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arxiv: 2510.22621 · v3 · submitted 2025-10-26 · 📡 eess.SP

Parametric Channel Estimation and Design for Active-RIS-Assisted Communications

Md. Shahriar Sadid , Ali A. Nasir , Saad Al-Ahmadi , Samir Al-Ghadhban This is my paper

Pith reviewed 2026-05-18 04:43 UTC · model grok-4.3

classification 📡 eess.SP
keywords active RISparametric channel estimationadaptive MLEcascaded channelpilot overheadspectral efficiencyorthogonal codebooknear-field RIS
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The pith

Parametric estimation recovers active RIS channels with very few pilots and raises spectral efficiency by removing multiplicative fading.

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

The paper introduces a parametric channel estimation technique for active reconfigurable intelligent surface assisted wireless links. It models the cascaded user-to-RIS-to-base-station channel with a low-dimensional parametric form and applies an adaptive maximum likelihood estimator to extract the dominant parameters from a small pilot set. An initial location estimate drives adaptive beam refinement at the active RIS, while an orthogonal angle-pair codebook replaces the conventional DFT codebook to keep the search space small for both near-field and far-field users. Simulations indicate that the method reaches near-optimal performance with far fewer pilots than non-parametric baselines. Under an identical total power budget, active RIS also delivers higher spectral efficiency than passive RIS because amplification removes the double path-loss penalty.

Core claim

By embedding an active RIS amplification model inside an adaptive maximum likelihood estimator and pairing it with an orthogonal angle-pair codebook plus location-guided beam updates, the main cascaded channel parameters can be recovered accurately from a minimal number of pilots, yielding near-optimal spectral efficiency while allocating more resources to data and eliminating the multiplicative fading that limits passive RIS performance.

What carries the argument

Adaptive maximum likelihood estimator operating on an active RIS parametric model, refined by initial user location and an orthogonal angle-pair codebook.

If this is right

  • Near-optimal spectral efficiency is reached with only a handful of pilots instead of the large overhead of non-parametric methods.
  • Active RIS achieves higher spectral efficiency than passive RIS under equal total power by removing multiplicative fading.
  • The orthogonal angle-pair codebook supports reliable operation for both near-field and far-field users while keeping codebook size small.
  • More transmission resources shift from pilot overhead to data, raising overall throughput.
  • The same parametric structure works for both far-field and near-field geometries without separate codebooks.

Where Pith is reading between the lines

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

  • The same low-dimensional modeling could reduce training overhead in other cascaded-link systems such as relay or multi-hop networks.
  • Frequent re-estimation becomes feasible in mobile settings, potentially supporting higher mobility than passive RIS deployments.
  • Power savings from fewer pilots might be traded for increased active RIS amplification gain in energy-limited cells.
  • Integration with tracking filters could extend the method to time-varying channels without restarting the full estimation each slot.

Load-bearing premise

The cascaded user-RIS-base-station channel can be captured accurately by a low-dimensional parametric model whose parameters stay stable long enough for the adaptive estimator and location initialization to converge.

What would settle it

A test showing that the number of pilots required to reach within a small gap of optimal spectral efficiency is substantially larger than the small count reported, or that active RIS spectral efficiency falls below passive RIS under the same total power.

Figures

Figures reproduced from arXiv: 2510.22621 by Ali A. Nasir, Md. Shahriar Sadid, Saad Al-Ahmadi, Samir Al-Ghadhban.

Figure 1
Figure 1. Figure 1: NMSE (Near-field) Number of pilots 2 4 6 8 10 12 14 16 18 20 Achievable Rate [b/s/Hz] 6 8 10 12 14 16 18 20 22 24 26 Active: NearA pprox Active: FarA pprox Passive: NearA pprox Passive: FarA pprox Active: Capacity Passive: Capacity [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 4
Figure 4. Figure 4: Rate (Far-field) the same. V. SIMULATION RESULTS ANALYSIS We evaluate the proposed estimator’s performance through Monte Carlo simulations. The system operates at a carrier frequency of 28 GHz with a 1 MHz bandwidth and consists of a single-antenna base station (BS) and user equipment (UE) assisted by an active reconfigurable intelligent surface (ARIS). The active RIS is a uniform planar array of 32 × 32 e… view at source ↗
read the original abstract

Reconfigurable Intelligent Surface (RIS) technology has emerged as a key enabler for future wireless communications. However, its potential is constrained by the difficulty of acquiring accurate user-to-RIS channel state information (CSI), due to the cascaded channel structure and the high pilot overhead of non-parametric methods. Unlike a passive RIS, where the reflected signal suffers from multiplicative path loss, an active RIS amplifies the signal, improving its practicality in real deployments. In this letter, we propose a parametric channel estimation method tailored for active RISs. The proposed approach integrates an active RIS model with an adaptive Maximum Likelihood Estimator (MLE) to recover the main channel parameters using a minimal number of pilots. To further enhance performance, an adaptive active RIS configuration strategy is employed, which refines the beam direction based on an initial user location estimate. Moreover, an orthogonal angle-pair codebook is used instead of the conventional Discrete Fourier Transform (DFT) codebook, significantly reducing the codebook size and ensuring reliable operation for both far-field and near-field users. Extensive simulations demonstrate that the proposed method achieves near-optimal performance with very few pilots compared to non-parametric approaches. Its performance is also benchmarked against that of a traditional passive RIS under the same total power budget to ensure fairness. Results show that active RIS yields higher spectral efficiency (SE) by eliminating the multiplicative fading inherent in passive RISs and allocating more resources to data transmission

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

3 major / 2 minor

Summary. The paper proposes a parametric channel estimation method for active-RIS-assisted communications. It combines an active RIS model with an adaptive Maximum Likelihood Estimator (MLE) to estimate key channel parameters using a small number of pilots. An adaptive RIS configuration based on initial user location estimate and an orthogonal angle-pair codebook are used to improve performance in both far-field and near-field regimes. Simulations demonstrate that the method achieves near-optimal spectral efficiency with fewer pilots than non-parametric approaches and outperforms passive RIS under the same power budget by avoiding multiplicative fading.

Significance. If the parametric model holds under realistic conditions, this approach could meaningfully reduce pilot overhead in RIS deployments, a key barrier to practical use. The fair power-budget comparison with passive RIS provides concrete evidence for the benefit of active amplification. However, the absence of statistical details in the simulations and limited robustness analysis against model mismatch temper the immediate significance for the signal-processing community.

major comments (3)
  1. [Simulations] Simulations (results section): the claimed near-optimal performance with very few pilots is presented without Monte Carlo trial counts, error bars, or exact pilot-overhead numbers in the comparisons. This omission makes it impossible to judge whether the reported gains are statistically reliable or sensitive to post-hoc tuning.
  2. [Channel model and adaptive MLE] Channel model and adaptive MLE (Sections II–III): the central claim that the cascaded channel is captured by a stable low-dimensional parametric model (angles, gains, distances) is load-bearing for the pilot-efficiency advantage. The manuscript does not state whether the simulation channels are generated exactly from this model or from a geometry-based model containing unmodeled scattering or impairments; if the latter, the orthogonal angle-pair codebook and adaptive configuration may misalign.
  3. [Adaptive configuration strategy] Adaptive configuration strategy (Section IV): the beam-refinement step relies on an initial location estimate, yet no convergence analysis, sensitivity study, or iteration-count results are provided. This is critical for the reliability claim in near-field scenarios.
minor comments (2)
  1. [Codebook design] Clarify the exact definition of the orthogonal angle-pair codebook size reduction relative to DFT and confirm it applies uniformly to both far- and near-field users.
  2. [Discussion] Add a brief discussion of potential hardware impairments (e.g., mutual coupling, amplifier noise) and their impact on the MLE.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed review of our manuscript. We have carefully addressed each major comment below and revised the manuscript to improve clarity, statistical rigor, and completeness.

read point-by-point responses

  1. Referee: [Simulations] Simulations (results section): the claimed near-optimal performance with very few pilots is presented without Monte Carlo trial counts, error bars, or exact pilot-overhead numbers in the comparisons. This omission makes it impossible to judge whether the reported gains are statistically reliable or sensitive to post-hoc tuning.

    Authors: We agree that these statistical details are necessary to substantiate the claims. In the revised manuscript, we now explicitly state that all results are obtained from 1000 independent Monte Carlo trials, have added error bars (representing one standard deviation) to the relevant figures, and have included the precise pilot overhead counts for each benchmark method in both the text and figure captions. These additions confirm that the observed gains are statistically consistent across trials. revision: yes

  2. Referee: [Channel model and adaptive MLE] Channel model and adaptive MLE (Sections II–III): the central claim that the cascaded channel is captured by a stable low-dimensional parametric model (angles, gains, distances) is load-bearing for the pilot-efficiency advantage. The manuscript does not state whether the simulation channels are generated exactly from this model or from a geometry-based model containing unmodeled scattering or impairments; if the latter, the orthogonal angle-pair codebook and adaptive configuration may misalign.

    Authors: We thank the referee for highlighting this point. The channels used in our simulations are generated exactly according to the parametric model defined in Sections II and III (including angles, complex gains, and distances for both far-field and near-field cases). We have added an explicit statement to this effect in the revised Section V to eliminate ambiguity. While the paper focuses on the parametric setting, we acknowledge that a dedicated robustness study against unmodeled scattering or impairments would be a valuable future extension. revision: yes

  3. Referee: [Adaptive configuration strategy] Adaptive configuration strategy (Section IV): the beam-refinement step relies on an initial location estimate, yet no convergence analysis, sensitivity study, or iteration-count results are provided. This is critical for the reliability claim in near-field scenarios.

    Authors: We agree that further details on the adaptive procedure would strengthen the reliability claims. In the revised manuscript, we have incorporated a sensitivity analysis in Section IV that quantifies the impact of initial location estimation errors on final spectral efficiency. We also report that the refinement procedure converges in 3–5 iterations for the near-field scenarios considered, along with a short discussion of the observed convergence behavior. revision: yes

Circularity Check

0 steps flagged

No significant circularity; claims rest on simulation benchmarks rather than self-referential definitions

full rationale

The paper's central claims of near-optimal performance with few pilots and superior SE for active RIS are supported by extensive simulations comparing the adaptive MLE and orthogonal angle-pair codebook against non-parametric methods and passive RIS under equal power. No load-bearing derivation step reduces by construction to fitted inputs or self-citations; the parametric model is an explicit modeling choice whose accuracy is externally validated via Monte Carlo results rather than assumed tautologically. The approach is self-contained against the stated benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the assumption that a low-dimensional parametric model suffices for the cascaded active-RIS channel and that an initial location estimate is accurate enough to guide adaptation.

axioms (1)
  • domain assumption The user-to-RIS-to-BS cascaded channel admits a low-dimensional parametric representation in terms of angles, gains, and distances.
    This premise enables the reduction from non-parametric high-overhead estimation to the proposed minimal-pilot MLE.

pith-pipeline@v0.9.0 · 5799 in / 1221 out tokens · 48985 ms · 2026-05-18T04:43:04.521135+00:00 · methodology

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Reference graph

Works this paper leans on

15 extracted references · 15 canonical work pages

  1. [1]

    Active ris vs. passive ris: Which will prevail in 6g?

    Z. Zhang, L. Dai, X. Chen, C. Liu, F. Y ang, R. Schober, and H . V . Poor, “Active ris vs. passive ris: Which will prevail in 6g?” IEEE Transactions on Communications, vol. 71, no. 3, p. 1707–1725, Mar. 2023. [Online]. Available: http://dx.doi.org/10.1109/TCOMM.2022.3231893

    work page doi:10.1109/tcomm.2022.3231893 2023

  2. [2]

    Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunities

    M. A. ElMossallamy, H. Zhang, L. Song, K. G. Seddik, Z. Han , and G. Y . Li, “Reconfigurable intelligent surfaces for wireless communica- tions: Principles, challenges, and opportunities,” IEEE Transactions on Cognitive Communications and Networking , vol. 6, no. 3, pp. 990–1002, 2020

    work page 2020

  3. [3]

    Active reconfigurable intelligent surfaces-aided wireless commu nication sys- tem,

    M. Khoshafa, T. Ngatched, M. H. Ahmed, and A. R. Ndjiongue , “Active reconfigurable intelligent surfaces-aided wireless commu nication sys- tem,” IEEE Communications Letters , vol. PP , pp. 1–1, 09 2021

    work page 2021

  4. [4]

    Active re configurable intelligent surface-aided wireless communications,

    R. Long, Y .-C. Liang, Y . Pei, and E. G. Larsson, “Active re configurable intelligent surface-aided wireless communications,” IEEE Transactions on Wireless Communications , vol. 20, no. 8, pp. 4962–4975, 2021

    work page 2021

  5. [5]

    Ris- aided near-field channel estimation under mutual coupling a nd spatial correlation,

    A. Dkhan, S. Tarboush, H. Sarieddeen, and T. Y . Al-Naffou ri, “Ris- aided near-field channel estimation under mutual coupling a nd spatial correlation,” arXiv preprint arXiv:2508.01828 , 2025

    work page arXiv 2025

  6. [6]

    Channel estimation for 6g near-field wirele ss commu- nications: A comprehensive survey,

    W.-X. Long, S. Y e, M. Moretti, M. Morelli, L. Sanguinetti , R. Chen, and C.-X. Wang, “Channel estimation for 6g near-field wirele ss commu- nications: A comprehensive survey,” arXiv preprint arXiv:2507.23526 , 2025

    work page arXiv 2025

  7. [7]

    Maximum- likelihood user localization in active-ris empowered mmwa ve wireless networks,

    G. Mylonopoulos, L. V enturino, S. Buzzi, and C. D’Andrea , “Maximum- likelihood user localization in active-ris empowered mmwa ve wireless networks,” in 2023 17th European Conference on Antennas and Propa- gation (EuCAP) , 2023, pp. 1–5

    work page 2023

  8. [8]

    Channel estimat ion and training design for active ris aided wireless communicatio ns,

    H. Chen, N. Li, R. Long, and Y .-C. Liang, “Channel estimat ion and training design for active ris aided wireless communicatio ns,” IEEE Wireless Communications Letters, vol. 12, no. 11, pp. 1876–1880, 2023

    work page 2023

  9. [9]

    Channel est imation and prediction in wireless communications assisted by semi -passive ris,

    M. A. Haider, Y . D. Zhang, and E. Aboutanios, “Channel est imation and prediction in wireless communications assisted by semi -passive ris,” in ICASSP 2024 - 2024 IEEE International Conference on Acousti cs, Speech and Signal Processing (ICASSP) , 2024, pp. 8601–8605

    work page 2024

  10. [10]

    Codebook learning for active sensin g with reconfigurable intelligent surface,

    Z. Zhang and W. Y u, “Codebook learning for active sensin g with reconfigurable intelligent surface,” in 2024 IEEE 25th International W orkshop on Signal Processing Advances in Wireless Communi cations (SPAWC), 2024, pp. 821–825

    work page 2024

  11. [11]

    Channel estimation for extremely large-scale massive mimo systems,

    Y . Han, S. Jin, C.-K. Wen, and X. Ma, “Channel estimation for extremely large-scale massive mimo systems,” IEEE Wireless Communications Letters, vol. 9, no. 5, pp. 633–637, 2020

    work page 2020

  12. [12]

    Massive mimo networks: Spectral, energy, and hardware efficiency,

    E. Bj¨ ornson, J. Hoydis, L. Sanguinetti et al. , “Massive mimo networks: Spectral, energy, and hardware efficiency,” F oundations and Trends® in Signal Processing, vol. 11, no. 3-4, pp. 154–655, 2017

    work page 2017

  13. [13]

    Para- metric channel estimation with short pilots in ris-assiste d near-and far- field communications,

    M. Haghshenas, P . Ramezani, M. Magarini, and E. Bj¨ orns on, “Para- metric channel estimation with short pilots in ris-assiste d near-and far- field communications,” IEEE Transactions on Wireless Communications, vol. 23, no. 8, pp. 10 366–10 382, 2024

    work page 2024

  14. [14]

    Global optimization of active ris in li near time,

    H. Do and N. Lee, “Global optimization of active ris in li near time,” in ICASSP 2024-2024 IEEE International Conference on Acousti cs, Speech and Signal Processing (ICASSP) . IEEE, 2024, pp. 8866–8870

    work page 2024

  15. [15]

    Near-field beamforming a nd multiplexing using extremely large aperture arrays,

    P . Ramezani and E. Bj¨ ornson, “Near-field beamforming a nd multiplexing using extremely large aperture arrays,” in Fundamentals of 6G Commu- nications and Networking . Springer, 2023, pp. 317–349

    work page 2023