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REVIEW 5 major objections 4 minor 57 references

Receiving RISs: Enabling Channel Estimation and Autonomous Configuration

T0 review · 5 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A semi-passive RIS can estimate the channels of two multi-antenna users and configure its own reflection phases to approach capacity, all with a few receive radio-frequency chains.

desk verdict A plausible receiving-RIS extension with a coherent protocol, but Algorithm 2 disagrees with the ADMM derivation and the capacity-achieving label is too strong. read the letter →

arxiv 2506.10662 v3 pith:CXP6IGRB submitted 2025-06-12 eess.SP cs.ETcs.ITmath.IT

George C. Alexandropoulos , Konstantinos D. Katsanos , Evangelos Vlachos This is my paper · ORCID
classification eess.SPcs.ETcs.ITmath.IT
keywords reconfigurableintelligentsurfacessemi-passiveRISchannelestimationADMMbeamspacesparsitylow-rankmatrixcompletionrandomspatialabsorptionsamplingMIMOcapacityoptimization
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 is about making a reconfigurable intelligent surface (RIS) an active participant in its own operation. It claims that a 'receiving' RIS—one equipped with a small number of radio-frequency receive chains and a baseband processor—can estimate the MIMO channels between itself and two multi-antenna users, then use those estimates to set its own reflection phases so the end-to-end link approaches capacity. The method is a two-phase TDD protocol: users transmit non-orthogonal pilots, the RIS samples the incident field through random absorption profiles, and an ADMM algorithm recovers the channels by exploiting their beamspace sparsity and low-rank structure. The practical payoff is that the RIS no longer depends on an external node to learn channels or compute reflection coefficients, which matters for high-frequency systems where such overhead is expensive.

What carries the argument

Random spatial absorption sampling is the enabling measurement mechanism: at each of $T$ slots, the RIS selects $N_{\mathrm{RF}}$ absorption profiles uniformly at random, so the observed data $\mathbf{r}(t)=\omega(t)\circ(\mathbf{W}^T\mathbf{y}_{\mathrm{RIS}}(t))+\mathbf{n}(t)$ contains only a fraction $N_{\mathrm{RF}}/N_{\mathrm{RIS}}$ of entries of a linear transformation of the incident signal. The recovery model is the beamspace representation $\bar{H}=D_{\mathrm{RIS}}\bar{Z}D_{\mathrm{UEs}}^H$, where the $D$ matrices are DFT matrices; the optimization problem minimizes the nuclear norm of $\bar{H}$ plus the $\ell_1$ norm of $\bar{Z}$ subject to a data-fit term. ADMM splits this into a singular value thresholding step for $\bar{H}$ and a LASSO soft-thresholding step for $\bar{Z}$, which is what lets the estimator work with very few RF chains and with $T$ much smaller than $N_{\mathrm{RIS}}(N_1+N_2)$.

What would settle it

Take a one-chain receiving RIS with a single propagation path and an absorption codebook whose profiles repeat with the same period as the beamspace grid; if the proposed estimator's NMSE stops improving as $T$ grows and stays far above the least-squares bound, the random-sampling premise fails.

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Extended reading notes

Core claim

The chapter's central claim is that a receiving RIS—a metasurface with a small number of receive radio-frequency chains and an attached baseband processor—can play the role of both channel sounder and beamformer for the MIMO link it serves. In the proposed TDD protocol, the two multi-antenna terminals synchronously send non-orthogonal pilot sequences; the RIS, configured in tunable absorption states, samples the incident signal through random spatial absorption profiles $\boldsymbol{\Omega}$ and $\mathbf{W}$, producing a zero-padded received matrix $\mathbf{R} = \boldsymbol{\Omega}\circ(\mathbf{W}^T \mathbf{Y}_{\mathrm{RIS}}) + \mathbf{N}$. The baseband unit then solves a joint low-rank and sparse recovery problem in the beamspace, $\bar{H}=D_{\mathrm{RIS}}\bar{Z}D_{\mathrm{UEs}}^H$, using ADMM whose $\bar{H}$ update is singular value thresholding and whose $\bar{Z}$ update is a LASSO soft-thresholding step. With the recovered $\hat{H}_1$ and $\hat{H}_2$, the same controller runs a projected gradient ascent on the mutual-information objective to set the reflection phases, optionally quantized. The simulations show NMSE improving over least-squares and other benchmarks, and achievable rates close to the perfect-CSI upper bound at high SNR, even with one RF chain and two-bit phase quantization.

Load-bearing premise

The load-bearing premise is that a few randomly chosen absorption profiles always see the whole signal space without blind spots, a property the paper asserts rather than proves.

Editorial extensions

If this is right

  • With the proposed protocol, the RIS controller becomes the node that performs channel estimation and reflection optimization, so no external feedback loop is needed to ship channel estimates or phase profiles back and forth.
  • Because the estimator uses beamspace sparsity and low rank, training can use $T$ much smaller than $N_{\mathrm{RIS}}(N_1+N_2)$; in simulations $T=500$–$2000$ was enough for surfaces with $100$–$256$ elements.
  • At high SNR and with $T=2000$, the achievable rate with estimated channels nearly reaches the perfect-CSI upper bound even for a single receive RF chain.
  • Quantizing reflection phases to 2 bits costs negligible rate at high SNR, which makes low-resolution metasurface hardware viable.

Reading between the lines

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

  • Inference: the same receive hardware and beamspace estimates could double as a localization or direction-finding front end, since the recovered $\bar{Z}$ encodes the paths' angles of arrival; the paper cites direction estimation only as related work.
  • Inference: the training-length-to-RF-chain tradeoff seen in the simulations—partially connected with two chains tracks fully connected with one—suggests a designer can trade wiring complexity against chain count; a systematic exchange-rate analysis would be a natural follow-up.
  • Inference: extending the protocol to more than two users would likely require scheduling or contention among user pairs, and the ADMM structure would need to keep the concatenated beamspace matrix recoverable; the paper lists this as future work but does not analyze the overhead.
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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

5 major / 4 minor

Summary. The manuscript proposes a semi-passive 'receiving RIS' architecture with RF chains that directly receive pilot signals from two multi-antenna UEs via tunable absorption profiles. It formulates the joint estimation of the two RIS-UE channels as a low-rank and beamspace-sparse optimization problem (OP2) and proposes an ADMM-based algorithm (Algorithm 2) that alternates between singular value thresholding and soft-thresholded LASSO steps. Using the channel estimates, the RIS controller then optimizes its reflection phase profile by a relaxed projected gradient ascent followed by quantization (Algorithm 1). The paper reports numerical NMSE and achievable-rate results showing that the proposed estimator outperforms least-squares benchmarks and that imperfect CSI at the RIS produces rates close to the perfect-CSI upper bound.

Significance. If the algorithmic claims are correct, the paper addresses a timely problem: equipping RISs with limited RF reception capability for on-board channel estimation and autonomous reflection optimization, with potential savings in pilot overhead and control signalling. The system model is explicit, the protocol is clearly described, and the numerical study covers several hardware configurations, including partially-connected reception. However, the value of the contribution rests on Algorithm 2 actually solving the derived ADMM problem and on the reported superiority not being an artifact of undisclosed tuning. At present, the derivations and the pseudocode disagree in load-bearing places, and the 'capacity-achieving' reflection design is a heuristic with no optimality guarantee. These issues need to be fixed before the central claims can be accepted.

major comments (5)
  1. [Section 3.2.2, Eq. (35)] The LASSO subproblem (33)-(34) is claimed to be solved exactly by z = S_{τ_Z}(Φ† ξ) in (35). This is exact only when Φ has orthonormal columns. Here Φ = [Φ1^T, sqrt(ρ)Φ2^T]^T, where Φ1 = diag(vec(Ω))((D_UEs^H S)^T ⊗ W^T P^H D_RIS) contains a 0/1 mask and Φ2 = D_UEs^* ⊗ D_RIS is unitary; the stacked matrix is in general not orthonormal. Therefore the update in Steps 3-4 of Algorithm 2 is not the exact minimizer of (33), and the statement in Section 3.2 that convergence follows from the convergence properties of SVT and LASSO is unsupported. Because the numerical results in Section 4 are obtained with Algorithm 2, this mismatch directly affects the central comparison claims.
  2. [Section 3.2.1 and Algorithm 2, Step 2] The derived H-update in Eq. (28) is SVT_{τ_Y/ρ}(D_RIS Z D_UEs^H - (2/ρ)Γ), but Algorithm 2 uses SVT_ρ(D_RIS Z D_UEs^H - (4/ρ)Γ). The threshold and the dual-variable scaling disagree with the derivation. Consequently, the implemented iteration is not the ADMM whose optimality and convergence properties are discussed in Section 3.2. The convergence curves in Figs. 3-4 therefore validate a different algorithm, and they cannot be cited as evidence for the proposed ADMM estimator.
  3. [Section 2.1.2, OP1 and Algorithm 1] The paper calls the reflection design 'capacity-achieving', but Algorithm 1 is a projected gradient ascent on a continuous relaxation of OP1, followed by a quantization step in Eq. (9). No proof is given that this procedure reaches a global optimum of the nonconvex discrete problem OP1, nor is a bound on the quantization loss provided. The claim in the abstract and in Section 1.1 that the RIS controller designs the 'capacity-achieving reflection phase configuration' is therefore overstated; the scheme should be presented as a heuristic with empirical support.
  4. [Section 3.1, OP2] The paper asserts that random spatial absorption sampling 'captures the entire signal space' and enables recovery of H1 and H2 with T training symbols, but it does not state any coherence, incoherence, or restricted-isometry conditions on the mask matrices Ω and W under which the low-rank/sparse recovery is guaranteed. In particular, the partially-connected architecture produces a structured sampling pattern rather than fully independent random measurements, so the matrix-completion premise is not automatic. A recovery guarantee, or at least a precise statement of the required conditions, is needed to support the claim that the estimator is doing more than fitting a heuristic optimization.
  5. [Section 4] The numerical evaluation never reports the values of the algorithm parameters τ_Y, τ_Z, and ρ used in Algorithm 2, nor the step size μ used in Algorithm 1, nor the initial phase vector for Algorithm 1. No sensitivity analysis with respect to these parameters is provided. Since the NMSE and rate comparisons in Section 4 depend on these choices, the reported superiority over the benchmarks is not reproducible without this information; the paper should disclose all tuning parameters and ideally show that performance is insensitive to reasonable variations.
minor comments (4)
  1. [Abstract and Section 4] The acronym NMSE is expanded as 'Mean Square Error' in the Introduction and as 'Normalized Mean Squared Error' in Section 4; the expansion should be consistent.
  2. [Reference [52]] The page range of the SVT paper by Cai, Candès, and Shen is given as '1956-1952', which appears to be a typo; please correct the bibliographic details.
  3. [Figures 3 and 4] The captions and the surrounding text refer to 'three different versions' of the fully-connected architecture, but the figures are not visible in the text and the text does not clearly state which curves correspond to NRF = 1, 4, and 360; the figures should include a legend and the caption should be explicit.
  4. [Section 3.2.1] The sentence 'Let us introduce the weight parameters τ_Y and τ_Z that depend on the number of propagation paths' is confusing because these parameters were already introduced in OP2; the sentence should be removed or rephrased to describe a dependence that is used later.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the channel estimator is a self-contained inverse-problem solution, and the noted algorithmic mismatches are correctness issues rather than input-output circularity.

full rationale

The paper's derivation chain does not reduce any predicted quantity to an input by construction. OP2 is a standard penalized inverse problem whose data-fidelity term is the received-pilot model in (5) and (17); the ADMM updates in (27)-(30) are derived from that objective, and the channel estimates are outputs of the optimization, not fitted values fed back into the estimator. The true channel matrices enter only in simulation to synthesize R and in the NMSE evaluation (41), which is a conventional test protocol, not circularity. The RIS reflection phase configuration computed by Algorithm 1 is a design output obtained from the estimates and then evaluated against the true channels, which is the standard imperfect-CSI assessment rather than a circular prediction. Self-citations to [29], [34], [35], and [36] are used for hardware context and technique extension, but no load-bearing uniqueness theorem or ansatz is imported solely from those citations; the core ADMM formulation and updates are given in the paper itself. The unsupported assertions, such as random spatial absorption sampling capturing the entire signal space (Section 3.1, without an incoherence or recovery guarantee), the convergence claim 'following the convergence properties of SVT and LASSO' (Section 3.2), and the mismatches between Algorithm 2 and the derived updates (Eq. (28) versus Step 2, and the soft-thresholded pseudoinverse in (35) being exact only for orthonormal Phi-bar), are correctness and robustness concerns, not circular dependencies. Correcting any of these would not change the fact that the estimator's output is not baked into its input. Therefore, no circularity is present.

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

The central claim rests on the standard ADMM/SVT/LASSO machinery plus domain assumptions about channel sparsity and random sampling. No new physical entities are introduced. The main unstated premises are the recovery conditions for matrix completion under structured random sampling and the choice of regularization weights.

free parameters (4)
  • τY = not reported
    Nuclear norm weight in OP2; said to depend on number of paths P,Q but no formula given; likely tuned for simulations.
  • τZ = not reported
    ℓ1 weight in OP2; same as above.
  • ρ = not reported
    ADMM stepsize in (0,1); chosen without stated criterion.
  • µ = 2.5
    Step size of the projected gradient ascent in Algorithm 1 for reflection phase optimization.
assumptions (4)
  • domain assumption Beamspace sparsity and low rank of \bar H
    H1 and H2 are modeled with P and Q paths in (10) and (14), so \bar H has low rank and sparse beamspace representation; this is central to OP2.
  • domain assumption Random absorption sampling captures the signal space
    Section 3.1 states the RIS random spatial absorption sampling captures the entire signal space, but no incoherence or RIP proof is given.
  • standard math ADMM convergence properties for SVT and LASSO apply
    Convergence analysis in Section 3.2 is asserted to follow from SVT and LASSO convergence, not proved.
  • domain assumption Lorentzian-constrained phase model
    Absorption and reflection coefficients are restricted to codebooks Wa and Wr in (3) and (6); capacity claims depend on this model.

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Cite this review

Pith. "Pith review of Receiving RISs: Enabling Channel Estimation and Autonomous Configuration." pith.science (2026). https://pith.science/paper/CXP6IGRB

@misc{pith2026250610662,
  author       = {Pith},
  title        = {Pith review of: Receiving RISs: Enabling Channel Estimation and Autonomous Configuration},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CXP6IGRB}},
  note         = {Machine review of arXiv:2506.10662}
}
read the original abstract

This chapter focuses on a hardware architecture for semi-passive Reconfigurable Intelligent Surfaces (RISs) and investigates its consideration for boosting the performance of Multiple-Input Multiple-Output (MIMO) communication systems. The architecture incorporates a single or multiple radio-frequency chains to receive pilot signals via tunable absorption phase profiles realized by the metasurface front end, as well as a controller encompassing a baseband processing unit to carry out channel estimation, and consequently, the optimization of the RIS reflection coefficients. A novel channel estimation protocol, according to which the RIS receives non-orthogonal training pilot sequences from two multi-antenna terminals via tunable absorption phase profiles, and then, estimates the respective channels via its signal processing unit, is presented. The channel estimates are particularly used by the RIS controller to design the capacity-achieving reflection phase configuration of the metasurface front end. The proposed channel estimation algorithm, which is based on the Alternating Direction Method of Multipliers (ADMM), profits from the RIS random spatial absorption sampling to capture the entire signal space, and exploits the beamspace sparsity and low-rank properties of extremely large MIMO channels, which is particularly relevant for communication systems at the FR3 band and above. Our extensive numerical investigations showcase the superiority of the proposed channel estimation technique over benchmark schemes for various system and RIS hardware configuration parameters, as well as the effectiveness of using channel estimates at the RIS side to dynamically optimize the possibly phase-quantized reflection coefficients of its unit elements.

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Reference graph

Works this paper leans on

57 extracted references · 56 canonical work pages

  1. [29]

    In: Proc

    Alexandropoulos, G.C., Vlachos, E.: A hardware architecture for reconfigurable intel- ligent surfaces with minimal active elements for explicit channel estimation. In: Proc. Int. Conf. Acoustics, Speech, and Signal Process. (ICASSP). pp. 9175–9179. Barcelona, Spain (May 2020)

    work page 2020

  2. [1]

    EURASIP Journal on Wireless Communications and Networking (103), 1–38 (2023)

    Alexandropoulos, G.C., Phan-Huy, D.T., Katsanos, K.D., Crozzoli, M., Wymeersch, H., Popovski, P., Ratajczak, P., B´ en´ edic, Y., Hamon, M.H., Herraiz Gonzalez, S., Mursia, P., Rossanese, M., Sciancalepore, V., Gros, J.B., Terranova, S., Gradoni, G., Di Lorenzo, P., Rahal, M., Denis, B., D’Errico, R., Clemente, A., Calvanese Strinati, E.: RIS-enabled smar...

    work page 2023

  3. [2]

    IEEE Vehicular Technology Magazine 19(3), 27–47 (2024)

    Basar, E., Alexandropoulos, G.C., Liu, Y., Wu, Q., Jin, S., Yuen, C., Dobre, O.A., Schober, R.: Reconfigurable intelligent surfaces for 6G: Emerging hardware architec- tures, applications, and open challenges. IEEE Vehicular Technology Magazine 19(3), 27–47 (2024)

    work page 2024

  4. [3]

    In: Proc

    Matos, S., Ma, Y., Luo, Q., Deuermeier, J., Lucci, L., Gavriilidis, P., Kiazadeh, A., Lain-Rubio, V., Phan, T.D., Soh, P.J., Clemente, A., Pessoa, L.M., Alexandropoulos, G.C.: Reconfigurable intelligent surfaces for thz: Hardware impairments and switching technologies. In: Proc. International Conference on Electromagnetics in Advanced Ap- plications and I...

    work page 2024

  5. [4]

    IEEE Trans

    Huang, C., Zappone, A., Alexandropoulos, G.C., Debbah, M., Yuen, C.: Reconfigurable intelligent surfaces for energy efficiency in wireless communication. IEEE Trans. Wire- less Commun. 18(8), 4157–4170 (Aug 2019)

    work page 2019

  6. [5]

    EURASIP J

    Di Renzo, M., et al.: Smart radio environments empowered by AI reconfigurable meta- surfaces: An idea whose time has come. EURASIP J. Wireless Commun. Netw. 129, 1–20 (May 2019)

    work page 2019

  7. [6]

    IEEE Communications Magazine 59(6), 28–34 (2021)

    Alexandropoulos, G.C., Shlezinger, N., del Hougne, P.: Reconfigurable intelligent sur- faces for rich scattering wireless communications: Recent experiments, challenges, and opportunities. IEEE Communications Magazine 59(6), 28–34 (2021)

    work page 2021

  8. [7]

    IEEE Access 12, 160464–160494 (2024)

    Ghaneizadeh, A., Gavriilidis, P., Joodaki, M., Alexandropoulos, G.C.: Metasurface energy harvesters: State-of-the-art designs and their potential for energy sustainable reconfigurable intelligent surfaces. IEEE Access 12, 160464–160494 (2024)

    work page 2024

Show all 57 references
  1. [8]

    IEEE Commun

    Liaskos, C., Nie, S., Tsioliaridou, A.I., Pitsillides, A., Ioannidis, S., Akyildiz, I.F.: A new wireless communication paradigm through software-controlled metasurfaces. IEEE Commun. Mag. 56(9), 162–169 (Sep 2018)

    2018

  2. [9]

    Jian, M., Alexandropoulos, G.C., Basar, E., Huang, C., Liu, R., Liu, Y., Yuen, C.: Reconfigurable intelligent surfaces for wireless communications: Overview of hardware designs, channel models, and estimation techniques. Intell. Converged Netw. 3(1), 1–32 (Mar 2022)

    2022

  3. [10]

    Proceedings of the IEEE 110(9), 1312–1338 (2022) 32 George C

    Swindlehurst, A.L., Zhou, G., Liu, R., Pan, C., Li, M.: Channel estimation with recon- figurable intelligent surfaces—a general framework. Proceedings of the IEEE 110(9), 1312–1338 (2022) 32 George C. Alexandropoulos, Konstantinos D. Katsanos, and Evangelos Vlachos

    2022

  4. [11]

    In: Proc

    Mishra, D., Johansson, H.: Channel estimation and low-complexity beamforming de- sign for passive intelligent surface assisted MISO wireless energy transfer. In: Proc. IEEE ICASSP. pp. 4659–4663. Brighton, UK (Apr 2019)

    2019

  5. [12]

    IEEE Wireless Communications Letters 9(2), 210–214 (2020)

    He, Z.Q., Yuan, X.: Cascaded channel estimation for large intelligent metasurface assisted massive MIMO. IEEE Wireless Communications Letters 9(2), 210–214 (2020)

    2020

  6. [13]

    IEEE Wireless Communications Letters 9(4), 518–522 (2020)

    Zheng, B., Zhang, R.: Intelligent reflecting surface-enhanced ofdm: Channel estimation and reflection optimization. IEEE Wireless Communications Letters 9(4), 518–522 (2020)

    2020

  7. [14]

    IEEE Transactions on Wireless Communications 19(10), 6607–6620 (2020)

    Wang, Z., Liu, L., Cui, S.: Channel estimation for intelligent reflecting surface assisted multiuser communications: Framework, algorithms, and analysis. IEEE Transactions on Wireless Communications 19(10), 6607–6620 (2020)

    2020

  8. [17]

    IEEE Transactions on Communications 69(6), 4144–4157 (2021)

    Wei, L., Huang, C., Alexandropoulos, G.C., Yuen, C., Zhang, Z., Debbah, M.: Chan- nel estimation for RIS-empowered multi-user MISO wireless communications. IEEE Transactions on Communications 69(6), 4144–4157 (2021)

    2021

  9. [18]

    IEEE Transactions on Wireless Communications 20(2), 741–754 (2021)

    Lin, S., Zheng, B., Alexandropoulos, G.C., Wen, M., Renzo, M.D., Chen, F.: Reconfig- urable intelligent surfaces with reflection pattern modulation: Beamforming design and performance analysis. IEEE Transactions on Wireless Communications 20(2), 741–754 (2021)

    2021

  10. [19]

    IEEE Transactions on Communications 70(7), 4640–4655 (2022)

    Wei, L., Huang, C., Guo, Q., Yang, Z., Zhang, Z., Alexandropoulos, G.C., Debbah, M., Yuen, C.: Joint channel estimation and signal recovery for RIS-empowered multiuser communications. IEEE Transactions on Communications 70(7), 4640–4655 (2022)

    2022

  11. [20]

    IEEE Transactions on Signal Process- ing 70, 1478–1492 (2022)

    Zhou, G., Pan, C., Ren, H., Popovski, P., Swindlehurst, A.L.: Channel Estimation for RIS-Aided Multiuser Millimeter-Wave Systems. IEEE Transactions on Signal Process- ing 70, 1478–1492 (2022)

    2022

  12. [21]

    IEEE Transactions on Wireless Communications 23(12), 19696–19712 (2024)

    Bayraktar, M., Gonz´ alez-Prelcic, N., Alexandropoulos, G.C., Chen, H.: Ris-aided joint channel estimation and localization at mmwave under hardware impairments: A dictio- nary learning-based approach. IEEE Transactions on Wireless Communications 23(12), 19696–19712 (2024)

    2024

  13. [22]

    IEEE Wireless Communications Letters 9(11), 1845–1849 (2020)

    You, C., Zheng, B., Zhang, R.: Fast beam training for irs-assisted multiuser commu- nications. IEEE Wireless Communications Letters 9(11), 1845–1849 (2020)

    2020

  14. [23]

    In: 2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM)

    Alexandropoulos, G.C., Jamali, V., Schober, R., Poor, H.V.: Near-field hierarchical beam management for RIS-enabled millimeter wave multi-antenna systems. In: 2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM). pp. 460–464 (2022)

    2022

  15. [24]

    IEEE Wireless Communications 29(6), 64–71 (2022)

    Wang, P., Fang, J., Zhang, W., Chen, Z., Li, H., Zhang, W.: Beam training and alignment for RIS-assisted millimeter-wave systems: State of the art and beyond. IEEE Wireless Communications 29(6), 64–71 (2022)

    2022

  16. [25]

    EURASIP Journal on Wireless Communications and Networking (86), 1–23 (2023)

    Rahal, M., Denis, B., Keykhosravi, K., Keskin, M.F., Uguen, B., Alexandropoulos, G.C., Wymeersch, H.: Performance of RIS-aided nearfield localization under beams approximation from real hardware characterization. EURASIP Journal on Wireless Communications and Networking (86), ...

    2023

  17. [26]

    IEEE Communica- tions Letters 26(4), 932–936 (2022)

    Jamali, V., Alexandropoulos, G.C., Schober, R., Poor, H.V.: Low-to-zero-overhead irs reconfiguration: Decoupling illumination and channel estimation. IEEE Communica- tions Letters 26(4), 932–936 (2022)

    2022

  18. [27]

    IEEE Access 10, 113200– 113213 (2022) Receiving RISs: Enabling Channel Estimation and Autonomous Configuration 33

    Chen-Hu, K., Alexandropoulos, G.C., Armada, A.g.: Differential data-aided beam training for RIS-empowered multi-antenna communications. IEEE Access 10, 113200– 113213 (2022) Receiving RISs: Enabling Channel Estimation and Autonomous Configuration 33

    2022

  19. [28]

    ITU Journal on Future and Evolving Technologies 3(2), 1–14 (2022)

    Chen-Hu, K., Alexandropoulos, G.C., Armada, A.g.: Non-coherent modulation with random phase configurations in RIS-empowered cellular MIMO systems. ITU Journal on Future and Evolving Technologies 3(2), 1–14 (2022)

    2022

  20. [30]

    IEEE Wireless Commun

    Alexandropoulos, G.C., Vinieratou, I., Wymeersch, H.: Localization via multiple re- configurable intelligent surfaces equipped with single receive RF chains. IEEE Wireless Commun. Lett. 11(5), 1072–1076 (May 2022)

    2022

  21. [31]

    IEEE Access 9, 44304–44321 (2021)

    Taha, A., Alrabeiah, M., Alkhateeb, A.: Enabling large intelligent surfaces with com- pressive sensing and deep learning. IEEE Access 9, 44304–44321 (2021)

    2021

  22. [32]

    IEEE Vehicular Technology Magazine 19(1), 75–84 (2024)

    Alexandropoulos, G.C., Shlezinger, N., Alamzadeh, I., Imani, M.F., Zhang, H., Eldar, Y.C.: Hybrid reconfigurable intelligent metasurfaces: Enabling simultaneous tunable reflections and sensing for 6G wireless communications. IEEE Vehicular Technology Magazine 19(1), 75–84 (2024)

    2024

  23. [33]

    IEEE Transactions on Communications 71(4), 2441–2456 (2023)

    Zhang, H., Shlezinger, N., Alexandropoulos, G.C., Shultzman, A., Alamzadeh, I., Imani, M.F., Eldar, Y.C.: Channel estimation with hybrid reconfigurable intelligent metasurfaces. IEEE Transactions on Communications 71(4), 2441–2456 (2023)

    2023

  24. [34]

    IEEE Signal Process

    Vlachos, E., Alexandropoulos, G.C., Thompson, J.: Massive MIMO channel estimation for millimeter wave systems via matrix completion. IEEE Signal Process. Lett. 11(11), 1675–1679 (Nov 2018)

    2018

  25. [35]

    Vlachos, E., Alexandropoulos, G.C., Thompson, J.: Wideband MIMO channel estima- tion for hybrid beamforming millimeter wave systems via random spatial sampling. IEEE J. Sel. Topics Signal Process. 13(5), 1136–1150 (Sep 2019)

    2019

  26. [36]

    IEEE Wireless Communications 28(2), 106–113 (2021)

    Shlezinger, N., Alexandropoulos, G.C., Imani, M.F., Eldar, Y.C., Smith, D.R.: Dy- namic metasurface antennas for 6G extreme massive MIMO communications. IEEE Wireless Communications 28(2), 106–113 (2021)

    2021

  27. [37]

    Journal of the Royal Statistical Society

    Tibshirani, R.: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (methodological) (Wiley 58 (1): 267–88 JSTOR 2346178, 1996)

    1996

  28. [38]

    IEEE Open Journal of the Communications Society 5, 4383–4399 (2024)

    Saggese, F., Croisfelt, V., Kotaba, R., Stylianopoulos, K., Alexandropoulos, G.C., Popovski, P.: On the impact of control signaling in RIS-empowered wireless commu- nications. IEEE Open Journal of the Communications Society 5, 4383–4399 (2024)

    2024

  29. [39]

    IEEE Transactions on Wire- less Communications 22(12), 8736–8750 (2023)

    Moustakas, A.L., Alexandropoulos, G.C., Debbah, M.: Reconfigurable intelligent sur- faces and capacity optimization: A large system analysis. IEEE Transactions on Wire- less Communications 22(12), 8736–8750 (2023)

    2023

  30. [40]

    IEEE Vehicular Technology Magazine 17(4), 83–91 (2022)

    Alexandropoulos, G.C., Islam, M.A., Smida, B.: Full-duplex massive multiple-input, multiple-output architectures: Recent advances, applications, and future directions. IEEE Vehicular Technology Magazine 17(4), 83–91 (2022)

    2022

  31. [41]

    IEEE Com- mun

    Kunz, A., Baskaran, S.B.M., Alexandropoulos, G.C.: Lightweight security for ambient- powered programmable reflections with reconfigurable intelligent surfaces. IEEE Com- mun. Standards Mag. (early access, 2025)

    2025

  32. [42]

    IEEE Transactions on Wireless Communications 23(9), 12054–12067 (2024)

    Xu, J., You, L., Alexandropoulos, G.C., Yi, X., Wang, W., Gao, X.: Near-field wide- band extremely large-scale MIMO transmissions with holographic metasurface-based antenna arrays. IEEE Transactions on Wireless Communications 23(9), 12054–12067 (2024)

    2024

  33. [43]

    Gavriilidis, P., Alexandropoulos, G.C.: How do microstrip losses impact near-field beam depth in dynamic metasurface antennas? In: Proc. EUSIPCO. Palermo, Italy (2025)

    2025

  34. [44]

    Cambridge Uni- versity Press (2018)

    Heath Jr, R.W., Lozano, A.: Foundations of MIMO communication. Cambridge Uni- versity Press (2018)

    2018

  35. [45]

    Zhang, S., Zhang, R.: Capacity characterization for intelligent reflecting surface aided mimo communication. IEEE J. Sel. Areas Commun. 38(8), 1823–1838 (Aug 2020) 34 George C. Alexandropoulos, Konstantinos D. Katsanos, and Evangelos Vlachos

    2020

  36. [46]

    IEEE Trans

    Perovi´ c, N.S., Tran, L.N., Di Renzo, M., Flanagan, M.F.: Achievable rate optimiza- tion for mimo systems with reconfigurable intelligent surfaces. IEEE Trans. Wireless Commun. 20(6), 3865–3882 (Jun 2021)

    2021

  37. [47]

    IEEE Wireless Commun

    Sirojuddin, A., Suryani, T., Alouini, M.S., et al.: Mimo capacity maximization with a practical model for reconfigurable intelligent surfaces. IEEE Wireless Commun. Lett. (early access, 2024)

    2024

  38. [48]

    In: Proc

    Huang, C., Alexandropoulos, G.C., Zappone, A., Debbah, M., Yuen, C.: Energy effi- cient multi-user MISO communication using low resolution large intelligent surfaces. In: Proc. IEEE Globecom Workshop GC Wkshps. pp. 1–6. Abu Dhabi, UAE (Dec 2018)

    2018

  39. [49]

    IEEE Trans

    Wu, Q., Zhang, R.: Beamforming optimization for wireless network aided by intelligent reflecting surface with discrete phase shifts. IEEE Trans. Commun. 68(3), 1838–1851 (Mar 2019)

    2019

  40. [50]

    Di, B., Zhang, H., Song, L., Li, Y., Han, Z., Poor, H.V.: Hybrid beamforming for reconfigurable intelligent surface based multi-user communications: Achievable rates with limited discrete phase shifts. IEEE J. on Sel. Areas Commun. 38(8), 1809–1822 (Aug 2020)

    2020

  41. [51]

    IEEE Transac- tions on Antennas and Propagation 61(7), 3814–3827 (2013)

    Brady, J., Behdad, N., Sayeed, A.M.: Beamspace MIMO for millimeter-wave commu- nications: System architecture, modeling, analysis, and measurements. IEEE Transac- tions on Antennas and Propagation 61(7), 3814–3827 (2013)

    2013

  42. [52]

    Cai, J.F., Candes, E.J., Shen, Z.: A singular value thresholding algorithm for matrix completion. SIAM J. OPTIM p. 1956–1952 (vol 20, no 4, 2010)

    1956

  43. [53]

    Linear Algebra Appl

    Baksalary, J., Baksalary, O.: Particular formulae for the moore–penrose inverse of a columnwise partitioned matrix. Linear Algebra Appl. p. 16–23 (vol 421, 2007)

    2007

  44. [54]

    Langville, A.N., Steward, W.J.: The kronecker product and stochastic automata net- works. JCoAM p. 429–447 (vol 147, no 2, 2004)

    2004

  45. [55]

    SIAM Review p

    Greville, T.N.E.: Note on the generalized inverse of a matrix product. SIAM Review p. 518–521 (vol 8, no 4, 1966)

    1966

  46. [56]

    arXiv preprint arXiv:2502.10693 (2025)

    Alexandropoulos, G.C., Gavras, I.: Extremely large full duplex MIMO for simultane- ous downlink communications and monostatic sensing at sub-THz frequencies. arXiv preprint arXiv:2502.10693 (2025)

    2025 arXiv

  47. [57]

    In: Proc

    Stamatelis, G., Gavriilidis, P., Fakhreddine, A., Alexandropoulos, G.C.: On the detec- tion of non-cooperative RISs: Scan B-testing via deep support vector data description. In: Proc. IEEE Int. Conf. Commun. Montreal, Canada (Jun 2025)

    2025

  48. [58]

    IEEE Trans

    Moustakas, A.L., Alexandropoulos, G.C.: MIMO mac empowered by reconfigurable intelligent surfaces: Capacity region and large system analysis. IEEE Trans. Wireless Commun. (early access, 2024)

    2024

  49. [59]

    In: Proc

    Gavriilidis, P., Atzeni, I., Alexandropoulos, G.C.: Metasurface-based receivers with 1-bit ADCs for multi-user uplink communications. In: Proc. IEEE Int. Conf. Acoust. Speech Signal Process. ICASSP. pp. 9141–9145. Seoul, South Korea (Apr 2024)

    2024

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.