pith. sign in

arxiv: 2604.17634 · v1 · submitted 2026-04-19 · 📡 eess.SP

RIS-Assisted Cell-Free Massive MIMO: RIS-MS Selection in FR1 and FR3

Alejandro de la Fuente , Fernando Galindo , Uriel Garc\'ia-B\'arbulo , Sandra-Noemy Arana-Alegre , Jan Garc\'ia-Morales This is my paper

Pith reviewed 2026-05-10 05:06 UTC · model grok-4.3

classification 📡 eess.SP
keywords RIScell-free massive MIMORIS-user associationspectral efficiencyLoS connectivityFR1 FR3 bandsP-MMSE combiningRicean fading
0
0 comments X

The pith

Assigning each RIS to a single user by line-of-sight connectivity raises spectral efficiency in cell-free massive MIMO.

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

The paper develops a framework for RIS-assisted cell-free massive MIMO systems in the FR1 and FR3 bands under realistic Ricean fading. It introduces a RIS-user association algorithm that pairs each surface with one user according to LoS conditions and then optimizes phase shifts accordingly. Numerical results show this selective pairing lifts spectral efficiency over random or exhaustive RIS configurations, with the gain most pronounced when the number of surfaces is moderate. The work also quantifies how pilot overhead grows with RIS density and element count, revealing that excessive training can erase the performance advantage. These observations point toward FR3 as a promising band for such deployments once channel estimation methods scale efficiently.

Core claim

The central claim is that a simple RIS-user association rule, which assigns each reconfigurable surface to exactly one user on the basis of line-of-sight connectivity and then tunes the surface phases for that link, produces higher spectral efficiency than either random RIS placement or exhaustive search over all surfaces, particularly when the number of RISs remains moderate; the same rule also exposes a clear trade-off in which added pilot symbols required for larger or denser RIS deployments can cancel the throughput gain.

What carries the argument

The RIS-user association algorithm that assigns each RIS to a single user according to LoS connectivity and then sets phase shifts for that dedicated link.

If this is right

  • The proposed single-user LoS assignment outperforms random and exhaustive RIS configurations when the number of surfaces is moderate.
  • Training overhead grows with RIS density and element count, so that beyond a certain point added pilots erase the spectral-efficiency gain.
  • FR3 bands become attractive for RIS-assisted cell-free MIMO once channel estimation overhead is controlled.
  • Scalable P-MMSE combining remains effective under the spatially correlated Ricean channels assumed in the model.

Where Pith is reading between the lines

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

  • The same LoS-based pairing rule might reduce computational load in real-time 6G schedulers that must reconfigure surfaces every few milliseconds.
  • Extending the rule to predict LoS availability from user location data rather than instantaneous channel estimates could further cut pilot overhead.
  • If multi-RIS cooperation becomes feasible, the current single-user restriction would need re-examination to capture additional array gains.

Load-bearing premise

Assigning each RIS to only one user on the basis of LoS connectivity is enough to set useful phase shifts without creating large multi-user interference or requiring joint optimization over all users and surfaces.

What would settle it

A direct comparison in which joint phase-shift optimization across multiple users and all RISs simultaneously produces measurably higher spectral efficiency than the single-user LoS assignment, especially at moderate RIS counts, would falsify the central claim.

Figures

Figures reproduced from arXiv: 2604.17634 by Alejandro de la Fuente, Fernando Galindo, Jan Garc\'ia-Morales, Sandra-Noemy Arana-Alegre, Uriel Garc\'ia-B\'arbulo.

Figure 1
Figure 1. Figure 1: Average sum spectral efficiency for K = 10 MSs and L = 20 APs as a function of the number of RISs (R = 0, . . . , 20) under FR1 and FR3. The curves compare the five RIS-configuration methods described in Sec. IV. Method 5 implements 16-element subarray grouping, reducing the separation overhead. • Method 2: Phase-shifts optimized using channels of all MSs. • Method 3: Phase-shifts optimized using a randoml… view at source ↗
Figure 2
Figure 2. Figure 2: CDF of the SE achieved with the proposed MS-RIS selection for [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Average sum spectral efficiency for equal-area RIS panels [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
read the original abstract

This paper explores the integration of reconfigurable intelligent surfaces (RISs) into cell-free massive multiple-input-multiple-output (CF-mMIMO) networks operating in FR1 and FR3 frequency bands. We present a comprehensive framework for analyzing RIS-assisted CF-mMIMO systems under realistic propagation conditions, accounting for frequency-dependent characteristics and RIS configurations. A novel RIS-user association algorithm is proposed to optimize phase-shift settings by assigning each RIS to a single user based on line of sight (LoS) connectivity. The system model incorporates spatially correlated Ricean fading channels and employs scalable partial-minimum mean square error (P-MMSE) combining. The numerical results demonstrate that the proposed RIS-user selection strategy significantly improves the spectral efficiency compared to random or exhaustive RIS configurations, particularly when the number of RISs is moderate. We also analyze the trade-off between training overhead and performance gains, showing that excessive pilot requirements can offset benefits when RIS density or element count increases. The results highlight the potential of the FR3 bands for RIS-assisted CF-mMIMO, provided advanced channel estimation techniques are adopted to mitigate overhead. These findings emphasize the importance of intelligent RIS-user pairing and scalable estimation methods for future 6G deployments.

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

1 major / 2 minor

Summary. The manuscript proposes a RIS-user association algorithm for RIS-assisted cell-free massive MIMO systems operating in FR1 and FR3 frequency bands. Each RIS is assigned to a single user based on line-of-sight connectivity, followed by per-pair phase-shift optimization. The system uses spatially correlated Rician fading channels and partial minimum mean square error (P-MMSE) combining at the access points. Numerical simulations show that this strategy yields higher spectral efficiency than random or exhaustive RIS configurations, especially with a moderate number of RISs, and analyze the trade-off with pilot training overhead.

Significance. If the central performance claims hold, the work is significant in demonstrating the value of intelligent RIS-user pairing for improving spectral efficiency in cell-free massive MIMO networks, particularly highlighting opportunities in FR3 bands. The use of realistic Ricean channels and scalable P-MMSE combining provides a solid foundation for the analysis, and the overhead trade-off discussion is a practical contribution for 6G system design.

major comments (1)
  1. [Proposed RIS-user association algorithm] The phase-shift optimization is described as LoS-driven and specific to each RIS-user pair. However, since the P-MMSE combiner's SINR depends on the aggregate effective channels including multi-user interference, it is unclear whether the per-pair optimization sufficiently mitigates interference for non-associated users. This assumption is load-bearing for the claim of significant SE improvements and requires explicit justification or a joint-optimization comparison.
minor comments (2)
  1. [Numerical results] The abstract mentions improvements 'particularly when the number of RISs is moderate' but the full manuscript should include more details on the exact parameter values used in simulations (e.g., RIS element count, pilot overhead) to allow reproducibility.
  2. Consider adding a reference to prior work on joint RIS phase optimization in multi-user settings for context.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and detailed review of our manuscript. We address the major comment below and have incorporated revisions to strengthen the presentation of our RIS-user association approach.

read point-by-point responses

  1. Referee: The phase-shift optimization is described as LoS-driven and specific to each RIS-user pair. However, since the P-MMSE combiner's SINR depends on the aggregate effective channels including multi-user interference, it is unclear whether the per-pair optimization sufficiently mitigates interference for non-associated users. This assumption is load-bearing for the claim of significant SE improvements and requires explicit justification or a joint-optimization comparison.

    Authors: We thank the referee for this insightful observation. Our RIS-user association assigns each RIS to a single user based on the strongest LoS path, with phase shifts then optimized to maximize the effective channel gain for that dedicated pair under the spatially correlated Rician model. The P-MMSE combiner at the APs operates on the aggregate effective channels (including contributions from all RISs and direct paths) and is designed to suppress multi-user interference via its partial MMSE structure. By strengthening the desired signal component through targeted LoS-based optimization, the resulting SINR benefits are realized even as interference is handled at the receiver. We acknowledge that a joint optimization over all RIS-user pairs could in principle further reduce interference leakage, but such an approach would scale poorly with the number of RISs and users and is outside the scope of the practical heuristic we propose. In the revised manuscript we have added a dedicated paragraph in Section IV-C that explicitly justifies the per-pair design, derives the effective SINR expression under this assignment, and discusses why the observed SE gains (relative to random and exhaustive baselines) remain valid. We have also included a brief complexity comparison to highlight the practicality of the method. revision: partial

Circularity Check

0 steps flagged

No circularity: performance gains shown via independent simulations

full rationale

The paper proposes a RIS-user association algorithm based on LoS connectivity and evaluates its spectral efficiency via numerical simulations under spatially correlated Rician fading with P-MMSE combining. No load-bearing derivation step reduces the claimed gains to fitted parameters, self-citations, or ansatzes by construction; the selection rule and phase optimization are presented as explicit algorithmic choices whose benefits are measured against random/exhaustive baselines in the results section. The framework is self-contained against external benchmarks and does not invoke uniqueness theorems or prior author work to force the outcome.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claim depends on standard wireless propagation models and the proposed selection heuristic. No new physical entities are postulated. Simulation-specific parameters such as RIS count and element numbers are tuned but not detailed in the abstract.

free parameters (2)
  • RIS element count and density
    Values are varied in simulations to demonstrate trade-offs with training overhead; specific fitted values not stated in abstract.
  • Pilot training overhead
    Analyzed as a trade-off parameter that can offset gains at high RIS density.
axioms (2)
  • domain assumption Spatially correlated Ricean fading channels
    Incorporated in the system model for realistic propagation in FR1 and FR3.
  • domain assumption Scalable partial minimum mean square error (P-MMSE) combining
    Employed at the receivers as the combining method.

pith-pipeline@v0.9.0 · 5540 in / 1362 out tokens · 49908 ms · 2026-05-10T05:06:15.473090+00:00 · methodology

discussion (0)

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

Reference graph

Works this paper leans on

19 extracted references · 19 canonical work pages

  1. [1]

    Ubiquitous cell-free massive MIMO communications,

    G. Interdonato, E. Björnson, H. Q. Ngo, P. Frenger, and E. G. Larsson, “Ubiquitous cell-free massive MIMO communications,” EURASIP J. Wireless Commun. and Netw., vol. 2019, no. 1, p. 197, 2019

    work page 2019

  2. [2]

    Cell-Free Millimeter-Wave Massive MIMO Systems With Limited Fronthaul Capacity,

    G. Femenias and F. Riera-Palou, “Cell-Free Millimeter-Wave Massive MIMO Systems With Limited Fronthaul Capacity,” IEEE Access, vol. 7, pp. 44 596–44 612, 2019

    work page 2019

  3. [3]

    Ultradense cell-free massive MIMO for 6G: Technical overview and open questions,

    H. Q. Ngo, G. Interdonato, E. G. Larsson, G. Caire, and J. G. Andrews, “Ultradense cell-free massive MIMO for 6G: Technical overview and open questions,” Proceedings of the IEEE, pp. 1– 27, 2024, early access

    work page 2024

  4. [4]

    Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network,

    Q. Wu and R. Zhang, “Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network,” IEEE Communications Magazine, vol. 58, no. 1, pp. 106–112, 2020

    work page 2020

  5. [5]

    Introduction to Multiple Antenna Communications and Reconfigurable Surfaces,

    E. Björnson and Ö. T. Demir, “Introduction to Multiple Antenna Communications and Reconfigurable Surfaces,” Boston-Delft: now publishers, 2024. [Online]. A vailable: http://dx.doi.org/10.1561/9781638283157

    work page doi:10.1561/9781638283157 2024

  6. [6]

    Smart radio environments empowered by reconfigurable AI meta-surfaces: an idea whose time has come,

    M. Renzo, M. Debbah, D. Phan-Huy, and et al., “Smart radio environments empowered by reconfigurable AI meta-surfaces: an idea whose time has come,” EURASIP J. Wireless Commun. and Netw., vol. 2019, no. 129, 2019

    work page 2019

  7. [7]

    Smart Electro- magnetic Skin Assisted Multicasting: Moving to 6G Intelligent Communications,

    A. de la Fuente and E. Martínez-de Rioja, “Smart Electro- magnetic Skin Assisted Multicasting: Moving to 6G Intelligent Communications,” in 2025 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2025, pp. 1–6

    work page 2025

  8. [8]

    Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming,

    Q. Wu and R. Zhang, “Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming,” IEEE Transactions on Wireless Communications, vol. 18, no. 11, pp. 5394–5409, 2019

    work page 2019

  9. [9]

    A joint precoding framework for wide- band reconfigurable intelligent surface-aided cell-free network,

    Z. Zhang and L. Dai, “A joint precoding framework for wide- band reconfigurable intelligent surface-aided cell-free network,” IEEE Transactions on Signal Processing, vol. 69, pp. 4085–4101, 2021

    work page 2021

  10. [10]

    Joint Distributed Precoding and Beamforming for RIS- Aided Cell-Free Massive MIMO Systems,

    P. Zhang, J. Zhang, H. Xiao, X. Zhang, D. W. K. Ng, and B. Ai, “Joint Distributed Precoding and Beamforming for RIS- Aided Cell-Free Massive MIMO Systems,” IEEE Transactions on Vehicular Technology, vol. 73, no. 4, pp. 5994–5999, 2024

    work page 2024

  11. [11]

    RIS-aided cell-free massive MIMO sys- tems for 6G: Fundamentals, system design, and applications,

    E. Shi, J. Zhang, H. Du, B. Ai, C. Yuen, D. Niyato, K. B. Letaief, and X. Shen, “RIS-aided cell-free massive MIMO sys- tems for 6G: Fundamentals, system design, and applications,” Proceedings of the IEEE, vol. 112, no. 4, pp. 331–364, 2024

    work page 2024

  12. [12]

    User association for reconfigurable intelligent surfaces aided cell-free networks,

    Q. Bie, Z. Liang, Y. Liu, Q. Wu, X. Zhao, and X. Y. Zhang, “User association for reconfigurable intelligent surfaces aided cell-free networks,” IEEE Transactions on Vehicular Technology, vol. 72, no. 11, pp. 14 456–14 467, 2023

    work page 2023

  13. [13]

    6G wireless systems: Vision, requirements, challenges, insights, and opportunities,

    H. Tataria, M. Shafi, A. F. Molisch, M. Dohler, H. Sjöland, and F. Tufvesson, “6G wireless systems: Vision, requirements, challenges, insights, and opportunities,” Proceedings of the IEEE, vol. 109, no. 7, pp. 1166–1199, 2021

    work page 2021

  14. [14]

    The Road Towards 6G: A Comprehensive Survey,

    W. Jiang, B. Han, M. A. Habibi, and H. D. Schotten, “The Road Towards 6G: A Comprehensive Survey,” IEEE Open Journal of the Communications Society, vol. 2, pp. 334–366, 2021

    work page 2021

  15. [15]

    Performance analysis of irs-assisted cell-free commu- nication,

    D. L. Galappaththige, D. Kudathanthirige, and G. Amara- suriya, “Performance analysis of irs-assisted cell-free commu- nication,” in 2021 IEEE Global Communications Conference (GLOBECOM), 2021, pp. 1–6

    work page 2021

  16. [16]

    Fundamental and practical performance assessment in monostatic ISAC: From sub-6GHz to sub-THz,

    E. M. Taghavi, H. Saarnisaari, and M. Juntti, “Fundamental and practical performance assessment in monostatic ISAC: From sub-6GHz to sub-THz,” in 2024 3rd International Con- ference on 6G Networking (6GNet), 2024, pp. 221–226

    work page 2024

  17. [17]

    Foundations of user-centric cell-free massive MIMO,

    Ö. T. Demir, E. Björnson, L. Sanguinetti et al., “Foundations of user-centric cell-free massive MIMO,” Foundations and Trends® in Signal Processing, vol. 14, no. 3-4, pp. 162–472, 2021

    work page 2021

  18. [18]

    Performance of Cell- Free Massive MIMO With Rician Fading and Phase Shifts,

    Ö. Özdogan, E. Björnson, and J. Zhang, “Performance of Cell- Free Massive MIMO With Rician Fading and Phase Shifts,” IEEE Transactions on Wireless Communications, vol. 18, no. 11, pp. 5299–5315, 2019

    work page 2019

  19. [19]

    Cell-free massive MIMO: User- centric approach,

    S. Buzzi and C. D’Andrea, “Cell-free massive MIMO: User- centric approach,” IEEE Wireless Commun. Lett., vol. 6, no. 6, pp. 706–709, 2017

    work page 2017