Pith. sign in

REVIEW 4 major objections 5 minor 68 references

A Planning Strategy for Building a Heterogeneous Smart EM Environment

T0 review · 4 major / 5 minor · reviewed 2026-08-04 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A mixed deployment of passive surfaces and active repeaters, chosen by a multi-objective planner, restores urban wireless coverage at far lower cost and energy than raising base-station power.

desk verdict Useful extension of EMS planning to heterogeneous devices with a credible cost/energy trade-off, but the superposition assumption in Eq. (9) needs validation before trusting the coverage numbers. read the letter →

arxiv 2509.08378 v1 pith:VLBGOELD submitted 2025-09-10 eess.SY cs.SY

Arianna Benoni , Marco Salucci , Baozhu Li , Andrea Massa This is my paper · ORCID
classification eess.SYcs.SY
keywords smartelectromagneticenvironmentwirelesscoverageplanningmulti-objectiveoptimizationNSGA-IIskinsrepeatersintegratedaccessandbackhaulurbanpropagation
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 argues that a city's dead zones need not be fixed with more base stations or more transmit power. Instead, a planner can deploy a heterogeneous mix of cheap passive reflectors and modest active repeaters, and a multi-objective search can choose where each device goes. The authors formalize the choice as a planning problem, solve it with an integer genetic algorithm, and test it on two real districts of Trento, Italy, at 3.5 GHz. In the first district, the best-compromise deployment shrank the blind-spot area by 86-89 percent while costing about a third of the full-coverage solution and using a tenth of its energy; matching that coverage by raising base-station power would have required a 525-550 percent increase in transmit power. The takeaway is that the propagation environment itself - walls, facades, and poles - can be treated as a design resource rather than an obstacle.

What carries the argument

The load-bearing mechanism is the Heterogeneous Planning Problem (HPP): an integer chromosome of length N encodes which of four device types (SP-EMS, RP-EMS, SR, IAB) occupies each admissible site, with zero meaning no device. Feasibility is pre-computed geometrically - an ellipse for passive skins traced from the base station and each blind-spot barycenter under a single-reflection path-loss bound, and the intersection of two visibility circles for active devices. Each device's coverage contribution is pre-computed once with a ray-tracing tool and stored, so any deployment's field is evaluated as a superposition of stored maps (Eq. 9). An integer NSGA-II then searches the resulting space un

What would settle it

Install, or full-wave simulate, the best-compromise deployment - 6 SP-EMSs, 1 RP-EMS, and 3 SRs in Trento Nord - with all devices present at once, and measure the received-power CDF inside the original blind-spot region at Pth = -65 dBm. The paper's superposition model predicts 7.8 percent of samples below threshold at t1 and 5.8 percent at t2; if the measured CDF is materially higher, the neglect of inter-device shadowing and re-radiation is the cause.

Watch

Extended reading notes

Core claim

The central claim is that a heterogeneous smart electromagnetic environment - static and reconfigurable electromagnetic skins (passive reflective surfaces), smart repeaters, and integrated access-backhaul nodes placed on building facades and poles - can recover coverage in user-defined regions of interest of a real urban scenario, and that the recovery can be planned as a three-objective optimization. Each candidate installation site is assigned one device type or none; the optimizer minimizes the time-averaged coverage shortfall, the installation cost, and the energy consumption simultaneously, producing a Pareto front of trade-off deployments rather than a single answer. The authors report

Load-bearing premise

The planning tool predicts coverage by adding up each device's contribution as if it acted alone in the scene; if installed devices interact - one skin shadowing another or re-radiating onto it - the real coverage could fall short of the predicted 86-89 percent reductions.

Editorial extensions

If this is right

  • Network operators can restore QoS in identified blind spots without new base stations or fiber backhaul, using the planner's Pareto front to pick a deployment matched to their budget.
  • Passive electromagnetic skins do substantial work on their own: in the San Martino test, skins alone removed up to roughly 75 percent of one region's blind spot, showing that zero-energy devices can carry part of the load.
  • The same planning pipeline carries over to any class of device, since the chromosome encoding and cost terms are defined generically over the device alphabet.
  • The full-coverage benchmark quantifies what complete restoration costs, and every cheaper Pareto point makes explicit the coverage the operator trades away for cost and energy savings.
  • The reported best-compromise solutions use far fewer active devices than the full-coverage solution (e.g., 3 SRs and 0 IABs vs. 2 IABs and 1 SR in Trento Nord), indicating that the planner is favoring a mostly passive infrastructure.

Reading between the lines

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

  • The reported gains rest on a superposition assumption (Eq. 9) that ignores device-to-device interactions; a field trial or full-wave simulation of a dense deployment would show whether real gains close to 86-89 percent survive mutual shadowing and re-radiation.
  • The same formulation could be re-targeted almost unchanged at indoor scenarios or Wi-Fi access points - the paper lists these as future work - since the cost terms and the integer encoding do not depend on the 3.5 GHz outdoor setting.
  • The reconfigurable skins are used here in a single-bit static configuration; using their switching capability across time instants could let one deployment track the moving blind spots the paper models rather than just the union of them.
  • The paper's comparison with a 525-550 percent transmit-power increase suggests an operator-side test: measure the marginal cost of extra BTS power against the amortized cost of a skin-and-repeater deployment, a trade-off the installation-cost and energy terms do not fully capture.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The manuscript proposes a multi-objective planning strategy for deploying heterogeneous smart electromagnetic entities (SEEs) — SP-EMSs, RP-EMSs, SRs, and IABs — to recover and enhance wireless coverage in urban blind-spot regions. The heterogeneous planning problem (HPP) is encoded as an integer chromosome over candidate installation sites, with three objectives: coverage mismatch (Φ_CV), installation cost (Φ_CS), and energy consumption (Φ_EC). A database of per-SEE ray-traced coverage maps is precomputed, and an integer NSGA-II explores the solution space. Two real-world OSM-based scenarios are considered (Trento Nord, N=20; San Martino, N=25), with T=2 BTS configurations each. The paper reports Pareto fronts and four representative trade-off solutions (χ_BC, χ_BCS, χ_CC, χ_CE), claiming that the best-compromise solution reduces blind-spot area by 86.1%/88.9% in Trento Nord and achieves below-threshold probabilities of 4.9–5.3% in San Martino, at substantially lower cost and energy than the full-coverage solution or a 525–550% increase in BTS transmit power.

Significance. If the simulation methodology is accepted, the paper makes a useful contribution: it integrates four SEE classes into a single planning framework, precomputes coverage maps to make the NSGA-II optimization tractable, uses realistic urban geometry from OSM with full-wave/ray-tracing models, and provides concrete cost/energy trade-offs. The comparison against increasing BTS transmit power is instructive and strengthens the practical motivation. However, the quantitative claims are conditional on the isolated-scatterer superposition in Eq. (9), which is not validated against a simultaneous multi-SEE simulation or measurement. Since that assumption is load-bearing for every coverage result, the numerical conclusions are not yet established at the claimed level. The paper also contains several correctable but important inconsistencies in the formulation and in the key numerical tables.

major comments (4)
  1. [Sect. 3.4, Eq. (9)-(10)] Equation (9) forms the total field for a candidate deployment as the sum of fields E^(s,n) computed for each SEE deployed alone, plus the BTS field. This isolated-scatterer superposition is the basis for Φ_CV, for every NSGA-II evaluation, and therefore for all reported coverage gains (ΔΩ_BCS, CDF values). It neglects inter-SEE shadowing, mutual coupling, and interactions among active repeaters/IABs. At 3.5 GHz, the 4.58 m² EMSs are roughly 25λ across, so shadowing is physically plausible. The paper provides no comparison against a simultaneous multi-SEE ray-tracing simulation, a full-wave model, or measurement. Since the Conclusions explicitly defer multi-hop links to future work, the current model assumes independent single-hop contributions. Please justify this assumption quantitatively (e.g., run a joint simulation for χ_BCS and χ_BC and report the change in coverage) or weaken the c
  2. [Table IV, BCS row] There is a numerical inconsistency in a key reported result. The χ_BCS row lists #SEEs=9, but the component counts are 6 SP-EMS + 1 RP-EMS + 3 SRs = 10. Using Table I costs, the installation cost is 6×$500 + 1×$750 + 3×$3000 = $12,750, not the reported $6,750. The energy value ν=62 W is consistent with the listed components. Since the paper's headline comparison of χ_BCS with χ_BC and with increased BTS power relies on this row, the corrected cost and the resulting comparison must be reported.
  3. [Sect. 3.3, Eqs. (6)-(7)] Equation (7) defines ξ_max = Σ_n max_s {δ_{χn s} ξ_s}. Because δ_{χn s}=1 only for the class selected at site n, the maximum over s of the δ-weighted cost equals the selected class cost, making the denominator in Eq. (6) equal to the numerator for any nonempty χ. Hence Φ_CS ≡ 1 for every nonempty solution, which is inconsistent with the nonzero but less-than-one values in Tables IV and VII. The same issue affects ν_max in Eq. (8). This is presumably a normalization typo, but it must be corrected in the formulation; for example, ξ_max should be the maximum possible cost over all classes and sites, independent of χ.
  4. [Sects. 3.1.1-3.1.2] Candidate sites are generated from free-space path-loss ellipses/circles with heuristic exclusion rules. The optimizer is restricted to these N sites, so any location that would be effective under multipath but lies outside the free-space geometry is never considered. No sensitivity analysis is given for the candidate-site selection. Since the planning strategy's final deployments are constrained to this set, the optimality of the reported solutions is contingent on the free-space site model. Please quantify how many candidate sites are removed/retained by the environmental rules, and ideally compare the optimized Pareto front with a candidate set expanded by ray-traced strong-coverage locations.
minor comments (5)
  1. [Sect. 3.5, Step 2] The non-domination condition says there exists β for which Φ_β{χ_i^(p)} ≤ Φ_β{χ_i^(q)}; for strict non-domination this should be a strict inequality (<). As written, identical objective vectors could be classified as non-dominated.
  2. [Table VIII] In the χ_BCS row for w=4, ΔP_avg(t1)=30.4 dB exceeds ΔP_max(t1)=18.1 dB, which is impossible; this appears to be a typo (likely 3.4 dB). Similarly, the χ_CE row for w=1 lists ΔP_avg(t2)=20.5 dB with ΔP_max(t2)=6.2 dB; please check all average/max entries.
  3. [Sect. 4, Table IV] The #SEEs column in Table IV is inconsistent for χ_BCS as noted in the major comments; please also verify the counts for the other rows for completeness (the BC row is consistent: 1 SR + 2 IABs = 3 SEEs).
  4. [Fig. 15 / Sect. 4] The BTS power-increase comparison (PTX=125 W and 130 W) is informative, but please clarify explicitly whether these curves are generated with the same ray-tracing framework and the same BTS model apart from transmit power, so the comparison is apples-to-apples.
  5. [General] The abbreviations PSE and ASE are introduced but the text mostly uses SEE; please keep the terminology consistent throughout.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: coverage predictions are forward evaluations, not fitted to outcomes.

full rationale

I walked the derivation chain: candidate-site geometry (SD block), SEE synthesis from cited prior designs (SEED block), precomputed per-SEE ray-tracing field maps (DC block), superposition in Eq. (9) and power computation in Eq. (10), NSGA-II search (SSE block), and final coverage metrics. No parameter is fitted to the reported ΔΩ or CDF values; those metrics are forward evaluations of the optimized solutions using the same simulation model that generated the per-SEE maps. That is self-consistency, not circularity. Eq. (9) is a modeling approximation (isolated-scatterer superposition neglecting inter-SEE coupling and mutual interactions), but it is an input assumption, not a reduction of the predicted coverage to the optimization target. The self-citations [14], [25], [37] supply component design methods and are not used to justify the planning outcome; they are prior published results, not the load-bearing argument. The only notable defect is Eq. (7): as printed, ξmax is defined with δχns, which would make ΦCS ≡ 1 for any nonempty χ, contradicting Table IV; this appears to be a normalization typo and a correctness issue, but it does not make the coverage claims circular. The absence of external measurement validation is a limitation of empirical support, not circularity.

Assumptions & free parameters 2 free parameters · 6 assumptions · 0 invented entities

No new physical entities are introduced. The free parameters are cost and energy estimates from Table I that shape the trade-off objectives. The main load-bearing assumptions are the field-superposition model, free-space feasibility regions, and reliance on the ray-tracing tool and prior EMS designs.

free parameters (2)
  • Installation cost xi_s for each SEE class (SP-EMS $500, RP-EMS $750, SR $3000, IAB $7500) = SP-EMS: 500 $/unit (over-estimation), RP-EMS: 750 $/unit, SR: 3000 $/unit, IAB: 7500 $/unit
    Table I values are hand-chosen estimates used in the cost objective Phi_CS (Eq. 6). They affect the Pareto front and the selected trade-off solutions, but are not justified by market data.
  • Energy consumption nu_s for each SEE class (SP-EMS 0 W, RP-EMS <2 W, SR ~20 W, IAB <=350 W) = 0 W, <2 W, ~20 W, <=350 W
    Table I values are approximate or upper bounds used in the energy objective Phi_EC (Eq. 8). They are not fitted to experiments but influence the reported optimal deployments.
assumptions (6)
  • domain assumption The total electric field from multiple deployed SEEs is the sum of independently simulated single-SEE fields (Eq. 9).
    Section 3.4, Eq. (9). Assumes no interaction between SEEs (mutual coupling, shadowing, re-scattering). This directly drives all computed coverage maps and the claimed performance.
  • domain assumption Free-space path loss defines the feasible deployment regions (Eqs. 2-4).
    Section 3.1 uses free-space ellipse and circle intersections to select candidate sites. Actual NLOS urban propagation may make some selected sites poor or exclude good ones.
  • domain assumption Altair WinProp ray tracing accurately predicts received power in the simulated urban scenarios.
    Section 4 uses WinProp for all coverage estimates. No validation against field measurements or an independent reference is provided.
  • domain assumption The EMS synthesis procedures from refs. [14] and [25] produce devices with the assumed reflection behavior.
    Section 3.2 relies on designs from the authors' prior work; the precomputed coverage maps inherit the accuracy of those designs.
  • domain assumption The set of N candidate sites is predetermined by the network operator and is sufficiently rich to contain near-optimal solutions.
    The HPP formulation (Sect. 2) and the SD block (Sect. 3.1) assume the operator provides admissible sites. The optimizer cannot recover coverage that would require a site outside this set.
  • domain assumption Only single-hop BTS-to-SEE-to-RoI links are considered; no multi-hop interactions between SEEs are modeled.
    The deployment regions and coverage computations assume one hop through the SEE. Multi-hop is listed as future work in Sect. 5, so cascaded or cooperative SEE effects are not captured.

how reviews work

0 comments
Cite this review

Pith. "Pith review of A Planning Strategy for Building a Heterogeneous Smart EM Environment." pith.science (2026). https://pith.science/paper/VLBGOELD

@misc{pith2026250908378,
  author       = {Pith},
  title        = {Pith review of: A Planning Strategy for Building a Heterogeneous Smart EM Environment},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VLBGOELD}},
  note         = {Machine review of arXiv:2509.08378}
}
read the original abstract

This paper presents a planning strategy for the deployment of smart electromagnetic entities (SEEs) to enhance the wireless coverage and the Quality-of-Service (QoS) in large urban areas. The integration of different technological solutions such as integrated access-and-backhaul nodes (IABs), smart repeaters (SRs), and electromagnetic skins (EMSs) is here addressed to enable an effective and efficient implementation of the concept of Smart Electromagnetic Environment (SEME). By combining the features of such heterogeneous SEEs and optimizing their number, positions, orientations, and configuration, the electromagnetic (EM) coverage in a set of Regions-of-Interest (RoIs) of outdoor scenarios is recovered and/or enhanced subject to installation costs and energy consumption requirements. Numerical validations from real-world scenarios are reported to assess the effectiveness of the proposed planning scheme as well as to show the potentialities of an heterogeneous deployment of SEMEs.

Figures

Figures reproduced from arXiv: 2509.08378 by the authors.

Figure 2
Figure 2. There are passive SEEs (PSEs) that only reflect the impinging EM field (e.g., EMSs), while active SEEs (ASEs) amplify and re-transmit the incoming signal (e.g., IABs and SRs). Each s-th (s = 1, ..., S) type of SEE has specific EM features as well as a generally different installation cost, ξs, and an energy consumption, νs. Moreover, since the deployment of a SEE is not arbitrary and ad-hoc rules for each s-th (s = … view at source ↗
Figure 1
Figure 1. [PITH_FULL_IMAGE:figures/full_fig_p031_1.png] view at source ↗
Figure 8
Figure 8. [PITH_FULL_IMAGE:figures/full_fig_p031_8.png] view at source ↗
Figures from the paper (5 more)
Figure 13
Figure 13. Figure 13 [PITH_FULL_IMAGE:figures/full_fig_p032_13.png]
Figure 14
Figure 14. Figure 14 [PITH_FULL_IMAGE:figures/full_fig_p032_14.png]
Figure 15
Figure 15. Figure 15 [PITH_FULL_IMAGE:figures/full_fig_p032_15.png]
Figure 20
Figure 20. Figure 20 [PITH_FULL_IMAGE:figures/full_fig_p033_20.png]
Figure 21
Figure 21. Figure 21 [PITH_FULL_IMAGE:figures/full_fig_p033_21.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

68 extracted references · 67 canonical work pages

  1. [1]

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

    H. Tataria, M. Shafi, A. F. Molisch, M. Dohler, H. Sjoland, and F. Tufvesson, “6G wireless systems: vision, requirements, challenges, insights, and opportunities,” Proc. IEEE, vol. 109, no. 7, pp. 1166-1199, Jul. 2021

    work page 2021

  2. [2]

    The roa d towards 6G: a comprehen- sive survey,

    W. Jiang, B. Han, M. A. Habibi, and H. D. Schotten, "The roa d towards 6G: a comprehen- sive survey," IEEE Open J. Commun. Soc. , vol. 2, pp. 334-366, 2021

    work page 2021

  3. [3]

    GCWCN: 6G-based global coverage wireless communication network architecture,

    C. Wang, P . Zhang, N. Kumar, L. Liu, and T. Y ang, "GCWCN: 6G-based global coverage wireless communication network architecture," IEEE Network, vol. 37, no. 3, pp. 218-223, May 2023

    work page 2023

  4. [4]

    5 G network planning under service and EMF constraints: Formulation and solutions,

    L. Chiaraviglio, C. Di Paolo, and N. Blefari-Melazzi, “5 G network planning under service and EMF constraints: Formulation and solutions,” IEEE Trans. Mobile Comput. , vol. 21, no. 9, pp. 3053-3070, Sep. 2022

    work page 2022

  5. [5]

    Quality-of- experience evaluation for digital twins in 6G network envir onments,

    Z. Zhang, Y . Zhou, L. Teng, W. Sun, C. Li, X. Ming, Z.-P . Zhang, and G. Zhai, "Quality-of- experience evaluation for digital twins in 6G network envir onments," IEEE Trans. Broad- cast., 2024 (DOI: 10.1109/TBC.2023.3345656)

    work page arXiv 2024

  6. [6]

    From digital twin to metaverse: the role of 6G ultrareliabl e and low-latency communi- cations with multi-tier computing,

    T. Q. Duong, D. V an Huynh, S. R. Khosravirad, V . Sharma, O. A. Dobre, and H. Shin, “From digital twin to metaverse: the role of 6G ultrareliabl e and low-latency communi- cations with multi-tier computing,” IEEE Wireless Commun., vol. 30, no. 3, pp. 140-146, Jun. 2023

    work page 2023

  7. [7]

    Digital twin-aided orchestration of mobile ed ge computing with grant-free ac- cess,

    N. A. Mitsiou, V . K. Papanikolaou, P . D. Diamantoulakis, T. Q. Duong, and G. K. Kara- giannidis, “Digital twin-aided orchestration of mobile ed ge computing with grant-free ac- cess,” IEEE Open J. Commun. Soc. , vol. 4, pp. 841-853, 2023

    work page 2023

  8. [8]

    Design of energy- and cost-efficient massive MIMO arrays,

    A. Puglielli et al. , "Design of energy- and cost-efficient massive MIMO arrays, " IEEE Proc., vol. 104, no. 3, pp. 586-606, Mar. 2016. 22

    work page 2016

Show all 68 references
  1. [9]

    Designing smart electromagnetic environment s for next-generation wireless communications,

    A. Massa, A. Benoni, P . Da Ru, S. K. Goudos, B. Li, G. Oliver i, A. Polo, P . Rocca, and M. Salucci, “Designing smart electromagnetic environment s for next-generation wireless communications,” Telecom, vol. 2, no. 2, pp. 213-221, May 2021

    2021

  2. [10]

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

    M. Di Renzo, A. Zappone, M. Debbah, M.-S. Alouini, C. Y ue n, J. de Rosny, and S, Tretyakov, "Smart radio environments empowered by reconfig urable intelligent surfaces: How it works, state of research, and the road ahead," IEEE J. Sel. Areas Commun. , vol. 38, no. 11, pp. 2450...

    2020

  3. [11]

    Towards a heterogeneous smart electromagnetic envir onment for millimeter-wave communications: an industrial viewpoint,

    R. Flamini, D. De Donno, J. Gambini, F. Giuppi, C. Mazzuc co, A. Milani, and L. Restegh- ini, “Towards a heterogeneous smart electromagnetic envir onment for millimeter-wave communications: an industrial viewpoint,” IEEE Trans. Antennas Propag. , vol. 70, no. 10, pp. 8898-8910...

    2022

  4. [12]

    Guest Editorial Sma rt Electromagnetic Environ- ment,

    F. Y ang, D. Erricolo, and A. Massa, “Guest Editorial Sma rt Electromagnetic Environ- ment,” IEEE Trans. Antennas Propag., vol. 70, no. 10, pp. 8687-8690, Oct. 2022

    2022

  5. [13]

    A path to smart rad io environments: an industrial viewpoint on reconfigurable intelligent surfac es,

    R. Liu, Q. Wu, M. Di Renzo, and Y . Y uan, "A path to smart rad io environments: an industrial viewpoint on reconfigurable intelligent surfac es," IEEE Wireless Commun., vol. 29, no. 1, pp. 202-208, Feb. 2022

    2022

  6. [14]

    Hologra phic smart EM skins for ad- vanced beam power shaping in next generation wireless envir onments,

    G. Oliveri, P . Rocca, M. Salucci, and A. Massa, "Hologra phic smart EM skins for ad- vanced beam power shaping in next generation wireless envir onments," IEEE J. Multi- scale Multiphys. Comput. Tech., vol. 6, pp. 171-182, Oct. 2021

    2021

  7. [15]

    Macroscopic modeli ng of anomalously reflecting metasurfaces: angular response and far-field scattering,

    A. Diaz-Rubio and S. A. Tretyakov, "Macroscopic modeli ng of anomalously reflecting metasurfaces: angular response and far-field scattering," IEEE Trans. Antennas Propag. , vol. 69, no. 10, pp. 6560-6571, Oct. 2021

    2021

  8. [16]

    Building a smart EM envi- ronment - AI-enhanced aperiodic micro-scale design of pass ive EM skins,

    G. Oliveri, F. Zardi, P . Rocca, M. Salucci, and A. Massa, "Building a smart EM envi- ronment - AI-enhanced aperiodic micro-scale design of pass ive EM skins," IEEE Trans. Antennas Propag., vol. 70, no. 10, pp. 8757-8770, Oct. 2022

    2022

  9. [17]

    Constrained design of passive static EM skins,

    G. Oliveri, F. Zardi, P . Rocca, M. Salucci, and A. Massa, “Constrained design of passive static EM skins,” IEEE Trans. Antennas Propag., vol. 71, no. 2, pp. 1528-1538, Feb. 2023. 23

    2023

  10. [18]

    On the design of modular reflecting EM skins for enhanced urban w ireless coverage,

    P . Rocca, P . Da Ru, N. Anselmi, M. Salucci, G. Oliveri, D. Erricolo, and A. Massa, “On the design of modular reflecting EM skins for enhanced urban w ireless coverage,” IEEE Trans. Antennas Propag., vol. 70, no. 10, pp. 8771-8784, Oct. 2022

    2022

  11. [19]

    Perfect no n-specular reflection with polar- ization control by using a locally passive metasurface shee t on a grounded dielectric slab,

    C. Y epes, M. Faenzi, S. Maci, and E. Martini, "Perfect no n-specular reflection with polar- ization control by using a locally passive metasurface shee t on a grounded dielectric slab," Appl. Phys. Lett. , vol. 118, no. 23, Jun. 2021

    2021

  12. [20]

    Low-profile and low-visual impact smart electromagnetic curved passive sk ins for enhancing connectivity in urban scenarios,

    A. Freni, M. Beccaria, A. Mazzinghi, A. Massaccesi, and P . Pirinoli, "Low-profile and low-visual impact smart electromagnetic curved passive sk ins for enhancing connectivity in urban scenarios," Electronics, vol. 12, no. 21, p. 4491, Nov. 2023

    2023

  13. [21]

    Reradiation and scattering from a reconfigurable intelligent surface: a gen eral macroscopic model,

    V . Degli-Esposti, E. M. Vitucci, M. D. Renzo, and S. A. Tr etyakov, "Reradiation and scattering from a reconfigurable intelligent surface: a gen eral macroscopic model," IEEE Trans. Antennas Propag., vol. 70, no. 10, pp. 8691-8706, Oct. 2022

    2022

  14. [22]

    An angle-insensitive 3-bit reconfigurable intelligent surfa ce,

    J. C. Liang, Q. Cheng, Y . Gao, C. Xiao, S. Gao, L. Zhang, S. Jin, and T. J. Cui, "An angle-insensitive 3-bit reconfigurable intelligent surfa ce," IEEE Trans. Antennas Propag., vol. 70, no. 10, pp. 8798-8808, Oct. 2022

    2022

  15. [23]

    On the study of reconfigurable intelligent surfaces in the near-field region,

    P . Mei, Y . Cai, K. Zhao, Z. Ying, G. F. Pedersen, X. Q. Lin, and S. Zhang, "On the study of reconfigurable intelligent surfaces in the near-field region," IEEE Trans. Antennas Propag., vol. 70, no. 10, pp. 8718-8728, Oct. 2022

    2022

  16. [24]

    Macromodeling of reconfigurable intelligent surface based on microwave network theory,

    Z. Zhang, J. W. Zhang, J. W. Wu, J. C. Liang, Z. X. Wang, Q. C heng, Q. S. Cheng, T. J. Cui, H. Q. Y ang, G. B. Liu, and S. R. Wang, "Macromodeling of reconfigurable intelligent surface based on microwave network theory," IEEE Trans. Antennas Propag., vol. 70, no. 10, pp. 8707-...

    2022

  17. [25]

    Multi-scale single-bit RP- EMS synthesis for advanced propagation manipulation throu gh system-by-design,

    G. Oliveri, P . Rocca, M. Salucci, D. Erricolo, and A. Mas sa, “Multi-scale single-bit RP- EMS synthesis for advanced propagation manipulation throu gh system-by-design,” IEEE Trans. Antennas Propag., vol. 70, no. 10, pp. 8809-8824, Oct. 2022. 24

    2022

  18. [27]

    Metasurfaces 3.0: a new paradigm f or enabling smart electro- magnetic environments,

    M. Barbuto, Z. Hamzavi-Zarghani, M. Longhi, A. Monti, D . Ramaccia, S. V ellucci, A. Toscano, and F. Bilotti, "Metasurfaces 3.0: a new paradigm f or enabling smart electro- magnetic environments," IEEE Trans. Antennas Propag. , vol. 70, no. 10, pp. 8883-8897, Oct. 2022

    2022

  19. [28]

    A statistical approa ch for robust metasurfaces and metasurface-based RIS engineering,

    L. Stefanini, D. Ramaccia, M. Barbuto, Z. Hamzavi-Zarg hani, M. Longhi, A. Monti, S. V ellucci, A. Toscano, and F. Bilotti, "A statistical approa ch for robust metasurfaces and metasurface-based RIS engineering," IEEE Trans. Antennas Propag. , vol. 72, no. 6, pp. 5402-5407, Jun. 2024

    2024

  20. [29]

    Development of two-dimensional steerable reflectarray with liquid crystal for reconfigurable intelli gent surface applications,

    X. Li, H. Sato, H. Fujikake, and Q. Chen, "Development of two-dimensional steerable reflectarray with liquid crystal for reconfigurable intelli gent surface applications," IEEE Trans. Antennas Propag., vol. 72, no. 3, pp. 2108-2123, Mar. 2024

    2024

  21. [30]

    Toward re al-world indoor smart electro- magnetic environments - A large-scale experimental demons tration,

    A. Benoni, F. Capra, M. Salucci, and A. Massa, "Toward re al-world indoor smart electro- magnetic environments - A large-scale experimental demons tration," IEEE Trans. Anten- nas Propag., vol. 71, no. 11, pp. 8450-8463, Nov. 2023

    2023

  22. [31]

    Re- configurable intelligent surface (RIS) in the sub-6 GHz band : design, implementation, and real-world demonstration,

    A. Araghi, M. Khalily, M. Safaei, A. Bagheri, V . Singh, F . Wang, and R. Tafazolli, "Re- configurable intelligent surface (RIS) in the sub-6 GHz band : design, implementation, and real-world demonstration," IEEE Access, vol. 10, pp. 2646-2655, Jan. 2022

    2022

  23. [32]

    Indoor coverage en- hancement for RIS-assisted communication systems: Practical measurements and efficient grouping,

    S. Kayraklik, I. Yildirim, Y . Gevez, E. Basar, and A. Gor cin, "Indoor coverage en- hancement for RIS-assisted communication systems: Practical measurements and efficient grouping," ICC 2023 - IEEE Int. Conf. Commun. , Rome, Italy, May 2023, pp. 485-490

    2023

  24. [33]

    Experimental evaluation of multi-operator RIS-assisted links in indoor environment,

    M. Lodro, J.-B. Gros, S. Greedy, G. Lerosey, A. Al Rawi, a nd G. Gradoni, “Experimental evaluation of multi-operator RIS-assisted links in indoor environment,” arXiv:2206.07788 [eess.SP], Jun. 2022. 25

    2022 arXiv

  25. [34]

    High-resolution programmable scattering for wireless co verage enhancement: an indoor field trial campaign,

    J. Rains, J. Kazim, A. Tukmanov, T. J. Cui, L. Zhang, Q. H. Abbasi, and M. A. Imran, "High-resolution programmable scattering for wireless co verage enhancement: an indoor field trial campaign," IEEE Trans. Antennas Propag. , vol. 71, no. 1, pp. 518-530, Jan. 2023

    2023

  26. [35]

    Reconfigurable intelligent surfa ce-based wireless communi- cations: antenna design, prototyping, and experimental re sults,

    L. Dai, B. Wang, M. Wang, X. Y ang, J. Tan, S. Bi, S. Xu, F. Y ang, Z. Chen, M. D. Renzo, C.-B. Chae, and L. Hanzo, "Reconfigurable intelligent surfa ce-based wireless communi- cations: antenna design, prototyping, and experimental re sults," IEEE Access, vol. 8, pp. 45913-4592...

    2020

  27. [36]

    RIS-Aided wireless communications: prototyping, adaptive beamform ing, and indoor/outdoor field trials,

    X. Pei, H. Yin, L. Tan, L. Cao, Z. Li, K. Wang, K. Zhang, and E. Bjornson, “RIS-Aided wireless communications: prototyping, adaptive beamform ing, and indoor/outdoor field trials,” IEEE Trans. Commun., vol. 69, no. 12, pp. 8627- 8640, Dec. 2021

    2021

  28. [37]

    Planning of EM skins for improved quality-of-service in urban areas,

    A. Benoni, M. Salucci, G. Oliveri, P . Rocca, B. Li, and A. Massa, “Planning of EM skins for improved quality-of-service in urban areas,” IEEE Trans. Antennas Propag. , vol. 70, no. 10, pp. 8849-8862, Oct. 2022

    2022

  29. [38]

    A multi-hop strategy for the planning of EM skins in a smart electromagnetic environm ent,

    M. Salucci, A. Benoni, G. Oliveri, P . Rocca, B. Li, and A. Massa, “A multi-hop strategy for the planning of EM skins in a smart electromagnetic environm ent,” IEEE Trans. Antennas Propag., vol. 71, no. 3, pp. 2758-2767, Mar. 2023

    2023

  30. [39]

    Coverage enhancement by deploying RIS in 5G commercial mobile networ ks: Field trials,

    J. Sang, Y . Y uan, W. Tang, Y . Li, X. Li, S. Jin, Q. Cheng, an d T. J. Cui, “Coverage enhancement by deploying RIS in 5G commercial mobile networ ks: Field trials,” IEEE Trans. Wireless Commun., vol. 31, no. 1, pp. 172-180, Feb. 2024

    2024

  31. [40]

    Design and eva luation of reconfigurable intelligent surfaces in real-world environment,

    G. C. Trichopoulos, P . Theofanopoulos, B. Kashyap, A. Shekhawat, A. Modi, T. Osman, S. Kumar, A. Sengar, A. Chang, and A. Alkhateeb, “Design and eva luation of reconfigurable intelligent surfaces in real-world environment,” IEEE Open J. Commun. Soc. , vol. 3, pp. 462-474, 2022

    2022

  32. [41]

    Path loss modeling and measurements for reconfigurabl e intelligent surfaces in the 26 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 reconfigurabl e intelligent surfaces in the 26 millimeter-wave frequency band," IEEE Trans. Commun ., vol. 70, no. 9, pp. 6259-6276, Sep. 2022

    2022

  33. [42]

    A wideband rec onfigurable intelligent surface for 5G millimeter-wave applications,

    R. Wang, Y . Y ang, B. Makki, and A. Shamim, "A wideband rec onfigurable intelligent surface for 5G millimeter-wave applications," IEEE Trans. Antennas Propag., vol. 72, no. 3, pp. 2399-2410, Mar. 2024

    2024

  34. [43]

    Generalized anal ysis and unified design of EM skins,

    G. Oliveri, M. Salucci, and A. Massa, "Generalized anal ysis and unified design of EM skins," IEEE Trans. Antennas Propag., vol. 71, no. 8, pp. 6579-6592, Aug. 2023

    2023

  35. [44]

    Active control of THz waves in wireless environments using graphene-based RI S,

    S. Dash, C. Psomas, I. Krikidis, I. F. Akyildiz, and A. Pi tsillides, "Active control of THz waves in wireless environments using graphene-based RI S," IEEE Trans. Antennas Propag., vol. 70, no. 10, pp. 8785-8797, Oct. 2022

    2022

  36. [45]

    Computation-efficient refle ction coefficient design for graphene-based RIS in wireless communications,

    J. Lee, H. Seo, and W. Choi, "Computation-efficient refle ction coefficient design for graphene-based RIS in wireless communications," IEEE Trans. V eh. Technol., vol. 73, no. 3, pp. 3663-3677, Mar. 2024

    2024

  37. [46]

    Holis tic enlightening of blackspots with passive tailorable reflecting surfaces for efficient ur ban mmWave networks,

    S. Hager, K. Heimann, S. Bocker, and C. Wietfeld, "Holis tic enlightening of blackspots with passive tailorable reflecting surfaces for efficient ur ban mmWave networks," IEEE Access, vol. 11, pp. 39318-39332, 2023

    2023

  38. [47]

    Lens based switched beam antenna for a 5G smart repeater,

    J. Romeu, S. Blanch, L. Pradell, A. Barlabe, J. -M. Rius, M. Albert-Gali, L. Jofre-Roca, C. Mazzucco, and R. Flamini, "Lens based switched beam antenna for a 5G smart repeater," IEEE Antennas Wireless Propag. Lett., vol. 22, no. 10, pp. 2482-2486, Oct. 2023

    2023

  39. [48]

    Network- controlled repeaters vs. reconfigurable intelligent surfa ces for 6G mmW coverage exten- sion: a simulative comparison,

    R. A. Ayoubi, M. Mizmizi, D. Tagliaferri, D. De Donno, an d U. Spagnolini, "Network- controlled repeaters vs. reconfigurable intelligent surfa ces for 6G mmW coverage exten- sion: a simulative comparison," 2023 21st Mediterranean Communication and Computer Networking Conference...

    2023

  40. [49]

    Metasurface coatin gs enabling antenna reconfig- urability for next-generation communications smart repea ters,

    S. V ellucci, A. Monti, M. Barbuto, Z. Hamzavi-Zarghani , M. Longhi, D. Ramaccia, L. Stefanini, A. Toscano, and F. Bilotti, "Metasurface coatin gs enabling antenna reconfig- urability for next-generation communications smart repea ters," 2023 27th International 27 Congress on A...

    2023

  41. [50]

    Shaping a smarter electromagnetic landscape: IAB, NCR, an d RIS in 5G standard and future 6G,

    C. -K. Wen, L. -S. Tsai, A. Shojaeifard, P . -K. Liao, K. -K . Wong, and C. -B. Chae, "Shaping a smarter electromagnetic landscape: IAB, NCR, an d RIS in 5G standard and future 6G," IEEE Commun. Standards Mag. , vol. 8, no. 1, pp. 72-78, Mar. 2024

    2024

  42. [51]

    Spectrum and energy efficiencies for multiuser pairs massive MIMO systems with full-duplex amplify-and-f orward relay,

    X. Jia, P . Deng, L. Y ang, and H. Zhu, "Spectrum and energy efficiencies for multiuser pairs massive MIMO systems with full-duplex amplify-and-f orward relay," IEEE Access, vol. 3, pp. 1907-1918, Oct. 2015

    1907

  43. [52]

    On integrated access and backhaul networks: current statu s and potentials,

    C. Madapatha, B. Makki, C. Fang, O. Teyeb, E. Dahlman, M. -S. Alouini, and T. Svensson, "On integrated access and backhaul networks: current statu s and potentials," IEEE Open J. Commun. Soc., vol. 1, pp. 1374-1389, 2020

    2020

  44. [53]

    5G; NR repeater radio transmission and receptio n,

    3GPP , “5G; NR repeater radio transmission and receptio n,” Tech. Rep., Release 17, TS 38.106, v. 17.1.0, 2022-08

    2022

  45. [54]

    Routing and resource allocat ion for IAB multi-hop network in 5G advanced,

    H. Yin, S. Roy, and L. Cao, "Routing and resource allocat ion for IAB multi-hop network in 5G advanced," IEEE Trans. Commun., vol. 70, no. 10, pp. 6704-6717, Oct. 2022

    2022

  46. [55]

    5G; NR integrated access and backhaul (IAB) radio transmiss ion and reception,

    3GPP , “5G; NR integrated access and backhaul (IAB) radio transmiss ion and reception,” Tech. Rep., Release 16, TS 38.174, v. 16.2.0, 2021-04

    2021

  47. [56]

    Topology planning using Q-learning for microwave- based wireless backhaul networks,

    L. Li, Y . Li, S. K. Bose, and G. Shen, "Topology planning using Q-learning for microwave- based wireless backhaul networks," IEEE Trans. Cogn. Commun. Netw., vol. 9, no. 4, pp. 1041-1052, Aug. 2023

    2023

  48. [57]

    Boosting 5G mm-wave IAB reliability with reconfigurable intelligent surfaces,

    P . Fiore, E. Moro, I. Filippini, A. Capone, and D. D. Donn o, "Boosting 5G mm-wave IAB reliability with reconfigurable intelligent surfaces," 2022 IEEE Wirel. Commun. Netw. Conf. (WCNC), Austin, TX, USA, pp. 758-763, 2022

    2022

  49. [58]

    Towards reliable mmWave 6G RAN: Reconfigurable surfaces, smart repeaters, or both?,

    G. Leone, E. Moro, I. Filippini, A. Capone, and D. D. Donn o, "Towards reliable mmWave 6G RAN: Reconfigurable surfaces, smart repeaters, or both?, " 2022 20th International 28 Symposium on Modeling and Optimization in Mobile, Ad hoc, an d Wireless Networks (WiOpt), Torino, Ital...

    2022

  50. [59]

    On antenna parameters self optimization in LTE cellular networks,

    F. Kasem, A. Haskou, and Z. Dawy, "On antenna parameters self optimization in LTE cellular networks," 2013 Third Int. Conf. Commun. Inf. Technol.(ICCIT), Beirut, Lebanon, Jun. 2013, pp. 44-48

    2013

  51. [60]

    Dynamic antenna azimuth pl anning for 3G, 4G and future 5G broadband radio networks

    Y . Y ou and D. Kolokotronis, “Dynamic antenna azimuth pl anning for 3G, 4G and future 5G broadband radio networks”, ISWCS Workshop on Ultra-Dense Cell-Less 5G Cellular Networks: Wireless Access and Programmable Network Archit ecture (5G Cell-Less Nets) (ISWCS), Bologna, Italy...

    2017

  52. [61]

    A fast a nd elitist multiobjective genetic algorithm: NSGA-II,

    K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast a nd elitist multiobjective genetic algorithm: NSGA-II," IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182-197, Apr. 2002

    2002

  53. [62]

    No free lunch theorems for optimization,

    D. H. Wolpert and W. G. Macready, "No free lunch theorems for optimization," IEEE Trans. Evol. Computat., vol. 1, no. 1, pp. 67-82, Apr. 1997

    1997

  54. [63]

    Accessed on: June 26, 2024

    OpenStreetMap. Accessed on: June 26, 2024. [Online]. A vailable: https://www.openstreetmap.org/

    2024

  55. [64]

    Altair Winprop 2021, Altair Engineering, Inc., www.al tairhyperworks.com/feko

    2021

  56. [65]

    D. J. Daniels, Ground Penetrating Radar (2nd Ed.), London, UK: The Institution of Elec- trical Engineers, 2004

    2004

  57. [66]

    ANSYS, Inc

    ANSYS Electromagnetics Suite - HFSS (2021). ANSYS, Inc

    2021

  58. [67]

    Accessed on: Jun

    Data Sheet CEL-FI GO G51 5G Dual Band Cellular Mobile Phone Repeater . Accessed on: Jun. 2024. [Online] Available: https://tincan.solutions/media/wysiwyg/pdf/products/celfi/Cel-Fi-GO-G51-Datasheet.pdf

    2024

  59. [68]

    Integrated access and backhaul in 5G mmwave networks: potential and cha llenges,

    M. Polese, M. Giordani, T. Zugno, A. Roy, S. Goyal, D. Cas tor, and M. Zorzi, "Integrated access and backhaul in 5G mmwave networks: potential and cha llenges," IEEE Commun. Mag., vol. 58, no. 3, pp. 62-68, Mar. 2020. 29

    2020

  60. [69]

    Unfeasible Incident Angle

    Google, Google Maps. Accessed on: Jun. 2024 [Online]. A vailable: https://www.google.com/maps 30 FIGURE CAPTIONS • Figure 1. Sketch of an urban time-varying scenario at different time i nstants: ( a) t1 and (b) t2. • Figure 2. Sketch of an heterogeneous SEME. • Figure 3. Block...

    2024

Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.