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REVIEW 3 major objections 6 minor 62 references

Channel-Aware Holographic Decision Fusion

T0 review · 3 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A fusion center built from an M-element reconfigurable holographic surface and one or two receive feeds can match the detection performance of a fully-digital 100-antenna array, at about 6.5 times lower receive-side power.

desk verdict Solid, genuinely new RHS-as-receiver design for decision fusion; the central near-digital performance claim is plausible but conditional on perfect CSI and on simulation evidence without error bars. read the letter →

arxiv 2505.21035 v1 pith:QKN7SP3K submitted 2025-05-27 eess.SP

Domenico Ciuonzo , Alessio Zappone , Marco Di Renzo This is my paper
classification eess.SP
keywords DistributedDetectionDecisionFusionGoal-orientedcommunicationsInternetofThings(IoT)ReconfigurableHolographicSurface(RHS)WirelessSensorNetworksWidely-linearNear-fieldspherical-wavechannel
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

The paper argues that the expensive receive side of a wireless sensor network's fusion center can be replaced by a reconfigurable holographic surface placed in the near field of one or two feeds, with the surface's phase shifts optimized jointly with a fusion statistic that is linear in the received signal and its conjugate. For a fixed surface it derives the optimal fusion statistic, and because the exact log-likelihood ratio costs $2^K$ terms, it proposes two tractable joint designs: one based on the full second-order statistics of the received vector and one based on an 'ideal sensors' assumption that needs no sensor-quality information. In simulation with ten sensors, the holographic design with 64–144 surface elements and a single feed reaches a detection probability within roughly 2–4% of a fully-digital 100-antenna fusion center at false-alarm rate 0.01, while reducing receive-side power by about 6.5 times. If correct, this means the detection benefits of massive MIMO decision fusion could be obtained with one or two RF chains instead of hundreds.

What carries the argument

The load-bearing object is the reconfigurable holographic surface treated as an analog pre-processor. Its phase shifts form the diagonal matrix $\boldsymbol{\Theta}=\mathrm{diag}(e^{j\varphi_1},\ldots,e^{j\varphi_M})$, and the received signal at the $N$ feeds is $\mathbf{y}=(\mathbf{G}\boldsymbol{\Theta}\mathbf{H})\mathbf{D}_\alpha\mathbf{x}+\mathbf{w}$, where $\mathbf{H}$ is the far-field sensor-to-surface channel, $\mathbf{G}$ follows the deterministic near-field spherical-wave model of Eq. (5), $\mathbf{D}_\alpha$ collects sensor transmit amplitudes, and $\mathbf{x}$ holds the BPSK local decisions. The optimization targets the deflection of the fusion statistic, which for the widely-linear rule becomes a generalized Rayleigh quotient in the phase vector $\boldsymbol{\theta}$: $g(\boldsymbol{\theta})=\boldsymbol{\theta}^\dagger\boldsymbol{\Xi}(\mathbf{a})\boldsymbol{\theta}/\boldsymbol{\theta}^\dagger\boldsymbol{\Psi}(\mathbf{a})\boldsymbol{\theta}$. The argument runs on two closed-form blocks: for fixed phases, the optimal $\mathbf{a}$ is the whitened matched filter of Eq. (31); for fixed $\mathbf{a}$, majorization-minimization gives an update where the new phases are simply the phases of a matrix-vector product (Eqs. (45)–(46)). The near-field model is what converts many surface elements into many equivalent channel degrees of freedom visible to a very small number of feeds.

What would settle it

Run the paper's simulation scenario with channel estimation errors added to $\mathbf{H}$ and $\mathbf{G}$, or with the RHS phase shifts quantized to fewer than three bits and mutual coupling included; if the detection probability at $P_{F0}=0.01$ drops by more than the single-digit gap the paper reports, the central claim would be contradicted. A more direct test is a tabletop prototype with about 100 surface elements, one feed, and ten transmitters, measuring the ROC against the predicted curve.

Watch

Extended reading notes

Core claim

The central claim is that a holographic decision-fusion architecture—an RHS with $M$ reconfigurable elements whose phase shifts are optimized together with a widely-linear fusion statistic $\Lambda_{\mathrm{wl}}=\mathbf{a}^\dagger\mathbf{y}$—can match the detection performance of a fully-digital fusion center with $N_{\mathrm{dig}}=100$ antennas and RF chains. The paper establishes this by maximizing the deflection of the fusion statistic under two knowledge models: FuC, which uses the full second-order characterization of the received vector (including its pseudocovariance), and IS, which assumes perfect sensor decisions and needs only the channel matrices. The unit-modulus phase constraint makes the joint design non-convex; the paper solves it by alternating optimization with closed-form majorization-minimization updates, so each step is a phase-of-vector update. With $K=10$ sensors and $M=144$ surface elements, the simulated detection probability at $P_{F0}=0.01$ is about 2–4% below the fully-digital benchmark, and the ratio of receive-side power consumption is $\epsilon_{\mathrm{rx},\mathrm{dig}}/\epsilon_{\mathrm{rx},\mathrm{holo}}\approx 6.5$. The claim is that the near-field degrees of freedom of the surface give one or two RF feeds access to spatial diversity that would otherwise require a hundred antenna chains.

Load-bearing premise

The design stands on the assumption that the fusion center has perfect knowledge of both the sensor-to-surface and surface-to-feed channels, and that the surface elements deliver lossless, continuous phase shifts.

Editorial extensions

If this is right

  • If the central claim holds, a WSN fusion center designed this way can approach the detection performance of a 100-antenna fully-digital array while needing only one or two RF chains, making the receive hardware roughly 6.5 times cheaper in power.
  • The sensor-agnostic IS design needs only the channels $\mathbf{G}$ and $\mathbf{H}$ and the sensor transmit powers, yet with two or three bits of phase quantization it stays within a small loss of full precision, so practical coarse-resolution RHS hardware is usable.
  • Detection probability rises with both the number of surface elements $M$ and the number of sensors $K$, so scaling the architecture means adding cheap passive elements or sensors rather than RF chains.
  • Because the optimized RHS also improves the log-likelihood-ratio fusion statistic, the joint phase design transfers beyond the linear statistic it was derived for.
  • The FuC designs outperform the IS design in detection probability but need sensor-level detection and false-alarm probabilities; the IS design is more robust to phase quantization.

Reading between the lines

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

  • The strongest open question this raises is robustness: since the design assumes exact knowledge of both $\mathbf{H}$ and $\mathbf{G}$, a natural next test is to recompute the same ROC under channel estimation error and phase impairments; the performance gap to the fully-digital array could widen appreciably.
  • The same architecture could be pointed at other goal-oriented inference tasks, such as parameter estimation or spectrum sensing, replacing the deflection objective with estimation MSE or detection probability; the near-field degrees of freedom are not specific to binary hypothesis testing.
  • The power comparison counts receive-side hardware only; an end-to-end energy accounting that includes channel-estimation overhead and surface control might change the reported 6.5x factor, though not necessarily in either direction.
  • A practical implementation would likely need mutual-coupling calibration and non-ideal element patterns; if those effects are small, the 3-bit phase quantization result suggests a low-cost prototype could come close to the simulated performance.
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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 / 6 minor

Summary. This paper considers distributed detection in a wireless sensor network where K sensors send BPSK decisions over a flat-fading multiple-access channel to a fusion center equipped with a reconfigurable holographic surface (RHS) with M phase-controllable elements and N receive feeds. The authors derive the optimal LLR fusion rule for a fixed RHS configuration, note its exponential complexity, and then propose two joint designs of a widely-linear fusion vector and the RHS phase shifts: a full-characterization (FuC) design that maximizes deflection under exact conditional second-order statistics, and an ideal-sensor (IS) design that is agnostic to sensor-level performance. Both designs are solved by alternating optimization with majorization-minimization phase updates, yielding closed-form iterations. Simulations provide ROC curves, detection probability versus M and versus K, and a phase-quantization study, all compared against a fully-digital 100-antenna fusion center. The central claim is that the holographic design with N=1 or 2 feeds attains detection performance within 2–4% of the fully-digital baseline at M=144 while reducing receive-side power by roughly 6.5x (Eqs. (49)-(50), Figs. 3–4).

Significance. If the claims hold, the paper makes a credible contribution: it proposes a new RHS-based architecture for channel-aware decision fusion, provides closed-form AO/MM updates with complexity and knowledge-requirement tables, and gives an independent ROC-based check of the deflection-based designs. The comparison with a fully-digital massive MIMO baseline is an appropriate benchmark, and the 3-bit quantization study is a useful practical robustness result. However, the central quantitative claims are supported only by simulation without error bars, and they rest on perfect knowledge of both the sensor-to-RHS and RHS-to-feed channels, so the significance is conditional on additional robustness evidence.

major comments (3)
  1. [Sec. II-B, Eqs. (36), (45)-(46), and Sec. V-D] The system model assumes perfect knowledge of both G and H (Sec. II-B, 'Remarks on channel knowledge'), and the RHS phase design in Eqs. (45)-(46) coherently aligns M elements using N_r = G diag(H D_alpha rho_10) (Eq. (36)). Under channel estimation errors, the optimized phases maximize the estimated deflection rather than the true one, and the true coherent combining gain is reduced. Because the reported advantage of the holographic system over the N_dig=100 baseline is only about 2–4 percentage points in P_D0 at M=144 (Fig. 3), even moderate CSI errors could plausibly erase the claimed 'comparable detection performance.' The paper explicitly defers imperfect CSI to future work in Sec. VI, so this is an acknowledged gap rather than an internal contradiction; nevertheless, the central claim is not robust to the paper's own stated limitation. Please add a sensitivity analysis, for example by modeling H_est = H + E with normalized MSE in the range 0.01–0.1 and recomputing the key comparisons in Figs. 3–4.
  2. [Sec. V, Figs. 2–5] The simulation section reports ROC curves and scalar P_D0 values without stating the number of Monte Carlo trials or providing confidence intervals. Since the decisive comparison in Fig. 3 at M=144 is a 2–4% P_D0 difference and some ROC branches in Fig. 2 are close to each other, the absence of error bars makes it impossible to judge whether the claimed parity with the fully-digital baseline is statistically distinguishable from Monte Carlo noise. Please report the number of independent realizations and add error bars or confidence bands, at least for Figs. 3–4.
  3. [Sec. IV, Eqs. (28) and (33)] Eq. (24) defines D_FuC,i with Cov(y|H_i) in the denominator, and Step (A) in Eq. (30) correctly uses H_i, but Eq. (28) writes Cov(y|H_1) and Eq. (33) also writes Cov(y|H_1) for the general problem P_FuC,i. Since the paper later reports separate FuC-1 and FuC-0 results (Sec. V), the general formulation should use H_i. Please correct the equations and clarify the exact objective used for FuC-0.
minor comments (6)
  1. [Eq. (16)] The denominator of Eq. (16) repeats ||p_bar_rhs - p_fc_n|| twice; the second factor should presumably be ||p_rhs_m - p_fc_n||.
  2. [Algorithms 1 and 2] The update 'Set ℓ ← −ℓ + 1' in Step 5 of both algorithms is a typo; as written it alternates ℓ between 0 and 1 and could prevent correct termination. It should read ℓ ← ℓ + 1.
  3. [Eq. (45)] Equation (45) is typeset with misplaced or missing parentheses and awkward line breaks, so the closed-form phase update is difficult to verify as printed.
  4. [Fig. 5 caption] The caption contains the typo 'Fuc-1' instead of 'FuC-1'.
  5. [Sec. V-C] The sentence reporting 'detection rate improvements ... of approximately 30%, and 55%' should state whether these are relative or absolute improvements and should define the reference point explicitly.
  6. [Sec. IV-C] The random initialization of the AO procedure is mentioned only briefly; a short discussion of multiple restarts or initialization sensitivity would strengthen the practical recommendations, since the algorithm only guarantees convergence to a local optimum.

Circularity Check

0 steps flagged · score 1.0 of 10

No load-bearing circularity: the RHS/fusion design is optimized for deflection, and the claimed detection performance is verified by independent ROC simulation; only the illustrative 6.5x energy-efficiency factor borrows a component-power ratio from the authors' prior [63].

full rationale

The paper's derivation chain is self-contained against external benchmarks. The joint design maximizes deflection metrics (Eqs. (24) and (27)) for the WL statistic in Eq. (22); the reported figures of merit are global false-alarm and detection probabilities P_F0 and P_D0 computed by thresholding the same statistic and simulating the conditional received vectors (Sec. V-B, Figs. 2-5). Thus the evaluation metric is not identical to the design objective, and no parameter is fitted to force the ROC outcome. The fully-digital baseline (N_dig=100) uses the same FuC/IS fusion-rule principles from prior work [6], [9], but those rules are re-derived in Eqs. (30)-(32) and the comparison is a direct simulation, not an internal consistency check. The only self-citation of note is [63] (Zappone et al.) for the component-power assumption eps_rf ~ 10 eps_rhs used in the 6.5x receive-side energy-efficiency arithmetic of Eqs. (49)-(50); this is an illustrative hardware parameter, not a fitted output, and the central detection-performance claim does not depend on it. The acknowledged perfect-CSI assumption (Sec. II-B, Sec. VI) is a robustness limitation, not a circular step.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

No new physical entities are introduced; the RHS, feeds, and channels are all taken from prior literature. The central claim rests on idealized channel knowledge, a deflection-based design objective, and a specific set of simulation parameters.

free parameters (6)
  • Receive power ratio epsilon_rf / epsilon_rhs = 10 (from ref. [63])
    Used in Eqs. (49)-(50) to compute the claimed 6.5x receive-side energy saving; the energy gain scales directly with this ratio.
  • Sensor operating point (P_D,k, P_F,k) = (0.5, 0.05)
    Chosen for all sensors in simulation (Sec. V-A); these values set the observation bound and the comparison baselines.
  • Rician factor kappa_k = random in (3,5) dB
    Drawn per sensor in the Rician channel model; it affects the channel H and therefore the optimized RHS phases and simulated ROCs.
  • Noise power sigma_w^2 = -50 dBm
    Sets the operating SNR in Eq. (17); absolute detection probabilities in Figs. 2-5 depend on it.
  • RHS reflection efficiency eta = 1
    Assumes a lossless surface; a realistic eta less than 1 would reduce the effective signal and change the performance gap.
  • Energy-comparison sizes (M, N, N_dig) = (144, 1, 100)
    Used in Eqs. (49)-(50) to produce the 6.5x ratio; different sizes change the factor.
assumptions (5)
  • domain assumption RHS-to-feed channel G obeys the deterministic near-field spherical-wave model in Eq. (5) with no mutual coupling, no amplitude errors, and known geometry.
    This model is used to build H_e(Theta) and to compute N_r and the deflection; it is not validated by measurements in this paper.
  • domain assumption Both channel matrices G and H are perfectly known to the fusion center.
    Stated in the remarks of Sec. II-B; the AO/MM designs in Eqs. (31), (32), (45), (46) all require exact H and G.
  • domain assumption Maximizing deflection D_i is a valid surrogate for improving detection ROC.
    The optimization problems P_FuC,i and P_IS maximize deflection, while the claims are about detection probability; the link is shown only by simulation.
  • domain assumption For the IS design, the sensors are ideal at the design stage: Pr(x=1_K|H1)=Pr(x=-1_K|H0)=1.
    Used to obtain Eq. (26) and the simplified metric D_IS; simulation then evaluates with non-ideal sensors (P_D=0.5, P_F=0.05).
  • domain assumption In simulations, sensor decisions are conditionally independent and identically distributed.
    Used in Sec. V-A for the covariance simplification; the theory in Sec. II allows arbitrary dependency but the validation is i.i.d. only.

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Pith. "Pith review of Channel-Aware Holographic Decision Fusion." pith.science (2026). https://pith.science/paper/QKN7SP3K

@misc{pith2026250521035,
  author       = {Pith},
  title        = {Pith review of: Channel-Aware Holographic Decision Fusion},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QKN7SP3K}},
  note         = {Machine review of arXiv:2505.21035}
}
read the original abstract

This work investigates Distributed Detection (DD) in Wireless Sensor Networks (WSNs) utilizing channel-aware binary-decision fusion over a shared flat-fading channel. A reconfigurable metasurface, positioned in the near-field of a limited number of receive antennas, is integrated to enable a holographic Decision Fusion (DF) system. This approach minimizes the need for multiple RF chains while leveraging the benefits of a large array. The optimal fusion rule for a fixed metasurface configuration is derived, alongside two suboptimal joint fusion rule and metasurface design strategies. These suboptimal approaches strike a balance between reduced complexity and lower system knowledge requirements, making them practical alternatives. The design objective focuses on effectively conveying the information regarding the phenomenon of interest to the FC while promoting energy-efficient data analytics aligned with the Internet of Things (IoT) paradigm. Simulation results underscore the viability of holographic DF, demonstrating its advantages even with suboptimal designs and highlighting the significant energy-efficiency gains achieved by the proposed system.

Figures

Figures reproduced from arXiv: 2505.21035 by the authors.

Figure 1
Figure 1. The (channel-aware) holographic DF system model [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Assessing the benefits of joint design in holographic DF via ROCs ( [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. PD0 vs number of RHS elements M (with PF0 = 0.01) for two different feed scenarios (N ∈ 1, 2). WSN with K = 10 sensors, with sensing performance (PD,k, PF,k) = (0.5, 0.05), k ∈ K. noise variance is set to σ 2 w = −50 dBm. ≈ 4% when using FuC-1 strategy. No appreciable performance difference is observed for a large RHS when increasing the number of receive feeds considered from N = 1 to N = 2. Conversely, for a small… view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: PD0 vs number of sensors K (with PF0 = 0.01) of the considered rule/RHS configurations. Holographic DF is implemented with an RHS made of M = 100 elements and N = 1 receive feed; Fully-digital MIMO architecture is simulated with Ndig = 100 antennas. Noise variance is s…
Figure 5
Figure 5. Figure 5: PD0 vs PF0 of the proposed joint design strategies (Fuc-1, FuC-0 and IS) with different resolution of quantized RHS shifts. WSN with K = 10 sensors, (PD,k, PF,k) = (0.5, 0.05), k ∈ K.Holographic DF is implemented with an RHS made of M = 100 elements and N = 1 receive f…

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Works this paper leans on

62 extracted references · 58 canonical work pages

  1. [47]

    Wireless inference gets smarter: RIS-assisted channel-aware MIMO decision fusion,

    N. Mudkey, D. Ciuonzo, A. Zappone, and P. Salvo Rossi, “Wireless inference gets smarter: RIS-assisted channel-aware MIMO decision fusion,” inIEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM), 2022, pp. 26–30

    work page 2022

  2. [48]

    Massive MIMO channel-aware decision fusion aided by reconfigurable intelligent sur- faces,

    D. Ciuonzo, A. Zappone, M. Di Renzo, and L. Wu, “Massive MIMO channel-aware decision fusion aided by reconfigurable intelligent sur- faces,” inIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2025

    work page 2025

  3. [1]

    Ciuonzo and P

    D. Ciuonzo and P. Salvo Rossi, Eds.,Data Fusion in Wireless Sensor Networks: a Statistical Signal Processing Perspective. Institution of Engineering and Technology (IET), series on Control, Robotics & Sensors, 2019

    work page 2019

  4. [2]

    Providing spectrum information service using TV white space via distributed detection system,

    Z. Chen and Y . Zhang, “Providing spectrum information service using TV white space via distributed detection system,”IEEE Trans. Veh. Technol., vol. 68, no. 8, pp. 7655–7667, 2019

    work page 2019

  5. [3]

    Bayesian fault detection and localization through wireless sensor networks in industrial plants,

    G. Tabella, D. Ciuonzo, N. Paltrinieri, and P. Salvo Rossi, “Bayesian fault detection and localization through wireless sensor networks in industrial plants,”IEEE Internet Things J., 2024

    work page 2024

  6. [4]

    Distributed detection in wireless sensor networks using a multiple access channel,

    W. Li and H. Dai, “Distributed detection in wireless sensor networks using a multiple access channel,”IEEE Trans. Signal Process., vol. 55, no. 3, pp. 822–833, 2007

    work page 2007

  7. [6]

    Channel-aware decision fusion in distributed MIMO wireless sensor networks: Decode-and-fuse vs. decode-then-fuse,

    D. Ciuonzo, G. Romano, and P. Salvo Rossi, “Channel-aware decision fusion in distributed MIMO wireless sensor networks: Decode-and-fuse vs. decode-then-fuse,”IEEE Trans. Wireless Commun., vol. 11, no. 8, pp. 2976–2985, Aug. 2012

    work page 2012

  8. [7]

    Multiuser MIMO-OFDM for next-generation wireless systems,

    M. Jiang and L. Hanzo, “Multiuser MIMO-OFDM for next-generation wireless systems,”Proc. IEEE, vol. 95, no. 7, pp. 1430–1469, 2007

    work page 2007

Show all 62 references
  1. [8]

    An overview of massive MIMO: Benefits and challenges,

    L. Lu, G. Y . Li, A. L. Swindlehurst, A. Ashikhmin, and R. Zhang, “An overview of massive MIMO: Benefits and challenges,”IEEE J. Sel. Topics Signal Process., vol. 8, no. 5, pp. 742–758, 2014

    2014

  2. [9]

    Massive MIMO channel-aware decision fusion,

    D. Ciuonzo, P. Salvo Rossi, and S. Dey, “Massive MIMO channel-aware decision fusion,”IEEE Trans. Signal Process., vol. 63, no. 3, pp. 604– 619, 2015

    2015

  3. [10]

    Massive MIMO for wireless sensing with a coherent multiple access channel,

    F. Jiang, J. Chen, A. L. Swindlehurst, and J. A. L ´opez-Salcedo, “Massive MIMO for wireless sensing with a coherent multiple access channel,” IEEE Trans. Signal Process., vol. 63, no. 12, pp. 3005–3017, 2015

    2015

  4. [11]

    Distributed detection for centralized and decentralized millimeter wave massive MIMO sensor networks,

    A. Chawla, R. K. Singh, A. Patel, A. K. Jagannatham, and L. Hanzo, “Distributed detection for centralized and decentralized millimeter wave massive MIMO sensor networks,”IEEE Trans. Veh. Technol., vol. 70, no. 8, pp. 7665–7680, 2021

    2021

  5. [12]

    Smart radio environments empowered by reconfigurable intelligent surfaces: How it works, state of research, and the road ahead,

    M. Di Renzo, A. Zappone, M. Debbah, M.-S. Alouini, C. Yuen, J. De Rosny, and S. Tretyakov, “Smart radio environments empowered by reconfigurable intelligent surfaces: How it works, state of research, and the road ahead,”IEEE J. Sel. Areas Commun., vol. 38, no. 11, pp. 2450–2525, 2020

    2020

  6. [13]

    Holographic MIMO surfaces for 6G wireless networks: Opportunities, challenges, and trends,

    C. Huang, S. Hu, G. C. Alexandropoulos, A. Zappone, C. Yuen, R. Zhang, M. Di Renzo, and M. Debbah, “Holographic MIMO surfaces for 6G wireless networks: Opportunities, challenges, and trends,”IEEE Wireless Commun., vol. 27, no. 5, pp. 118–125, 2020

    2020

  7. [14]

    In- telligent surface-aided transmitter architectures for millimeter-wave ultra massive MIMO systems,

    V . Jamali, A. M. Tulino, G. Fischer, R. R. M ¨uller, and R. Schober, “In- telligent surface-aided transmitter architectures for millimeter-wave ultra massive MIMO systems,”IEEE Open Journal of the Communications Society, vol. 2, pp. 144–167, 2020

    2020

  8. [15]

    Approaching massive MIMO performance with reconfigurable intelli- gent surfaces: We do not need many antennas,

    G. Interdonato, F. Di Murro, C. D’Andrea, G. Di Gennaro, and S. Buzzi, “Approaching massive MIMO performance with reconfigurable intelli- gent surfaces: We do not need many antennas,”IEEE Trans. Commun., 2024

    2024

  9. [16]

    Wideband collaborative spectrum sensing using massive MIMO decision fusion,

    I. Dey, D. Ciuonzo, and P. Salvo Rossi, “Wideband collaborative spectrum sensing using massive MIMO decision fusion,”IEEE Trans. Wireless Commun., vol. 19, no. 8, pp. 5246–5260, 2020

    2020

  10. [17]

    Communicating with large intelligent surfaces: Fundamen- tal limits and models,

    D. Dardari, “Communicating with large intelligent surfaces: Fundamen- tal limits and models,”IEEE J. Sel. Areas Commun., vol. 38, no. 11, pp. 2526–2537, 2020

    2020

  11. [18]

    Detection with distributed sensors,

    R. R. Tenney and N. R. Sandell, “Detection with distributed sensors,” IEEE Trans. Aerosp. Electron. Syst., no. 4, pp. 501–510, 1981

    1981

  12. [19]

    Optimal data fusion in multiple sensor detection systems,

    Z. Chair and P. Varshney, “Optimal data fusion in multiple sensor detection systems,”IEEE Trans. Aerosp. Electron. Syst., no. 1, pp. 98– 101, 1986

    1986

  13. [20]

    Optimal detection and performance of distributed sensor systems,

    A. R. Reibman and L. Nolte, “Optimal detection and performance of distributed sensor systems,”IEEE Trans. Aerosp. Electron. Syst., no. 1, pp. 24–30, 1987

    1987

  14. [21]

    Channel aware decision fusion in wireless sensor networks,

    B. Chen, R. Jiang, T. Kasetkasem, and P. K. Varshney, “Channel aware decision fusion in wireless sensor networks,”IEEE Trans. Signal Process., vol. 52, no. 12, pp. 3454–3458, Dec. 2004. IEEE INTERNET OF THINGS JOURNAL, VOL. *, NO. *, MONTH 2024 14

    2004

  15. [22]

    Asymptotic detection performance of type-based multiple access over multiaccess fading channels,

    G. Mergen, V . Naware, and L. Tong, “Asymptotic detection performance of type-based multiple access over multiaccess fading channels,”IEEE Trans. Signal Process., vol. 55, no. 3, pp. 1081–1092, 2007

    2007

  16. [23]

    Nonorthogonal transmission and noncoherent fusion of censored decisions,

    S. Yiu and R. Schober, “Nonorthogonal transmission and noncoherent fusion of censored decisions,”IEEE Trans. Veh. Technol., vol. 58, no. 1, pp. 263–273, 2008

    2008

  17. [24]

    Optimal power allocation for distributed detection over MIMO channels in wireless sensor networks,

    X. Zhang, H. V . Poor, and M. Chiang, “Optimal power allocation for distributed detection over MIMO channels in wireless sensor networks,” IEEE Trans. Signal Process., vol. 56, no. 9, pp. 4124–4140, 2008

    2008

  18. [25]

    Energy-efficient detection in sensor networks,

    S. Appadwedula, V . V . Veeravalli, and D. L. Jones, “Energy-efficient detection in sensor networks,”IEEE J. Sel. Areas Commun., vol. 23, no. 4, pp. 693–702, 2005

    2005

  19. [26]

    Channel aware sensor selection in distributed detection systems,

    H. R. Ahmadi and A. V osoughi, “Channel aware sensor selection in distributed detection systems,” inIEEE 10th Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2009

    2009

  20. [27]

    Decision fusion rules in ambient backscatter wireless sensor networks,

    D. Ciuonzo, G. Gelli, A. Pescap ´e, and F. Verde, “Decision fusion rules in ambient backscatter wireless sensor networks,” inIEEE 30th Annual Int. Symp. on Personal, Indoor and Mobile Radio Communications (PIMRC), 2019

    2019

  21. [28]

    Decentralized hypothesis testing in energy harvesting wireless sensor networks,

    A. Tarighati, J. Gross, and J. Jald ´en, “Decentralized hypothesis testing in energy harvesting wireless sensor networks,”IEEE Trans. Signal Process., vol. 65, no. 18, pp. 4862–4873, 2017

    2017

  22. [29]

    Dis- tributed detection in massive MIMO wireless sensor networks under perfect and imperfect CSI,

    A. Chawla, A. Patel, A. K. Jagannatham, and P. K. Varshney, “Dis- tributed detection in massive MIMO wireless sensor networks under perfect and imperfect CSI,”IEEE Trans. Signal Process., vol. 67, no. 15, pp. 4055–4068, 2019

    2019

  23. [30]

    Surface-based techniques for IoT networks: Opportunities and challenges,

    A. Zappone, M. Di Renzo, and R. K. Fotock, “Surface-based techniques for IoT networks: Opportunities and challenges,”IEEE Internet of Things Magazine, vol. 5, no. 4, pp. 72–77, 2022

    2022

  24. [31]

    A survey on over-the-air computation,

    A. S ¸ahin and R. Yang, “A survey on over-the-air computation,”IEEE Commun. Surveys Tuts, vol. 25, no. 3, pp. 1877–1908, 2023

    1908

  25. [32]

    Over- the-air computation via reconfigurable intelligent surface,

    W. Fang, Y . Jiang, Y . Shi, Y . Zhou, W. Chen, and K. B. Letaief, “Over- the-air computation via reconfigurable intelligent surface,”IEEE Trans. Commun., vol. 69, no. 12, pp. 8612–8626, 2021

    2021

  26. [33]

    Worst-case design for RIS-aided over-the-air computation with imperfect CSI,

    W. Zhang, J. Xu, W. Xu, X. You, and W. Fu, “Worst-case design for RIS-aided over-the-air computation with imperfect CSI,”IEEE Commun. Lett., vol. 26, no. 9, pp. 2136–2140, 2022

    2022

  27. [34]

    Beamforming design based on two-stage stochastic optimization for RIS-assisted over-the-air compu- tation systems,

    X. Zhai, G. Han, Y . Cai, and L. Hanzo, “Beamforming design based on two-stage stochastic optimization for RIS-assisted over-the-air compu- tation systems,”IEEE Internet Things J., vol. 9, no. 7, pp. 5474–5488, 2022

    2022

  28. [35]

    RIS-Assisted CSIT-free data fusion with timing misalignment,

    Y . Zhao, W. Xu, and X. Ye, “RIS-Assisted CSIT-free data fusion with timing misalignment,” in9th IEEE International Conference on Computer and Communication Engineering (ICCCE), 2023, pp. 12–17

    2023

  29. [36]

    Beamforming design for active RIS-Aided over-the-air computation,

    D. Zhang, M. Xiao, M. Skoglund, and H. V . Poor, “Beamforming design for active RIS-Aided over-the-air computation,”arXiv preprint arXiv:2311.18418, 2023

    2023 arXiv

  30. [37]

    Double-RIS assisted over-the-air computation,

    J. Li, M. Fu, Y . Zhou, and Y . Shi, “Double-RIS assisted over-the-air computation,” inIEEE Globecom Workshops (GC Wkshps), 2021, pp. 1–6

    2021

  31. [38]

    Joint beamforming aided over-the-air computation systems relying on both BS-side and user- side reconfigurable intelligent surfaces,

    X. Zhai, G. Han, Y . Cai, and L. Hanzo, “Joint beamforming aided over-the-air computation systems relying on both BS-side and user- side reconfigurable intelligent surfaces,”IEEE Trans. Wireless Commun., vol. 21, no. 12, pp. 10 766–10 779, 2022

    2022

  32. [39]

    Simultaneously transmitting and reflecting (STAR) RIS assisted over-the-air computation systems,

    X. Zhai, G. Han, Y . Cai, Y . Liu, and L. Hanzo, “Simultaneously transmitting and reflecting (STAR) RIS assisted over-the-air computation systems,”IEEE Trans. Commun., vol. 71, no. 3, pp. 1309–1322, 2023

    2023

  33. [40]

    RIS-assisted over-the-air computation in millimeter wave communication networks,

    L. Hu, Z. Wang, H. Zhu, Y . Shi, and Y . Zhou, “RIS-assisted over-the-air computation in millimeter wave communication networks,” inIEEE 95th Vehicular Technology Conference (VTC2022-Spring), 2022, pp. 1–5

    2022

  34. [41]

    Intelligent reflecting surface- assisted over-the-air computation for backscatter sensor networks,

    S. Mao, N. Zhang, J. Hu, and K. Yang, “Intelligent reflecting surface- assisted over-the-air computation for backscatter sensor networks,”IEEE Trans. Veh. Technol., vol. 72, no. 5, pp. 6839–6843, 2023

    2023

  35. [42]

    Wireless-powered over-the-air computation in intelligent reflecting surface-aided IoT net- works,

    Z. Wang, Y . Shi, Y . Zhou, H. Zhou, and N. Zhang, “Wireless-powered over-the-air computation in intelligent reflecting surface-aided IoT net- works,”IEEE Internet Things J., vol. 8, no. 3, pp. 1585–1598, 2021

    2021

  36. [43]

    Joint beamforming and reflecting design for IRS-Aided wireless powered over-the-air computation and communication networks,

    S. Mao, N. Zhang, L. Liu, T. Liu, J. Hu, K. Yang, and D. Niyato, “Joint beamforming and reflecting design for IRS-Aided wireless powered over-the-air computation and communication networks,”IEEE Trans. Commun., vol. 72, no. 4, pp. 2216–2231, 2024

    2024

  37. [44]

    Joint transmit and reflective beamformer design for secure estimation in IRS-aided WSNs,

    M. F. Ahmed, K. P. Rajput, N. K. Venkategowda, K. V . Mishra, and A. K. Jagannatham, “Joint transmit and reflective beamformer design for secure estimation in IRS-aided WSNs,”IEEE Signal Process. Lett., vol. 29, pp. 692–696, 2022

    2022

  38. [45]

    RIS-Assisted cooperative spectrum sensing for cognitive radio networks,

    J. Ge, Y .-C. Liang, S. Wang, and C. Sun, “RIS-Assisted cooperative spectrum sensing for cognitive radio networks,”IEEE Trans. Wireless Commun., vol. 23, no. 9, pp. 12 547–12 562, 2024

    2024

  39. [46]

    Joint transmit precoders and passive reflection beamformer design in IRS-Aided IoT networks,

    K. P. Rajput, L. Wu, M. B. Shankar, and P. K. Varshney, “Joint transmit precoders and passive reflection beamformer design in IRS-Aided IoT networks,” inIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024, pp. 156–160

    2024

  40. [49]

    Majorization-minimization algo- rithms in signal processing, communications, and machine learning,

    Y . Sun, P. Babu, and D. P. Palomar, “Majorization-minimization algo- rithms in signal processing, communications, and machine learning,” IEEE Trans. Signal Process., vol. 65, no. 3, pp. 794–816, 2016

    2016

  41. [50]

    Orthogonality and cooperation in collaborative spectrum sensing through MIMO decision fusion,

    P. Salvo Rossi, D. Ciuonzo, and G. Romano, “Orthogonality and cooperation in collaborative spectrum sensing through MIMO decision fusion,”IEEE Trans. Wireless Commun., vol. 12, no. 11, pp. 5826–5836, Nov. 2013

    2013

  42. [51]

    Green Internet of Things (IoT): An overview,

    M. A. Albreem, A. A. El-Saleh, M. Isa, W. Salah, M. Jusoh, M. Azizan, and A. Ali, “Green Internet of Things (IoT): An overview,” inIEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA), 2017, pp. 1–6

    2017

  43. [52]

    Distributed detection fusion in clustered sensor networks over multiple access fading channels,

    S. A. Aldalahmeh and D. Ciuonzo, “Distributed detection fusion in clustered sensor networks over multiple access fading channels,”IEEE Transactions on Signal and Information Processing over Networks, vol. 8, pp. 317–329, 2022

    2022

  44. [53]

    Path loss in reconfigurable intelligent surface-enabled channels,

    S. W. Ellingson, “Path loss in reconfigurable intelligent surface-enabled channels,” inIEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2021, pp. 829– 835

    2021

  45. [54]

    Wireless communications with reconfigurable intelligent surface: Path loss modeling and experimental measurement,

    W. Tang, M. Z. Chen, X. Chen, J. Y . Dai, Y . Han, M. Di Renzo, Y . Zeng, S. Jin, Q. Cheng, and T. J. Cui, “Wireless communications with reconfigurable intelligent surface: Path loss modeling and experimental measurement,”IEEE Trans. Wireless Commun., vol. 20, no. 1, pp. 421– ...

    2020

  46. [55]

    Path loss modeling and measurements for reconfigurable intelligent surfaces in the millimeter-wave frequency band,

    W. Tang, X. Chen, M. Z. Chen, J. Y . Dai, Y . Han, M. Di Renzo, S. Jin, Q. Cheng, and T. J. Cui, “Path loss modeling and measurements for reconfigurable intelligent surfaces in the millimeter-wave frequency band,”IEEE Trans. Commun., vol. 70, no. 9, pp. 6259–6276, 2022

    2022

  47. [56]

    Intelligent reflecting sur- faces: Sum-rate optimization based on statistical position information,

    A. Abrardo, D. Dardari, and M. Di Renzo, “Intelligent reflecting sur- faces: Sum-rate optimization based on statistical position information,” IEEE Trans. Commun., vol. 69, no. 10, pp. 7121–7136, 2021

    2021

  48. [57]

    Reradiation and scattering from a reconfigurable intelligent surface: A general macroscopic model,

    V . Degli-Esposti, E. M. Vitucci, M. D. Renzo, and S. A. Tretyakov, “Reradiation and scattering from a reconfigurable intelligent surface: A general macroscopic model,”IEEE Trans. Antennas Propag., vol. 70, no. 10, pp. 8691–8706, 2022

    2022

  49. [58]

    Near-field modelling and performance analysis for extremely large-scale IRS communications,

    C. Feng, H. Lu, Y . Zeng, T. Li, S. Jin, and R. Zhang, “Near-field modelling and performance analysis for extremely large-scale IRS communications,”IEEE Trans. Wireless Commun., 2023

    2023

  50. [59]

    Fundamentals of statistical signal processing, vol. ii: Detection theory,

    S. M. Kay, “Fundamentals of statistical signal processing, vol. ii: Detection theory,”Signal Processing. Upper Saddle River, NJ: Prentice Hall, 1998

    1998

  51. [60]

    On deflection as a performance criterion in detection,

    B. Picinbono, “On deflection as a performance criterion in detection,” IEEE Trans. Aerosp. Electron. Syst., vol. 31, no. 3, pp. 1072–1081, 1995

    1995

  52. [61]

    Optimal linear cooperation for spectrum sensing in cognitive radio networks,

    Z. Quan, S. Cui, and A. H. Sayed, “Optimal linear cooperation for spectrum sensing in cognitive radio networks,”IEEE J. Sel. Topics Signal Process., vol. 2, no. 1, pp. 28–40, Feb. 2008

    2008

  53. [62]

    Coherent max-log decision fusion in wireless sensor networks,

    A. Lei and R. Schober, “Coherent max-log decision fusion in wireless sensor networks,”IEEE Trans. Commun., vol. 58, no. 5, pp. 1327–1332, 2010

    2010

  54. [63]

    Energy efficiency of holo- graphic transceivers based on RIS,

    A. Zappone, B. Matthiesen, and A. Dekorsy, “Energy efficiency of holo- graphic transceivers based on RIS,” inIEEE Global Communications Conference (GLOBECOM), 2022, pp. 4613–4618. IEEE INTERNET OF THINGS JOURNAL, VOL. *, NO. *, MONTH 2024 15 Domenico Ciuonzo(Senior Member, IEE...

    2022

Pith tools

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