforming design to solve the max-min fairness (MMF) problem of RSMA-aided VLC networks under the practical optical power constraint and electrical power constraint while considering the practical imperfect CSIT scenario. To address the problem, we propose a constrained-concave-convex programming (CCCP)-based beamforming design algorithm which exploits semidefinite relaxation (SDR) technique and a penalty method to deal with the rank-one constraint caused by SDR. Numerical results show that the proposed robust beamforming design algorithm for RSMA-aided VLC network achieves a superior performance over the existing ones for space-division multiple access (SDMA) and non-orthogonal multiple access (NOMA).

Figures

Figures reproduced from arXiv: 2411.17056 by the authors.

Figure 1
Figure 1. The considered downlink RSMA-aided multi-user MISO [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. The geometry of mobile users. Let (xL,n, yL,n, zL,n) denote the location of the nth LED. The distance between the nth LED and user k is bounded, i.e., d min k,n ≤ dk,n ≤ d max k,n , where d min k,n and d max k,n represent the minimum distance and the maximum distance between the nth LED and user k, respectively. Their values can be calculated as follows d min k,n =    r zL,n − zk, if(xL,n, yL,n) ∈ Rk  d x,y n,k … view at source ↗
Figure 3
Figure 3. Proposed optimization framework to solve [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Room configuration with LEDs’ locations. [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]
Figure 5
Figure 5. Figure 5: Convergence analysis of the proposed algorithm. [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 7
Figure 7. Figure 7: The MMF rate (bits/sec/Hz) versus SNR (dB) for an unde [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 6
Figure 6. Figure 6: The MMF rate (bits/sec/Hz) versus SNR (dB) under diffe [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 11
Figure 11. Figure 11: The MMF rate (bits/sec/Hz) versus the number of LEDs [PITH_FULL_IMAGE:figures/full_fig_p012_11.png]
Figure 9
Figure 9. Figure 9: The MMF rate (bits/sec/Hz) versus the Minimum curren [PITH_FULL_IMAGE:figures/full_fig_p012_9.png]
Figure 10
Figure 10. Figure 10: The MMF rate (bits/sec/Hz) versus the number of user [PITH_FULL_IMAGE:figures/full_fig_p012_10.png]
Figure 12
Figure 12. Figure 12: The MMF rate (bits/sec/Hz) versus the CSI uncertain [PITH_FULL_IMAGE:figures/full_fig_p013_12.png]

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Works this paper leans on

65 extracted references · 65 canonical work pages

  1. [47]

    Achiev able rate with closed-form for SISO channel and broadcast channel in v isible light communication networks,

    S. Ma, R. Y ang, H. Li, Z.-L. Dong, H. Gu, and S. Li, “Achiev able rate with closed-form for SISO channel and broadcast channel in v isible light communication networks,” J. Lightw. Technol., vol. 35, pp. 2778–2787, Jul. 2017

    work page 2017

  2. [57]

    Optimal and rob ust secure beamformer for indoor MISO visible light communication,

    S. Ma, Z.-L. Dong, H. Li, Z. Lu, and S. Li, “Optimal and rob ust secure beamformer for indoor MISO visible light communication,” J. Lightw. Technol., vol. 34, pp. 4988–4998, Nov. 2016

    work page 2016

  3. [1]

    Visible light communic ation (VLC) for 6G technology: The potency and research challenges,

    S. Ariyanti and M. Suryanegara, “Visible light communic ation (VLC) for 6G technology: The potency and research challenges,” in 2020 F ourth W orld Conference on Smart Trends in Systems, Securit y and Sustainability (W orldS4), pp. 490–493, 2020

    work page 2020

  4. [2]

    Visible light communic ation in 6G: Advances, challenges, and prospects,

    N. Chi, Y . Zhou, Y . Wei, and F. Hu, “Visible light communic ation in 6G: Advances, challenges, and prospects,” IEEE V ehicular Technology Magazine, vol. 15, no. 4, pp. 93–102, 2020

    work page 2020

  5. [3]

    Opportunities and challenges for v isible light communications in 6G,

    M. Katz and I. Ahmed, “Opportunities and challenges for v isible light communications in 6G,” in 2020 2nd 6G Wireless Summit (6G SUMMIT), pp. 1–5, 2020

    work page 2020

  6. [4]

    Comparison of optical OFDM-IDMA an d optical OFDMA for uplink visible light communications,

    J. Dang and Z. Zhang, “Comparison of optical OFDM-IDMA an d optical OFDMA for uplink visible light communications,” in Proc. Int. Conf. Wireless Commun. Signal Process. , pp. 1–6, Oct. 2012

    work page 2012

  7. [5]

    Orth ogonal frequency-division multiplexing access (OFDMA) based wir eless visible light communication (VLC) system,

    J. Y . Sung, C. H. Y eh, C. W. Chow, W. F. Lin, and Y . Liu, “Orth ogonal frequency-division multiplexing access (OFDMA) based wir eless visible light communication (VLC) system,” Opt. Commun., vol. 355, pp. 261– 268, Oct. 2015

    work page 2015

  8. [6]

    Ethernet-OCDMA syst em for multi-user visible light communications,

    M. Guerra-Medina, O. Gonzalez, B. Rojas-Guillama, J. Ma rtin- Gonzalez, F. Delgado, and J. Rabadan, “Ethernet-OCDMA syst em for multi-user visible light communications,” Electronics Letters , vol. 48, pp. 227–228, 02 2012

    work page 2012

Show all 65 references
  1. [7]

    On the performanc e of non-orthogonal multiple access in 5G systems with randomly deployed users,

    Z. Ding, Z. Y ang, P . Fan, and H. V . Poor, “On the performanc e of non-orthogonal multiple access in 5G systems with randomly deployed users,” IEEE Signal Process. Lett. , vol. 21, pp. 1501–1505, Dec. 2014

    2014

  2. [8]

    Non- orthogonal multiple access for 5G: Solutions, challenges, opportunities, and future research trends,

    L. Dai, B. Wang, Y . Y uan, S. Han, I. Chih-lin, and Z. Wang, “ Non- orthogonal multiple access for 5G: Solutions, challenges, opportunities, and future research trends,” IEEE Commun. Mag. , vol. 53, pp. 74–81, Sep. 2015

    2015

  3. [9]

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

    Z. Ding, X. Lei, G. K. Karagiannidis, R. Schober, J. Y uan, and V . K. Bhargava, “A survey on non-orthogonal multiple access for 5 G networks: Research challenges and future trends,” IEEE J. Sel. Areas Commun. , vol. 35, pp. 2181–2195, Oct. 2017

    2017

  4. [10]

    Non-orthogonal multiple access for visible light communi cations,

    H. Marshoud, V . M. Kapinas, G. K. Karagiannidis, and S. M uhaidat, “Non-orthogonal multiple access for visible light communi cations,” IEEE Photonics Technol. Lett. , vol. 28, pp. 51–54, Jan. 2016

    2016

  5. [11]

    Optimal pow er allo- cation for mobile users in non-orthogonal multiple access v isible light communication networks,

    S. Ma, Y . He, H. Li, S. Lu, F. Zhang, and S. Li, “Optimal pow er allo- cation for mobile users in non-orthogonal multiple access v isible light communication networks,” IEEE Trans. Commun. , vol. 67, pp. 2233– 2244, Mar. 2019

    2019

  6. [12]

    User grouping and po wer allocation for NOMA visible light communication multi-cel l networks,

    X. Zhang, Q. Gao, C. Gong, and Z. Xu, “User grouping and po wer allocation for NOMA visible light communication multi-cel l networks,” IEEE Commun. Lett. , vol. 21, pp. 777–780, Apr. 2017

    2017

  7. [13]

    Performance ev aluation of non-orthogonal multiple access in visible light communica tion,

    L. Yin, W. O. Popoola, X. Wu, and H. Haas, “Performance ev aluation of non-orthogonal multiple access in visible light communica tion,” IEEE Trans. Commun., vol. 64, pp. 5162–5175, Dec. 2016

    2016

  8. [14]

    Phase pre-dist ortion for non-orthogonal multiple access in visible light commun ications,

    X. Guan, Y . Hong, Q. Y ang, and C. C. Chan, “Phase pre-dist ortion for non-orthogonal multiple access in visible light commun ications,” in 2016 Optical Fiber Communications Conference and Exhibiti on (OFC) , pp. 1–3, Mar. 2016

    2016

  9. [15]

    R ate splitting for MIMO wireless networks: A promising PHY-layer strategy for LTE evolution,

    B. Clerckx, H. Joudeh, C. Hao, M. Dai, and B. Rassouli, “R ate splitting for MIMO wireless networks: A promising PHY-layer strategy for LTE evolution,” IEEE Commun. Mag. , vol. 54, pp. 98–105, May 2016

    2016

  10. [16]

    Rate-splitting multiple access: Fundamentals, sur vey, and future research trends,

    Y . Mao, O. Dizdar, B. Clerckx, R. Schober, P . Popovski, a nd H. V . Poor, “Rate-splitting multiple access: Fundamentals, sur vey, and future research trends,” IEEE Communications Surveys & Tutorials , vol. 24, no. 4, pp. 2073–2126, 2022

    2022

  11. [17]

    A primer on rate-splitting multiple access: Tut orial, myths, and frequently asked questions,

    B. Clerckx, Y . Mao, E. A. Jorswieck, J. Y uan, D. J. Love, E . Erkip, and D. Niyato, “A primer on rate-splitting multiple access: Tut orial, myths, and frequently asked questions,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 5, pp. 1265–1308, 2023

    2023

  12. [18]

    Rate-splitting multiple access for down- link communication systems: Bridging, generalizing, and o utperforming SDMA and NOMA,

    Y . Mao, B. Clerckx, and V . Li, “Rate-splitting multiple access for down- link communication systems: Bridging, generalizing, and o utperforming SDMA and NOMA,” EURASIP J. Wireless Commun. Netw. , pp. 1–54, May 2018

    2018

  13. [19]

    Rate-splitting for m ulti-antenna non-orthogonal unicast and multicast transmission: Spect ral and energy efficiency analysis,

    Y . Mao, B. Clerckx, and V . O. K. Li, “Rate-splitting for m ulti-antenna non-orthogonal unicast and multicast transmission: Spect ral and energy efficiency analysis,” IEEE Trans. Commun. , vol. 67, pp. 8754–8770, Dec. 2019

    2019

  14. [20]

    Rate-spl itting unifying sdma, oma, noma, and multicasting in miso broadcast channel : A simple two-user rate analysis,

    B. Clerckx, Y . Mao, R. Schober, and H. V . Poor, “Rate-spl itting unifying sdma, oma, noma, and multicasting in miso broadcast channel : A simple two-user rate analysis,” IEEE Wireless Communications Letters , vol. 9, no. 3, pp. 349–353, 2020

    2020

  15. [21]

    Rate-splitti ng multiple access: The first prototype and experimental validation of i ts superiority over SDMA and NOMA,

    X. Lyu, S. Aditya, J. Kim, and B. Clerckx, “Rate-splitti ng multiple access: The first prototype and experimental validation of i ts superiority over SDMA and NOMA,” IEEE Transactions on Wireless Communica- tions, pp. 1–1, 2024

    2024

  16. [22]

    Sum-rate maximization for li nearly precoded downlink multiuser MISO systems with partial CSIT: A rate-s plitting approach,

    H. Joudeh and B. Clerckx, “Sum-rate maximization for li nearly precoded downlink multiuser MISO systems with partial CSIT: A rate-s plitting approach,” IEEE Transactions on Communications , vol. 64, no. 11, pp. 4847–4861, 2016

    2016

  17. [23]

    Optimal DoF region of the K-u ser MISO BC with partial CSIT,

    E. Piovano and B. Clerckx, “Optimal DoF region of the K-u ser MISO BC with partial CSIT,” IEEE Communications Letters , vol. 21, no. 11, pp. 2368–2371, 2017

    2017

  18. [24]

    Rate-splitting mul tiple access for overloaded cellular internet of things,

    Y . Mao, E. Piovano, and B. Clerckx, “Rate-splitting mul tiple access for overloaded cellular internet of things,” IEEE Transactions on Commu- nications, vol. 69, no. 7, pp. 4504–4519, 2021

    2021

  19. [25]

    Multiple access techn iques for intelligent and multifunctional 6G: Tutorial, survey, and outlook,

    B. Clerckx, Y . Mao, Z. Y ang, M. Chen, A. Alkhateeb, L. Liu , M. Qiu, J. Y uan, V . W. S. Wong, and J. Montojo, “Multiple access techn iques for intelligent and multifunctional 6G: Tutorial, survey, and outlook,” Proceedings of the IEEE , pp. 1–48, 2024. 14

    2024

  20. [26]

    Rate-splitting mult iple access to mitigate the curse of mobility in (massive) MIMO networks,

    O. Dizdar, Y . Mao, and B. Clerckx, “Rate-splitting mult iple access to mitigate the curse of mobility in (massive) MIMO networks,” IEEE Transactions on Communications, vol. 69, no. 10, pp. 6765–6780, 2021

    2021

  21. [27]

    A rate spli tting strategy for massive MIMO with imperfect CSIT,

    M. Dai, B. Clerckx, D. Gesbert, and G. Caire, “A rate spli tting strategy for massive MIMO with imperfect CSIT,” IEEE Trans. Wireless Commun., vol. 15, pp. 4611–4624, Mar. 2016

    2016

  22. [28]

    Rate-splitting to mitigate residual transceiver hardware impairments in mas sive MIMO systems,

    A. Papazafeiropoulos, B. Clerckx, and T. Ratnarajah, “ Rate-splitting to mitigate residual transceiver hardware impairments in mas sive MIMO systems,” IEEE Trans. V eh. Technol., vol. 66, pp. 8196–8211, Sep. 2017

    2017

  23. [29]

    Rate-splitting for max-min f air multigroup multicast beamforming in overloaded systems,

    H. Joudeh and B. Clerckx, “Rate-splitting for max-min f air multigroup multicast beamforming in overloaded systems,” IEEE Trans. Wireless Commun., vol. 16, pp. 7276–7289, Nov. 2017

    2017

  24. [30]

    Rate-splitt ing multiple access for simultaneous multi-user communication and mult i-target sensing,

    K. Chen, Y . Mao, L. Yin, C. Xu, and Y . Huang, “Rate-splitt ing multiple access for simultaneous multi-user communication and mult i-target sensing,” IEEE Transactions on V ehicular Technology, pp. 1–6, 2024

    2024

  25. [31]

    Rate-sp litting multiple access for multi-antenna joint radar and communic ations,

    C. Xu, B. Clerckx, S. Chen, Y . Mao, and J. Zhang, “Rate-sp litting multiple access for multi-antenna joint radar and communic ations,” IEEE Journal of Selected Topics in Signal Processing, vol. 15, no. 6, pp. 1332– 1347, 2021

    2021

  26. [32]

    Jo int power and subcarrier allocation optimization for multigroup mul ticast systems with rate splitting,

    H. Chen, D. Mi, B. Clerckx, Z. Chu, J. Shi, and P . Xiao, “Jo int power and subcarrier allocation optimization for multigroup mul ticast systems with rate splitting,” IEEE Trans. V eh. Technol., vol. 69, pp. 2306–2310, Feb. 2020

    2020

  27. [33]

    Cooperative rat e splitting for MISO broadcast channel with user relaying, and performance benefits over cooperative NOMA,

    J. Zhang, B. Clerckx, J. Ge, and Y . Mao, “Cooperative rat e splitting for MISO broadcast channel with user relaying, and performance benefits over cooperative NOMA,” IEEE Signal Process. Lett., vol. 26, pp. 1678– 1682, Sep. 2019

    2019

  28. [34]

    M ax-min fairness of K-user cooperative rate-splitting in MISO broa dcast channel with user relaying,

    Y . Mao, B. Clerckx, J. Zhang, V . O. K. Li, and M. Arafah, “M ax-min fairness of K-user cooperative rate-splitting in MISO broa dcast channel with user relaying,” IEEE Trans. Wireless Commun. , vol. 19, pp. 6362– 6376, Oct. 2020

    2020

  29. [35]

    One-layer rat e-splitting multiple access with benefits over power-domain NOMA in indo or multi-cell visible light communication networks,

    S. Tao, H. Y u, Q. Li, Y . Tang, and D. Zhang, “One-layer rat e-splitting multiple access with benefits over power-domain NOMA in indo or multi-cell visible light communication networks,” in Proc. IEEE/CIC Int. Conf. Commun. China , pp. 1–7, Jun. 2020

    2020

  30. [36]

    Coordinated beamforming design for m ulti-user multi-cell MIMO VLC networks,

    S. Naser, L. Bariah, W. Jaafar, S. Muhaidat, M. Al-Qutay ri, M. Uysal, and P . C. Sofotasios, “Coordinated beamforming design for m ulti-user multi-cell MIMO VLC networks,” IEEE Photonics Journal , vol. 14, no. 3, pp. 1–10, 2022

    2022

  31. [37]

    Energy efficien cy optimization for rate-splitting multiple access-based in door visible light communication networks,

    F. Xing, S. He, V . C. M. Leung, and H. Yin, “Energy efficien cy optimization for rate-splitting multiple access-based in door visible light communication networks,” IEEE Journal on Selected Areas in Commu- nications, vol. 40, no. 5, pp. 1706–1720, 2022

    2022

  32. [38]

    The information capacity of amplitude- an d variance- constrained scalar gaussian channels,

    J. G. Smith, “The information capacity of amplitude- an d variance- constrained scalar gaussian channels,” Inf. Contr ., vol. 18, pp. 203–219, Feb. 1971

    1971

  33. [39]

    Capacity- achieving proba- bility measure for conditionally Gaussian channels with bo unded inputs,

    T. Chan, S. Hranilovic, and F. Kschischang, “Capacity- achieving proba- bility measure for conditionally Gaussian channels with bo unded inputs,” IEEE Trans. Inf. Theory , vol. 51, pp. 2073–2088, Jun. 2005

    2005

  34. [40]

    Max-min fairness and sum-rate maximiza- tion of MU-VLC local networks,

    T. V . Pham and A. T. Pham, “Max-min fairness and sum-rate maximiza- tion of MU-VLC local networks,” in 2015 IEEE Globecom W orkshops (GC Wkshps) , pp. 1–6, 2015

    2015

  35. [41]

    Max- min fairness in rate-splitting multiple access-based VLC n etworks with SLIPT,

    Y . Guo, K. Xiong, B. Gao, P . Fan, D. W. K. Ng, and K. B. Letai ef, “Max- min fairness in rate-splitting multiple access-based VLC n etworks with SLIPT,” IEEE Internet of Things Journal , pp. 1–1, 2024

    2024

  36. [42]

    T ight bounds on channel capacity for dimmable visible light commu nications,

    J.-B. Wang, Q.-S. Hu, J. Wang, M. Chen, and J.-Y . Wang, “T ight bounds on channel capacity for dimmable visible light commu nications,” Journal of Lightwave Technology, vol. 31, no. 23, pp. 3771–3779, 2013

    2013

  37. [43]

    On the capaci ty of free-space optical intensity channels,

    A. Lapidoth, S. M. Moser, and M. A. Wigger, “On the capaci ty of free-space optical intensity channels,” IEEE Transactions on Information Theory, vol. 55, no. 10, pp. 4449–4461, 2009

    2009

  38. [44]

    On the achievable rate of a hybrid PLC/VLC/RF communication sy stem,

    M. Kashef, A. Torky, M. Abdallah, N. Al-Dhahir, and K. Qa raqe, “On the achievable rate of a hybrid PLC/VLC/RF communication sy stem,” in 2015 IEEE Global Communications Conference (GLOBECOM) , pp. 1– 6, 2015

    2015

  39. [45]

    Rate-splitting multiple access: Unifying NOMA and SDMA in MISO VLC channels,

    S. Naser, P . C. Sofotasios, L. Bariah, W. Jaafar, S. Muha idat, M. Al- Qutayri, and O. A. Dobre, “Rate-splitting multiple access: Unifying NOMA and SDMA in MISO VLC channels,” IEEE Open J. V eh. Tech., vol. 1, pp. 393–413, Oct. 2020

    2020

  40. [46]

    Rate- splitting multiple access for indoor visible light communi cation net- works,

    S. Naser, P . C. Sofotasios, S. Muhaidat, and M. Al-Qutay ri, “Rate- splitting multiple access for indoor visible light communi cation net- works,” in 2021 IEEE Wireless Communications and Networking Con- ference W orkshops (WCNCW), pp. 1–7, Mar. 2021

    2021

  41. [48]

    Visible l ight commu- nication, networking, and sensing: A survey, potential and challenges,

    P . H. Pathak, X. Feng, P . Hu, and P . Mohapatra, “Visible l ight commu- nication, networking, and sensing: A survey, potential and challenges,” IEEE Commun. Surveys Tuts. , vol. 17, pp. 2047–2077, Sep. 2015

    2015

  42. [49]

    Joint transceiver and offset design for visible light communications with input-dependent shot no ise,

    Q. Gao, C. Gong, and Z. Xu, “Joint transceiver and offset design for visible light communications with input-dependent shot no ise,” IEEE Trans. Wireless Commun. , vol. 16, pp. 2736–2747, May 2017

    2017

  43. [50]

    MIMO trans ceiver design in dynamic-range-limited VLC systems,

    K. Ying, H. Qian, R. J. Baxley, and G. T. Zhou, “MIMO trans ceiver design in dynamic-range-limited VLC systems,” IEEE Photonics Tech- nology Letters, vol. 28, no. 22, pp. 2593–2596, 2016

    2016

  44. [51]

    Analys is of an optical wireless receiver using a hemispherical lens with applicat ion in MIMO visible light communications,

    T. Q. Wang, Y . A. Sekercioglu, and J. Armstrong, “Analys is of an optical wireless receiver using a hemispherical lens with applicat ion in MIMO visible light communications,” J. Lightw. Technol. , vol. 31, pp. 1744– 1754, Jun. 2013

    2013

  45. [52]

    High data rate multiple input multiple o utput (MIMO) optical wireless communications using white led lig hting,

    L. Zeng, D. C. O’Brien, H. L. Minh, G. E. Faulkner, K. Lee, D. Jung, Y . Oh, and E. T. Won, “High data rate multiple input multiple o utput (MIMO) optical wireless communications using white led lig hting,” IEEE Journal on Selected Areas in Communications , vol. 27, no. 9, pp....

    2009

  46. [53]

    Im- provement of date rate by using equalization in an indoor vis ible light communication system,

    L. Zeng, D. O’Brien, H. Le-Minh, K. Lee, D. Jung, and Y . Oh , “Im- provement of date rate by using equalization in an indoor vis ible light communication system,” in 2008 4th IEEE International Conference on Circuits and Systems for Communications , pp. 678–682, 2008

    2008

  47. [54]

    Performance comparison of MIMO tec hniques for optical wireless communications in indoor environments,

    T. Fath and H. Haas, “Performance comparison of MIMO tec hniques for optical wireless communications in indoor environments,” IEEE Trans. Commun., vol. 61, pp. 733–742, Feb. 2013

    2013

  48. [55]

    Wireless infrared communication s,

    J. Kahn and J. Barry, “Wireless infrared communication s,” Proc. IEEE , vol. 85, pp. 265–298, Feb. 1997

    1997

  49. [56]

    Beamforming design for secure MISO visible light communic ation networks with SLIPT,

    X. Liu, Y . Wang, F. Zhou, S. Ma, R. Q. Hu, and D. W. K. Ng, “Beamforming design for secure MISO visible light communic ation networks with SLIPT,” IEEE Trans. Commun. , vol. 68, pp. 7795–7809, Dec. 2020

    2020

  50. [58]

    Is NOMA efficie nt in multi-antenna networks? A critical look at next generati on multiple access techniques,

    B. Clerckx, Y . Mao, R. Schober, E. A. Jorswieck, D. J. Lov e, J. Y uan, L. Hanzo, G. Y . Li, E. G. Larsson, and G. Caire, “Is NOMA efficie nt in multi-antenna networks? A critical look at next generati on multiple access techniques,” IEEE Open J. Commun. Soc. , vol. 2, pp. 131...

    2021

  51. [59]

    A fr amework of robust transmission design for IRS-aided miso communica tions with imperfect cascaded channels,

    G. Zhou, C. Pan, H. Ren, K. Wang, and A. Nallanathan, “A fr amework of robust transmission design for IRS-aided miso communica tions with imperfect cascaded channels,” IEEE Transactions on Signal Processing , vol. 68, pp. 5092–5106, 2020

    2020

  52. [60]

    Boyd and L

    S. Boyd and L. V andenberghe, Convex Optimization. Cambridge,U.K.: Cambridge Univ, 2004

    2004

  53. [61]

    Sem idefinite relaxation of quadratic optimization problems,

    Z.-q. Luo, W.-k. Ma, A. M.-c. So, Y . Y e, and S. Zhang, “Sem idefinite relaxation of quadratic optimization problems,” IEEE Signal Process. Mag., vol. 27, pp. 20–34, May 2010

    2010

  54. [62]

    Simultaneo usly transmit- ting and reflecting (STAR) RIS aided wireless communication s,

    X. Mu, Y . Liu, L. Guo, J. Lin, and R. Schober, “Simultaneo usly transmit- ting and reflecting (STAR) RIS aided wireless communication s,” IEEE Transactions on Wireless Communications , vol. 21, no. 5, pp. 3083– 3098, 2022

    2022

  55. [63]

    Over-the-air computation via inte lligent reflecting surfaces,

    T. Jiang and Y . Shi, “Over-the-air computation via inte lligent reflecting surfaces,” in 2019 IEEE Global Communications Conference (GLOBE- COM), pp. 1–6, 2019

    2019

  56. [64]

    Transmitter side beyond-diagonal R IS for mmwave integrated sensing and communications,

    K. Chen and Y . Mao, “Transmitter side beyond-diagonal R IS for mmwave integrated sensing and communications,” in 2024 IEEE 25th International W orkshop on Signal Processing Advances in Wi reless Communications (SPAWC), pp. 951–955, 2024

    2024

  57. [65]

    On polyhedr al approxima- tions of the second-order cone.,

    Ben-Tal, Aharon, Nemirovski, and Arkadi, “On polyhedr al approxima- tions of the second-order cone.,” Mathematics of Operations Research , 2001

    2001

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