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Rate-Matching Framework for RSMA-Enabled Multibeam LEO Satellite Communications

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

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Pith's one-line read A rate-splitting precoder that matches each satellite user's offered rate to its traffic demand outperforms fixed beam reuse and MMSE-based RSMA under phase errors.

desk verdict Solid incremental RSMA precoding work for LEO multibeam with a correct SCA formulation; the unvalidated E[log] approximation in Eqs. (7)/(15) is the main thing to fix before trusting the claimed gains. read the letter →

arxiv 2502.05535 v1 pith:IWA3ANNW submitted 2025-02-08 cs.IT cs.NImath.IT

Jaehyup Seong , Juha Park , Juhwan Lee , Jungwoo Lee , Jung-Bin Kim , Wonjae Shin , H. Vincent Poor This is my paper · ORCID
classification cs.ITcs.NImath.IT
keywords rate-splittingmultipleaccessmultibeamLEOsatellitecommunicationsratematchingheterogeneoustrafficdemandphaseperturbationsuccessiveconvexapproximationoverloadedmultiuserdownlink
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

Multibeam LEO satellites serve users whose traffic needs differ widely across beams, but conventional precoding targets sum rate or fairness and so ends up either under- or over-delivering. This paper proposes a rate-matching framework based on rate-splitting multiple access (RSMA), in which each user's message is split into a common part decoded by all users and a private part for that user, that directly minimizes the gap between each user's offered rate and its traffic demand while also trimming transmit power. The optimization accounts for channel phase perturbations from estimation and feedback errors and is made tractable through successive convex approximation. In overloaded downlink simulations with four satellite feeds serving five users, the proposed RM-RSMA design reports higher traffic-demand satisfaction than MMSE-based RSMA, SDMA, multicast RSMA, and four-color reuse, under both perfect and imperfect CSI.

What carries the argument

The central object is one-layer RSMA with a common stream plus per-user private streams, optimized by a successive convex approximation (SCA) loop. Each user's message is split into a common part (merged into one stream decoded by all users, then cancelled) and a private part. The rate expressions are ergodic rates averaged over feedback phase errors, approximated by moving the expectation inside the logarithm, and the non-convex SINR constraints are convexified by replacing quadratic-over-linear terms with first-order Taylor lower bounds; the resulting problem is solved iteratively with a standard convex solver. Phase-perturbation statistics enter through closed-form correlation matrices that keep the convexified constraints positive semidefinite.

What would settle it

Run the optimized RM-RSMA precoder under the same phase-error statistics but evaluate the actual rates by Monte Carlo averaging over phase realizations (or by exact ergodic rate computation); if RM-RSMA's demand satisfaction no longer exceeds MMSE-RSMA's by the reported margins, the central claim is refuted for that regime.

Watch

Extended reading notes

Core claim

The paper's central claim is that a traffic-aware RSMA precoder can match non-uniform user demands and save power at the same time, something the baselines cannot do because they either ignore demand asymmetry or fix the private precoders to MMSE. The key mechanism is the common stream: by encoding part of every user's message into one stream decoded by all users and then removed by successive interference cancellation, the satellite can partially satisfy high-demand users while reducing interference toward low-demand users, overcoming the shortage of spatial dimensions when the number of users exceeds the number of feeds. The authors formulate the rate-matching problem as minimizing a weighted sum of demand-rate mismatch and transmit power under per-feed power limits, convexify it with successive convex approximation, and verify by simulation that the resulting design achieves higher and more stable traffic-demand satisfaction than the baselines, with the advantage growing as phase perturbation worsens.

Load-bearing premise

The load-bearing premise is that approximating each ergodic rate by the logarithm of the ratio of the expectations of signal and interference-plus-noise terms is accurate enough in the overloaded, low-SINR regime; if that approximation misleads the optimizer, the reported demand-matching gains may not materialize at the true rates.

Editorial extensions

If this is right

  • Using the common stream to carry part of high-demand users' traffic reduces inter- and intra-beam interference enough to serve more users than the number of satellite feeds, a regime where SDMA degrades sharply.
  • Incorporating the statistical spread of phase errors into the precoder design keeps demand satisfaction high as feedback error grows; ignoring that spread widens the gap to the proposed scheme.
  • A single regularization parameter trades demand matching against transmit power, so the satellite can spend less power when demands are modest without abandoning rate matching.
  • The L2-norm objective suits highly uneven demand profiles, while the L1-norm is competitive when residuals are small; neither dominates across all operating points.

Reading between the lines

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

  • The same rate-matching objective could be applied to terrestrial overloaded MIMO or multi-cell systems with heterogeneous user demand, where the common stream would play the same interference-offloading role.
  • Adapting the regularization parameter per beam or per demand forecast, rather than fixing it globally, is a natural extension that could improve the power-matching tradeoff under time-varying traffic.
  • Because the paper relies on the expectation-inside-logarithm approximation, its optimized rates should be checked against exact ergodic rates via Monte Carlo phase draws; if the approximation biases the match, a refined bound would be needed in low-SINR regimes.
  • The framework assumes a fixed beam layout and a single satellite; making the rate-matching precoder handover-aware across multiple LEO satellites is a natural next step that the paper gestures toward in its future directions.
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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 a multibeam LEO SATCOM downlink with four antenna feeds and five users, heterogeneous per-user traffic demands, and phase perturbations caused by channel estimation and feedback errors. It proposes an RSMA-based rate-matching (RM) framework that minimizes a weighted sum of the squared differences between offered rates and traffic demands plus the total transmit power, subject to common-rate decodability and per-feed power constraints. The non-convex problem is relaxed using successive convex approximation into a convex program, with common and private rates approximated via the substitution E[log2(1+X/Y)] ≈ log2(1+E[X]/E[Y]) and closed-form expectation matrices for the phase perturbation statistics. Numerical results compare the proposed RM-RSMA scheme with MMSE-RSMA, SDMA, multicast-RSMA, four-color reuse, and MMF-RSMA under both perfect and imperfect CSI, reporting higher traffic-demand satisfaction and lower transmit power.

Significance. If the reported results are reliable, the paper makes a useful contribution to traffic-aware precoding in overloaded multibeam satellite systems, and the explicit modeling of phase perturbations is practically motivated. The derivations of the expectation matrices in (11)-(12) are correct, Lemma 1 is valid, and the SCA reformulation follows standard practice. The evaluation uses 3GPP NTN parameters and compares against several external baselines, so the central claim is not circular. However, the performance claims currently rest on an unvalidated ergodic-rate approximation and an undefined satisfaction metric, and the simulation evidence has no error bars. The significance is therefore conditional on the authors providing the missing validation and statistical reporting.

major comments (3)
  1. [Section III, Eqs. (7) and (15); Section IV] The paper replaces the ergodic common and private rates by log2(1+E[X]/E[Y]) and builds the entire optimization in P5 on this surrogate. This approximation is not validated at the operating point actually simulated (Nt=4, K=5, per-feed EIRP 21.52 dBm, noise variance 1, δfb=5°, δce=2°), where X and Y in (7) and (15) are functions of the same feedback phase vector efb and are therefore correlated. Please provide a numerical comparison of the surrogate against a Monte Carlo evaluation of the true ergodic rates at these parameters, or a bound on the approximation error, and state explicitly whether the reported rates and satisfaction percentages in Figs. 3-14 are computed with the surrogate or with per-realization SINRs. This is load-bearing because the claimed 4.8-11.7% improvements over MMSE-RSMA inherit the surrogate; Remark 1 only validates the limiting cases δfb=0 and δce=0, not the realistic operating point. The convergence statement in Remark 4 should also be qualified, since P1 contains the true ergodic rates while P5 optimizes the surrogate.
  2. [Section IV, Figs. 7, 8, 13, 14] The 'traffic demand satisfaction' percentage used throughout the numerical evaluation is never defined. Please give the exact formula (for example, 100 times one minus the ratio of unmet plus unused rate to total demand) and specify whether the rates entering that formula are the approximate ergodic expressions from (7) and (15), the per-realization SINR after applying the optimized precoder, or something else. Without this definition, the reported percentage improvements cannot be reproduced or interpreted.
  3. [Section IV, paragraph after Table I] The simulation results are stated to be obtained by averaging 100 channel realizations, but no error bars, confidence intervals, or significance tests are reported for the satisfaction percentages. In an overloaded four-feed/five-user system, the differences of 4.8-11.7% over MMSE-RSMA could be within sampling variability. Please report standard errors or confidence intervals, and specify how many phase-perturbation realizations are used per channel realization.
minor comments (5)
  1. [Algorithm 1, Output] The output line 'Calculate instantaneous total rate R*_k using P*, c*' is unclear, because the rates in the model are ergodic and c* is a vector of common-rate portions; specify whether the final rates are the approximate ergodic rates or instantaneous rates under the phase perturbation model.
  2. [Section IV, Figs. 8 and 14] The benchmark 'RM-RSMA (no info δfb, δce)' is not defined; state whether it solves P5 with δfb=δce=0 while the evaluation still applies the actual phase perturbations.
  3. [Section I-C and Eqs. (11)-(12)] The notation for the all-ones matrix is inconsistent: the Notations section says '1 denoted a vector of all 1's', while equations (11)-(12) use 1_Nt for the all-ones matrix; use a distinct symbol for the all-ones matrix throughout.
  4. [Section IV, Fig. 2] The user-drop procedure for Fig. 2 is not specified; please state how users are placed (e.g., uniformly within each beam) and whether the same user locations are reused across channel realizations.
  5. [Appendix, Eq. (36)] The complex completing-square step in (36) is formal; a sentence noting that this is the standard Gaussian characteristic function would improve readability.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the rate expressions and SCA reformulation are self-contained; self-citations are historical/baseline only.

full rationale

The derivation chain is self-contained. The rate expressions in (7) and (15) are derived from explicit Gaussian phase-perturbation statistics, using the approximation E[log2(1+X/Y)]≈log2(1+E[X]/E[Y]) imported from independent reference [44]; the PSD matrix results used in Lemma 1 are proved in Appendix A from the stated phase distributions. The SCA reformulation P5 is derived from P1 by standard convexification and is not fitted to the simulation outputs. The reported traffic-demand satisfaction is evaluated from the same rate model, but the paper's claim is comparative: RM-RSMA outperforms external or prior-work benchmarks under the same channel model and constraints. No fitted parameter is renamed as a prediction; the regularization parameter η is a design tradeoff, not a fitted data parameter. The self-citations ([1] as the conference version and [38] as the prior RSMA rate-matching work and baseline) are used for context and comparison, not as load-bearing support for the central derivation. The unvalidated accuracy of the ergodic-rate approximation near the simulated operating points is a correctness/robustness concern, not a circularity, because the approximation is an explicit modeling assumption rather than an input that is later relabeled as a result.

Assumptions & free parameters 4 free parameters · 6 assumptions · 0 invented entities

The framework introduces no new physical entities or fitted constants. The rate expressions are derived from explicit statistical assumptions; the main hand-chosen degrees of freedom are the regularization weight eta and the phase perturbation parameters, which are simulation inputs rather than fitted values. The load-bearing approximation in (7),(15) is imported from [44] and not validated against exact ergodic rates.

free parameters (4)
  • eta (regularization parameter) = 0.91 bps/Hz/W
    Chosen by hand to balance rate matching against transmit power minimization in P3/P5; performance tradeoff shown in Figs 15-16 but no principled selection rule.
  • delta_fb (feedback phase perturbation std) = 5 degrees (and 0-15 in sweep)
    Simulation input from phase error model; not fitted but choice affects all results.
  • delta_ce (estimation phase perturbation std) = 2 degrees
    Simulation input for channel estimation error; affects derived rate expressions.
  • sigma_n^2 (noise variance) = 1
    Normalized noise variance used in simulations; arbitrary scaling.
assumptions (6)
  • domain assumption Phase perturbation angles theta_fb and theta_ce are i.i.d. zero-mean Gaussian with variances delta_fb^2 and delta_ce^2.
    Introduced in Section II-B; used to compute expectation matrices (11)-(12) and all rate expressions.
  • domain assumption Channel amplitude is perfectly known at satellite and users; only phase is perturbed.
    Stated in Section II; simplifies the channel model and is the basis for treating phase errors separately.
  • domain assumption Ergodic rate approximation E[log2(1+X/Y)] approximately log2(1+E[X]/E[Y]) from [44].
    Used at (7) and (15); the paper does not quantify its accuracy, and it is load-bearing for the optimized rate expressions.
  • domain assumption Single-feed-per-beam reflector generates known spot beams with Bessel-function pattern.
    Channel gains (2) from [3],[40]; standard for multibeam SATCOM but an idealization.
  • domain assumption Satellite knows each user's traffic demand exactly.
    Assumed in Section II-A before problem formulation; demand mismatch is the paper's motivation, but demand errors are not modeled.
  • standard math SCA convergence theorem of Marks and Wright [47] applies to the approximated problem P5.
    Remark 4 claims convergence to stationary points of P1, but P5 is obtained by convex lower-bound approximation and the theorem's hypotheses are not verified in the paper.

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Cite this review

Pith. "Pith review of Rate-Matching Framework for RSMA-Enabled Multibeam LEO Satellite Communications." pith.science (2026). https://pith.science/paper/IWA3ANNW

@misc{pith2026250205535,
  author       = {Pith},
  title        = {Pith review of: Rate-Matching Framework for RSMA-Enabled Multibeam LEO Satellite Communications},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IWA3ANNW}},
  note         = {Machine review of arXiv:2502.05535}
}
read the original abstract

With the goal of ubiquitous global connectivity, multibeam low Earth orbit (LEO) satellite communication (SATCOM) has attracted significant attention in recent years. The traffic demands of users are heterogeneous within the broad coverage of SATCOM due to different geological conditions and user distributions. Motivated by this, this paper proposes a novel rate-matching (RM) framework based on rate-splitting multiple access (RSMA) that minimizes the difference between the traffic demands and offered rates while simultaneously minimizing transmit power for power-hungry satellite payloads. Moreover, channel phase perturbations arising from channel estimation and feedback errors are considered to capture realistic multibeam LEO SATCOM scenarios. To tackle the non-convexity of the RSMA-based RM problem under phase perturbations, we convert it into a tractable convex form via the successive convex approximation method and present an efficient algorithm to solve the RM problem. Through the extensive numerical analysis across various traffic demand distribution and channel state information accuracy at LEO satellites, we demonstrate that RSMA flexibly allocates the power between common and private streams according to different traffic patterns across beams, thereby efficiently satisfying users non-uniform traffic demands. In particular, the use of common messages plays a vital role in overcoming the limited spatial dimension available at LEO satellites, enabling it to manage inter- and intra-beam interference effectively in the presence of phase perturbation.

Figures

Figures reproduced from arXiv: 2502.05535 by the authors.

Figure 1
Figure 1. System model of the proposed RSMA scheme, where the LEO satellite serves users requiring different traffics in a wide [PITH_FULL_IMAGE:figures/full_fig_p010_1.png] view at source ↗
Figure 2
Figure 2. Beam pattern of four spot beams and location of users uniformly distributed within each beam. [PITH_FULL_IMAGE:figures/full_fig_p023_2.png] view at source ↗
Figure 3
Figure 3. Achievable rate comparison of each user under perfect CSIT and CSIR ( [PITH_FULL_IMAGE:figures/full_fig_p024_3.png] view at source ↗
Figures from the paper (13 more)
Figure 4
Figure 4. Figure 4: Achievable rate comparison of each user under imperfect CSIT and CSIR ( [PITH_FULL_IMAGE:figures/full_fig_p025_4.png]
Figure 5
Figure 5. Figure 5: Portion of the common rate and private rate of each user for “ [PITH_FULL_IMAGE:figures/full_fig_p026_5.png]
Figure 6
Figure 6. Figure 6: Portion of the common rate and private rate of each user for “ [PITH_FULL_IMAGE:figures/full_fig_p027_6.png]
Figure 7
Figure 7. Figure 7: Traffic demand satisfaction comparison under both perfect CSIT and CSIR and imperfect CSIT and CSIR when the [PITH_FULL_IMAGE:figures/full_fig_p028_7.png]
Figure 8
Figure 8. Figure 8: Traffic demand satisfaction comparison per [PITH_FULL_IMAGE:figures/full_fig_p029_8.png]
Figure 9
Figure 9. Figure 9: Achievable rate comparison of each user under perfect CSIT and CSIR ( [PITH_FULL_IMAGE:figures/full_fig_p030_9.png]
Figure 10
Figure 10. Figure 10: Achievable rate comparison of each user under imperfect CSIT and CSIR ( [PITH_FULL_IMAGE:figures/full_fig_p031_10.png]
Figure 11
Figure 11. Figure 11: Portion of the common rate and private rate of each user for “ [PITH_FULL_IMAGE:figures/full_fig_p032_11.png]
Figure 12
Figure 12. Figure 12: Portion of the common rate and private rate of each user for “ [PITH_FULL_IMAGE:figures/full_fig_p033_12.png]
Figure 13
Figure 13. Figure 13: Traffic demand satisfaction comparison under both perfect CSIT and CSIR and imperfect CSIT and CSIR when the [PITH_FULL_IMAGE:figures/full_fig_p034_13.png]
Figure 14
Figure 14. Figure 14: Traffic demand satisfaction comparison per [PITH_FULL_IMAGE:figures/full_fig_p035_14.png]
Figure 15
Figure 15. Figure 15: Rate-matching performance comparison of the proposed framework when employing the L1-norm and L2-norm-based [PITH_FULL_IMAGE:figures/full_fig_p036_15.png]
Figure 16
Figure 16. Figure 16: Rate-matching performance comparison of the proposed framework when employing the L1-norm and L2-norm-based [PITH_FULL_IMAGE:figures/full_fig_p037_16.png]

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Forward citations

Cited by 1 Pith paper

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Reference graph

Works this paper leans on

50 extracted references · 45 canonical work pages · cited by 1 Pith paper

  1. [1]

    Robust rate-matching framework for multibeam satellite communications with phase perturbations,

    J. Seong et al. , “Robust rate-matching framework for multibeam satellite communications with phase perturbations,” in Proc. 2023 IEEE Globecom Workshops (GC Workshops) , 2023, pp. 280–285

    work page 2023

  2. [32]

    NOMA-enabled multi-beam satellite systems: Joint optimization to overcome offered-requested data mismatches,

    A. Wang, L. Lei, E. Lagunas, A. I. P ´erez-Neira, S. Chatzinotas, and B. Ottersten, “NOMA-enabled multi-beam satellite systems: Joint optimization to overcome offered-requested data mismatches,” IEEE Trans. Veh. Technol. , vol. 70, no. 1, pp. 900–913, 2020

    work page 2020

  3. [37]

    Energy-efficient RSMA for multigroup multicast and multibeam satellite communications,

    H. Cui, L. Zhu, Z. Xiao, B. Clerckx, and R. Zhang, “Energy-efficient RSMA for multigroup multicast and multibeam satellite communications,” IEEE Wireless Commun. Lett. , vol. 12, no. 5, pp. 838–842, 2023

    work page 2023

  4. [44]

    Power scaling of uplink massive MIMO systems with arbitrary-rank channel means,

    Q. Zhang et al., “Power scaling of uplink massive MIMO systems with arbitrary-rank channel means,” IEEE J. Sel. Top. Signal Process., vol. 8, no. 5, pp. 966–981, 2014

    work page 2014

  5. [2]

    Traffic matching in 5G ultra-dense networks,

    Y . Zhong, X. Ge, H. H. Yang, T. Han, and Q. Li, “Traffic matching in 5G ultra-dense networks,” IEEE Commun. Mag. , vol. 56, no. 8, pp. 100–105, 2018. 40

    work page 2018

  6. [3]

    Signal processing for high-throughput satellites: Challenges in new interference-limited scenarios,

    A. I. Perez-Neira, M. A. Vazquez, M. B. Shankar, S. Maleki, and S. Chatzinotas, “Signal processing for high-throughput satellites: Challenges in new interference-limited scenarios,” IEEE Signal Process. Mag., vol. 36, no. 4, pp. 112–131, 2019

    work page 2019

  7. [4]

    Ris-empowered LEO satellite networks for 6G: Promising usage scenarios and future directions,

    M. Toka et al., “Ris-empowered LEO satellite networks for 6G: Promising usage scenarios and future directions,” IEEE Commun. Mag., vol. 62, no. 11, pp. 128–135, 2024

    work page 2024

  8. [5]

    Secrecy-energy efficient hybrid beamforming for satellite- terrestrial integrated networks,

    Z. Lin, M. Lin, B. Champagne, W.-P. Zhu, and N. Al-Dhahir, “Secrecy-energy efficient hybrid beamforming for satellite- terrestrial integrated networks,” IEEE Trans. Commun. , vol. 69, no. 9, pp. 6345–6360, 2021

    work page 2021

Show all 50 references
  1. [6]

    Refracting RIS-aided hybrid satellite-terrestrial relay networks: Joint beamforming design and optimization,

    Z. Lin, H. Niu, K. An, Y . Wang, G. Zheng, S. Chatzinotas, and Y . Hu, “Refracting RIS-aided hybrid satellite-terrestrial relay networks: Joint beamforming design and optimization,” IEEE Trans. Aerosp. Electron. Syst. , vol. 58, no. 4, pp. 3717–3724, 2022

    2022

  2. [7]

    SLNR-based secure energy efficient beamforming in multibeam satellite systems,

    Z. Lin, K. An, H. Niu, Y . Hu, S. Chatzinotas, G. Zheng, and J. Wang, “SLNR-based secure energy efficient beamforming in multibeam satellite systems,” IEEE Trans. Aerosp. Electron. Syst. , vol. 59, no. 2, pp. 2085–2088, 2022

    2022

  3. [8]

    Robust precoding design for multibeam downlink satellite channel with phase uncertainty,

    A. Gharanjik, M. B. Shankar, P.-D. Arapoglou, M. Bengtsson, and B. Ottersten, “Robust precoding design for multibeam downlink satellite channel with phase uncertainty,” in Proc. IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP) , 2015, pp. 3083–3087

    2015

  4. [9]

    Robust beamforming for multibeam satellite communication in the face of phase perturbations,

    X. Zhang, J. Wang, C. Jiang, C. Yan, Y . Ren, and L. Hanzo, “Robust beamforming for multibeam satellite communication in the face of phase perturbations,” IEEE Trans. Veh. Technol., vol. 68, no. 3, pp. 3043–3047, 2019

    2019

  5. [10]

    Precoding in multibeam satellite communications: Present and future challenges,

    M. ´A. V ´azquez et al., “Precoding in multibeam satellite communications: Present and future challenges,” IEEE Wireless Commun., vol. 23, no. 6, pp. 88–95, 2016

    2016

  6. [11]

    Resource efficiency optimization for robust beamforming in multi-beam satellite communications,

    W. Wang et al., “Resource efficiency optimization for robust beamforming in multi-beam satellite communications,” IEEE Trans. Veh. Technol., vol. 70, no. 7, pp. 6958–6968, 2021

    2021

  7. [12]

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

    B. Clerckx, H. Joudeh, C. Hao, M. Dai, and B. Rassouli, “Rate splitting for MIMO wireless networks: A promising PHY-layer strategy for LTE evolution,” IEEE Commun. Mag. , vol. 54, no. 5, pp. 98–105, 2016

    2016

  8. [13]

    Energy efficiency of rate-splitting multiple access, and performance benefits over SDMA and NOMA,

    Y . Mao, B. Clerckx, and V . O. Li, “Energy efficiency of rate-splitting multiple access, and performance benefits over SDMA and NOMA,” in Proc. IEEE 15th Int. Symp. Wireless Commun. Sys. , 2018, pp. 1–5

    2018

  9. [14]

    Rate-splitting multiple access for downlink communication systems: Bridging, generalizing, and outperforming SDMA and NOMA,

    Y . Mao, B. Clerckx, and V . O. Li, “Rate-splitting multiple access for downlink communication systems: Bridging, generalizing, and outperforming SDMA and NOMA,” EURASIP J. Wirel. Commun. Netw. , vol. 2018, no. 1, pp. 1–54, 2018

    2018

  10. [15]

    Rate-splitting 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-splitting unifying SDMA, OMA, NOMA, and multicasting in MISO broadcast channel: A simple two-user rate analysis,” IEEE Wireless Commun. Lett. , vol. 9, no. 3, pp. 349–353, 2019

    2019

  11. [16]

    Rate-splitting multiple access for 6G networks: Ten promising scenarios and applications,

    J. Park et al., “Rate-splitting multiple access for 6G networks: Ten promising scenarios and applications,” IEEE Network, vol. 38, no. 3, pp. 128–136, 2024

    2024

  12. [17]

    Rate-splitting multiple access for multibeam satellite communications,

    L. Yin and B. Clerckx, “Rate-splitting multiple access for multibeam satellite communications,” in Proc. IEEE Int. Conf. Commun. Workshops (ICC Workshops), 2020, pp. 1–6

    2020

  13. [18]

    Rate-splitting multiple access for multigroup multicast and multibeam satellite systems,

    L. Yin and B. Clerckx, “Rate-splitting multiple access for multigroup multicast and multibeam satellite systems,” IEEE Trans. Commun., vol. 69, no. 2, pp. 976–990, 2020

    2020

  14. [19]

    Rate-splitting multiple access for multigroup multicast cellular and satellite communications: PHY layer design and link-level simulations,

    L. Yin, O. Dizdar, and B. Clerckx, “Rate-splitting multiple access for multigroup multicast cellular and satellite communications: PHY layer design and link-level simulations,” in Proc. IEEE Int. Conf. Commun. Workshops (ICC Workshops), 2021, pp. 1–6

    2021

  15. [20]

    Rate-splitting multiple access for multigateway multibeam satellite systems with feeder link interference,

    Z. W. Si, L. Yin, and B. Clerckx, “Rate-splitting multiple access for multigateway multibeam satellite systems with feeder link interference,” IEEE Trans. Commun. , vol. 70, no. 3, pp. 2147–2162, 2022. 41

    2022

  16. [21]

    Energy efficiency of rate-splitting multiple access for multibeam satellite system,

    J. Liu, Y . L. Guan, Y . Ge, L. Yin, and B. Clerckx, “Energy efficiency of rate-splitting multiple access for multibeam satellite system,” arXiv preprint arXiv:2303.11784, 2023

    2023 arXiv

  17. [22]

    Rate splitting multiple access for next generation cognitive radio enabled LEO satellite networks,

    W. U. Khan et al. , “Rate splitting multiple access for next generation cognitive radio enabled LEO satellite networks,” IEEE Trans. Wireless Commun. , vol. 22, no. 11, pp. 8423–8435, 2023

    2023

  18. [23]

    Distributed rate-splitting multiple access for multilayer satellite communications,

    Y . Xu, L. Yin, Y . Mao, W. Shin, and B. Clerckx, “Distributed rate-splitting multiple access for multilayer satellite communications,” IEEE Trans. Commun. , vol. 72, no. 10, pp. 6131–6144, 2024

    2024

  19. [24]

    Supporting IoT with rate-splitting multiple access in satellite and aerial-integrated networks,

    Z. Lin, M. Lin, T. De Cola, J.-B. Wang, W.-P. Zhu, and J. Cheng, “Supporting IoT with rate-splitting multiple access in satellite and aerial-integrated networks,” IEEE Internet Things J. , vol. 8, no. 14, pp. 11 123–11 134, 2021

    2021

  20. [25]

    Secure and energy efficient transmission for RSMA-based cognitive satellite-terrestrial networks,

    Z. Lin, M. Lin, B. Champagne, W.-P. Zhu, and N. Al-Dhahir, “Secure and energy efficient transmission for RSMA-based cognitive satellite-terrestrial networks,” IEEE Wireless Commun. Lett. , vol. 10, no. 2, pp. 251–255, 2020

    2020

  21. [26]

    Rate-splitting multiple access for satellite-terrestrial integrated networks: Benefits of coordination and cooperation,

    L. Yin and B. Clerckx, “Rate-splitting multiple access for satellite-terrestrial integrated networks: Benefits of coordination and cooperation,” IEEE Trans. Wireless Commun. , vol. 22, no. 1, pp. 317–332, 2023

    2023

  22. [27]

    Coordinated rate-splitting multiple access for integrated satellite-terrestrial networks with super-common message,

    J. Lee, J. Lee, L. Yin, W. Shin, and B. Clerckx, “Coordinated rate-splitting multiple access for integrated satellite-terrestrial networks with super-common message,” IEEE Trans. Veh. Technol., vol. 73, no. 2, pp. 2989–2994, 2024

    2024

  23. [28]

    Distributed precoding for satellite-terrestrial integrated networks without sharing csit: A rate-splitting approach,

    D. Kim, W. Shin, J. Park, and D. K. Kim, “Distributed precoding for satellite-terrestrial integrated networks without sharing csit: A rate-splitting approach,” arXiv preprint arXiv:2309.06325, 2023

    2023 arXiv

  24. [29]

    System capacity optimization in time and frequency for multibeam multi-media satellite systems,

    X. Alberti et al. , “System capacity optimization in time and frequency for multibeam multi-media satellite systems,” in Proc. 5th Adv. Satell. Multimedia Syst. Conf., 11th Signal Process. Space Commun. Workshops (ASMS/SPSC Workshops) , 2010, pp. 226–233

    2010

  25. [30]

    Flexible resource optimization for GEO multibeam satellite communication system,

    T. S. Abdu, S. Kisseleff, E. Lagunas, and S. Chatzinotas, “Flexible resource optimization for GEO multibeam satellite communication system,” IEEE Trans. Wireless Commun. , vol. 20, no. 12, pp. 7888–7902, 2021

    2021

  26. [31]

    GEO payload power minimization: Joint precoding and beam hopping design,

    V . N. Ha, T. T. Nguyen, E. Lagunas, J. C. M. Duncan, and S. Chatzinotas, “GEO payload power minimization: Joint precoding and beam hopping design,” in Proc. IEEE Glob. Commun. Conf. (GLOBECOM) , 2022, pp. 6445–6450

    2022

  27. [33]

    Multi-satellite beam hopping based on load balancing and interference avoidance for NGSO satellite communication systems,

    Z. Lin, Z. Ni, L. Kuang, C. Jiang, and Z. Huang, “Multi-satellite beam hopping based on load balancing and interference avoidance for NGSO satellite communication systems,” IEEE Trans. Commun. , vol. 71, no. 1, pp. 282–295, 2022

    2022

  28. [34]

    Generic optimization of linear precoding in multibeam satellite systems,

    G. Zheng, S. Chatzinotas, and B. Ottersten, “Generic optimization of linear precoding in multibeam satellite systems,” IEEE Trans. Wireless Commun. , vol. 11, no. 6, pp. 2308–2320, 2012

    2012

  29. [35]

    QoS-guarantee resource allocation for multibeam satellite industrial internet of things with NOMA,

    X. Liu et al., “QoS-guarantee resource allocation for multibeam satellite industrial internet of things with NOMA,” IEEE Trans. Ind. Informat. , vol. 17, no. 3, pp. 2052–2061, 2019

    2019

  30. [36]

    Joint optimization of beam-hopping design and NOMA-assisted transmission for flexible satellite systems,

    A. Wang, L. Lei, E. Lagunas, A. I. P ´erez-Neira, S. Chatzinotas, and B. Ottersten, “Joint optimization of beam-hopping design and NOMA-assisted transmission for flexible satellite systems,” IEEE Trans. Wireless Commun. , vol. 21, no. 10, pp. 8846–8858, 2022

    2022

  31. [38]

    RSMA-enabled multigroup multicast rate-matching for multibeam satellite systems,

    J. Lee, J. Seong, J. Lee, D. Le Ruyet, L. Yin, B. Clerckx, and W. Shin, “RSMA-enabled multigroup multicast rate-matching for multibeam satellite systems,” in Proc. 2023 IEEE 24th Int. Workshop Signal Process. Adv. Wireless Commun. (SPAWC), 2023, pp. 251–255

    2023

  32. [39]

    Solutions for NR to support non-terrestrial networks (NTN) (Release 16),

    3GPP TR 38.821 v16.2.0, “Solutions for NR to support non-terrestrial networks (NTN) (Release 16),” Apr. 2023. 42

    2023

  33. [40]

    Multicast multigroup precoding and user scheduling for frame-based satellite communications,

    D. Christopoulos, S. Chatzinotas, and B. Ottersten, “Multicast multigroup precoding and user scheduling for frame-based satellite communications,” IEEE Trans. Wireless Commun. , vol. 14, no. 9, pp. 4695–4707, 2015

    2015

  34. [41]

    Rate-splitting multiple access for multi-antenna broadcast channel with imperfect CSIT and CSIR,

    J. An, O. Dizdar, B. Clerckx, and W. Shin, “Rate-splitting multiple access for multi-antenna broadcast channel with imperfect CSIT and CSIR,” arXiv preprint arXiv:2102.08738, 2021

    2021 arXiv

  35. [42]

    Max-min fairness precoder design for rate-splitting multiple access: Impact of imperfect channel knowledge,

    B. Lee and W. Shin, “Max-min fairness precoder design for rate-splitting multiple access: Impact of imperfect channel knowledge,” IEEE Trans. Veh. Technol., vol. 72, no. 1, pp. 1355–1359, 2022

    2022

  36. [43]

    Rate-splitting multiple access for downlink MIMO: A generalized power iteration approach,

    J. Park, J. Choi, N. Lee, W. Shin, and H. V . Poor, “Rate-splitting multiple access for downlink MIMO: A generalized power iteration approach,” IEEE Trans. Wireless Commun. , vol. 22, no. 3, pp. 1588–1603, 2023

    2023

  37. [45]

    CVX: Matlab software for disciplined convex programming, version 2.1,

    M. Grant and S. Boyd, “CVX: Matlab software for disciplined convex programming, version 2.1,” 2014

    2014

  38. [46]

    Ye, Interior point algorithms: theory and analysis

    Y . Ye, Interior point algorithms: theory and analysis . John Wiley & Sons, 2011

    2011

  39. [47]

    A general inner approximation algorithm for nonconvex mathematical programs,

    B. R. Marks and G. P. Wright, “A general inner approximation algorithm for nonconvex mathematical programs,”Operations research, vol. 26, no. 4, pp. 681–683, 1978

    1978

  40. [48]

    Boyd and L

    S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge University Press, 2004

    2004

  41. [49]

    Computation offloading in LEO satellite networks with hybrid cloud and edge computing,

    Q. Tang, Z. Fei, B. Li, and Z. Han, “Computation offloading in LEO satellite networks with hybrid cloud and edge computing,” IEEE Internet Things J. , vol. 8, no. 11, pp. 9164–9176, 2021

    2021

  42. [50]

    Cache-assisted mobile edge computing over space-air-ground integrated networks for extended reality applications,

    S. Yoo, S. Jeong, J. Kim, and J. Kang, “Cache-assisted mobile edge computing over space-air-ground integrated networks for extended reality applications,” IEEE Internet Things J. , 2024

    2024

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.