Dimensioning and Optimization of Reliability Coverage in Local 6G Networks

Andr\'e Gomes; Arthur S. de Sena; Dian Echevarr\'ia P\'erez; Hirley Alves; Jacek Kibi{\l}da; Nurul Huda Mahmood; Onel L. Alcaraz L\'opez

REVIEW 3 major objections 5 minor 15 references

Dimensioning and Optimization of Reliability Coverage in Local 6G Networks

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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

Pith's one-line read Reliability coverage unifies 6G network dimensioning and optimization.

desk verdict Useful reliability-coverage framework, but the quantitative case study leans on an unvalidated latency model that needs sensitivity analysis. read the letter →

arxiv 2505.09440 v1 pith:UQX7F5KC submitted 2025-05-14 cs.NI

Jacek Kibi{l}da , Dian Echevarría Pérez , André Gomes , Onel L. Alcaraz López , Arthur S. de Sena , Nurul Huda Mahmood , Hirley Alves This is my paper · ORCID

classification cs.NI

keywords reliabilitycoverageresourcedimensioningoptimizationURLLCHRLLClocal6Gnetworksextremevaluetheoryradiomaps

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 introduces reliability coverage — the percentage of a service area whose links meet a stated latency and reliability target — and argues that this single figure can drive both long-term resource dimensioning and near-real-time resource optimization in local 6G networks. The practical claim is that a vertical, such as a factory or a hospital, can express its needs as a triple $(\gamma, \alpha^\star, \eta^\star)$ and receive back concrete resource levels, such as spectrum bandwidth and access-point density, that satisfy them. System-level simulations of a $200 \times 200$ m industrial scenario with a 3D urban-microcell channel model support the framework, showing that more stringent reliability and latency targets translate into higher spectrum demand and that network density helps at high coverage targets but can hurt in interference-limited low-coverage regimes. The framework matters because it replaces per-link reliability figures with a service-area objective, matching how verticals actually deploy mission-critical services.

What carries the argument

The load-bearing object is the reliability-coverage functional $\eta(\theta,\gamma,\alpha^\star)$, which converts a vertical's target triple into a single percentage that both design loops optimize. The mapping from SINR to the performance metric $\Gamma(\theta)$ — user-plane latency taken as inversely proportional to Shannon capacity — is what ties reliability to resources; without it, the coverage percentage cannot be computed. In the optimization phase, extreme-value-theory-enriched radio maps estimate the tail behavior of SINR across space and feed the same $\eta$ functional, so the two loops share one objective and can be compared directly.

What would settle it

Replay the paper's 200 m x 200 m, 20-access-point, 10 MHz scenario in a protocol-level simulator with MAC scheduling, retransmissions, and queueing, and compare the measured reliability coverage at $\alpha^\star = 99.999\%$, $\gamma = 1$ ms with the paper's approximately 80%; a large gap would falsify the inverse-Shannon latency mapping that carries the framework.

Watch

Extended reading notes

Core claim

The central claim is that reliability coverage, defined as $\eta(\theta,\gamma,\alpha^\star)=|\{x\in\mathcal{X}:\alpha_x(\theta,\gamma)\ge\alpha^\star\}|/|\mathcal{X}|$, is a sufficient design objective for the two time scales of network control. Here $\alpha_x(\theta,\gamma)=\Pr(\Gamma(\theta)\ge\gamma\mid x)$ is the per-location link reliability, with $\Gamma(\theta)$ an SINR-based performance metric (user-plane latency, modeled as inversely proportional to Shannon capacity), and $\theta=\{w,n\}$ the network resources (spectrum and access-point count). The paper shows by simulation that dimensioning $\theta$ so that $\eta\ge\eta^\star$ and optimizing the resulting deployment through tail-focused radio maps yield consistent answers: both phases indicate that higher reliability/latency targets require more spectrum, that densification helps only in the high-coverage regime, and that localization error demands more conservative optimization. The framework is deliberately resource-agnostic: any resource parameter that can be mapped onto $\Gamma(\theta)$ can be dimensioned in the same way.

Load-bearing premise

The paper's numbers rest on treating user-plane latency as simply inversely proportional to capacity, and all coverage percentages follow from that mapping; if real delays include waiting, scheduling, and resending, the predicted spectrum and density requirements would shift.

Editorial extensions

If this is right

A vertical can be served by handing over only $(\gamma, \alpha^\star, \eta^\star)$; the operator converts this triple into spectrum and density targets without additional input.
At $\alpha^\star = 99.999\%$ with 20 access points and 10 MHz, reliability coverage is about 80%; reaching 99% coverage requires more than 20 MHz.
Densification is a double-edged sword: in interference-limited low-coverage regimes it increases the bandwidth needed, but at $\eta^\star \ge 99\%$ deployments with 15 or more access points meet the target at roughly 20 MHz while sparse networks need tens or hundreds of MHz.
For a fixed deployment, tightening the latency target shrinks coverage: with 5 access points and 50 MHz, going from $\gamma=1$ ms to $\gamma=0.1$ ms lowers $\alpha^\star=99.999\%$ coverage from 95.2% to 78.51%.
The same $\eta$ objective can dimension other resource types (power, antennas, reconfigurable intelligent surfaces) because the method only needs a map from configuration $\theta$ to $\Gamma(\theta)$.

Reading between the lines

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

Beyond the paper, the quantitative results should be read as illustrations of the latency-to-capacity mapping: if user-plane latency were modeled with queueing, scheduling, or finite blocklength, the required spectrum and density would shift, though the framework's two-loop consistency would likely survive.
The consistency between dimensioning and optimization suggests that one learned reliability-coverage map could serve both loops, turning the framework into a digital-twin objective for local 6G deployments.
A natural test is to feed the same SINR traces to a machine-learning performance manifold and compare its coverage estimates with the paper's statistical-model outputs; agreement would make data-driven coverage maps a viable substitute for analytic dimensioning.
Pairing $\eta(\theta,\gamma,\alpha^\star)$ with risk measures such as value-at-risk or conditional value-at-risk (listed as future work in the paper) could turn the reliability requirement into a spectral risk constraint on latency and yield closed-form resource-sizing rules.

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.

Desk Editor's Note

read the letter

The worthwhile idea here is the definition of reliability coverage (Eq. 2), which gives verticals a single target—performance gamma, link reliability alpha*, area fraction eta*—and lets network designers map that triple to resource levels. That is genuinely useful and not circular: the metric is defined on the SINR distribution, and the two tasks (dimensioning vs. optimization) are separate. The paper also does an honest job of positioning itself against meta-distribution and cell availability, and the EVT radio-map connection is a natural extension.

The soft spots are real but localized. The numerical case study assumes user-plane latency is inversely proportional to Shannon capacity. That is an acknowledged simplification; the problem is that it is load-bearing for every number in Figs. 3–6. Real URLLC latency is shaped by TTI structure, scheduling, queuing, and HARQ, and it doesn't go smoothly to zero as SINR grows. The authors explicitly flag it as a phase-one simplification, but they don't provide sensitivity analysis or a discrete-time comparison. So the specific MHz and AP counts should be read as illustrative, not quantitative predictions. Also, the simulation details are thin: no sample counts, error bars, or confidence intervals. The consistency claim between Fig. 4 and Fig. 6 is approximately right (98% vs 95.2%), but the small gap is unexplained and the paper calls it 'remarkable' without statistical support.

That said, the framework itself does not depend on the latency assumption. If you replace the metric with a more realistic end-to-end delay model, the two-phase methodology still stands. The paper deserves a serious referee: the concept is worth publishing, and the missing validation and sensitivity analysis are fixable in revision.

My recommendation: send it to peer review, with the expectation of a major revision that adds simulation details, error bars, and at least a basic sanity check on the latency model.

Referee Report

3 major / 5 minor

Summary. The paper introduces the concept of "reliability coverage", defined as the percentage of a service area in which a link-level reliability target α* is met for a given performance target γ under a network configuration θ. The authors propose a two-phase design framework: a non-real-time resource dimensioning phase, in which spectrum and AP density are selected to satisfy a vertical's requirements expressed as a triple (γ, α*, η*), and a near-real-time resource optimization phase based on reliability coverage maps (with their prior EVT-based radio-map work referenced). The numerical study uses a 200×200 m industrial scenario with the 3GPP 3D-UMi model, an interference-limited assumption, and a user-plane latency metric defined as inversely proportional to Shannon capacity. System-level simulations are used to show how reliability coverage varies with bandwidth and AP density (Figs. 3–4), and how optimized resource allocation meets outage targets over the coverage area (Figs. 5–6). The claimed contribution is a unified framework that lets verticals translate reliability/latency/coverage targets into concrete network resource levels.

Significance. If the framework holds up, it provides an appealing and practically motivated design objective: instead of per-link reliability, a vertical can specify an area-based reliability coverage target and directly obtain resource dimensioning and allocation guidance. The two-phase structure, with its separation of non-real-time dimensioning and near-real-time optimization, is a coherent and useful organizing principle, and the consistency between the dimensioning and optimization results (Figs. 3–6) is a genuine strength. The paper also builds on prior work on meta-distributions and EVT-enriched radio maps, which gives the framework a credible foundation in the stochastic-geometry and extreme-value literature. The central concept is clearly defined and the framework is presented in a way that is easy to understand and potentially adaptable to other performance metrics and network models. The main weaknesses are the unvalidated latency model and the lack of reproducibility details for the simulations, both of which affect the quantitative claims.

major comments (3)

[Section IV] The assumption that user-plane latency is inversely proportional to Shannon capacity is load-bearing for every quantitative result in the paper, including Figs. 3–6, because it converts the latency deadline γ into a deterministic SINR threshold. Real URLLC latency is not a smooth inverse function of SINR: it includes scheduling opportunity, TTI structure, processing time, queuing, and HARQ retransmissions, and it saturates at high SINR while deteriorating sharply at low SINR. The paper explicitly labels this as an assumption but provides no sensitivity analysis, no comparison with a discrete-time model, and no estimate of how much the required bandwidth or AP density would change under a more realistic latency model. I request either a validation of this mapping against a more detailed URLLC latency model or a systematic sensitivity study showing that the qualitative and quantitative conclusions are robust to the choice of latency model.
[Section IV and Section V (Figs. 3–6)] The central numerical results come from Monte Carlo simulations whose details are not reported: no sample sizes, number of drops, confidence intervals, or error bars are given, and it is unclear how the empirical reliability α_x(θ, γ) is estimated for extreme targets such as α* = 99.999% (i.e., outages of 10^-5). Without this information, the reader cannot assess the statistical significance of the differences reported in Figs. 3–6, such as the claim that reliability coverage drops from 99.85% to 95.2% when α* goes from 99.9% to 99.999%. The paper should specify the simulation methodology, provide error bars or confidence regions, and state the number of independent realizations used.
[Section V] The resource optimization phase is not described in enough detail to be reproducible. The text refers to "optimized resources" and a "resource allocation policy", and Fig. 5 plots outage probabilities under an optimized configuration, but the optimization objective, the decision variables, the constraints, and the algorithm used are not given. It is also unclear whether the results in Fig. 5–6 are obtained from the EVT-based radio-map method cited as [13] or from direct simulation; if the former, the EVT estimation procedure (threshold selection, number of measurements, map interpolation) should be stated, and if the latter, the connection to the claimed framework's optimization phase needs clarification.

minor comments (5)

[Abstract and Introduction] Minor language issues: "self-driving fleet" should be plural ("self-driving fleets"), and the phrase "there is the rub" is informal for a journal article; consider rephrasing.
[Section II, Eq. (1)] The notation α_x(θ, γ) is defined as a probability but the subscript x appears only on the left-hand side; it would be clearer to write α_x(θ, γ) = Pr(Γ_x(θ) ≥ γ) or to define Γ(θ) as location-dependent.
[Figures 3 and 4] The vertical axis label "Reliability coverage ( )" appears to be missing the symbol η, and the caption of Fig. 3 uses "N = 20 APs" while the text in Section IV uses lowercase n for the number of APs. Please standardize the notation.
[Section IV] The sentence "As interference can complicate meeting URLLC requirements, by focusing on interference-limited networks, we can estimate the upper bound of what it takes to orchestrate resources" is grammatically awkward and the logical argument is unclear; please rewrite for clarity.
[Section V, Fig. 5] The caption refers to a logarithmic scale for the outage map, but it would be helpful to state the color scale explicitly (the caption says "log10(O)" in the figure itself, which is fine) and to indicate the AP positions with markers as done in the figure.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: reliability coverage is a definition, and the dimensioning/optimization results come from direct simulation; self-citations supply methodology but are not load-bearing.

full rationale

The paper's central object, reliability coverage, is explicitly defined in Equations (1)-(2) as the area fraction where the per-location reliability Pr(gamma(theta) >= gamma) meets alpha_star. This is a definition, not a derived prediction. The dimensioning study (Section IV) computes coverage directly from system-level simulation of the 3GPP UMi model for varying bandwidth and AP density; no fitted parameter is renamed as a prediction. The optimization study (Section V) invokes the authors' prior EVT-based radio-map work [13] as a tool, but the reported outage maps in Figures 5-6 are described as simulated outage probabilities, not as outputs of a fitted model being fed back as validation. The 'remarkable consistency' between Figures 4 and 6 is a self-consistency check between two evaluations built on the same simulator and the same latency model, which is a sanity check rather than an independent validation; the paper does not present it as external empirical evidence. The main visible self-citations ([11]-[13]) supply the EVT/radio-map methodology and prior spectrum-dimensioning studies, but the framework's core claim--that vertical requirements (gamma, alpha_star, eta_star) can be mapped to resource levels theta via reliability coverage--is not justified by those citations alone; it is demonstrated by the paper's own simulations. The assumption that user-plane latency is inversely proportional to Shannon capacity is an input modeling assumption and a correctness risk, not a circular step, because the paper does not derive that assumption from its conclusions; it explicitly labels it a phase-one simplification. No uniqueness theorem, no ansatz smuggled via citation, and no renaming of a known result as a derivation were found. The circularity burden is therefore low, and no specific reduction of an output to an input by construction could be exhibited.

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

The paper introduces a new metric (reliability coverage), not a physical entity. No parameters are fitted to data; the central results come from Monte Carlo simulation under a stated set of modeling assumptions. The main unverified premises are the latency model and the applicability of the 3GPP channel model to local industrial networks.

assumptions (4)

domain assumption SINR is the first-order predictor of link reliability and of user-plane latency.
The whole framework expresses performance as Pr(SINR >= gamma | x); invoked in Section I and Equation (1).
ad hoc to paper User-plane latency is inversely proportional to Shannon capacity.
Section IV states this without derivation; it converts SINR to latency and is used for all coverage figures.
domain assumption The 3GPP 3D-UMi channel model with binomial point process deployment represents a local 6G industrial network.
Section IV specifies the simulation scenario, but no field data validates the model choice.
domain assumption APs are constantly transmitting in the same band (interference-limited regime with no interference control).
Section IV states this; results depend on the absence of interference management.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Dimensioning and Optimization of Reliability Coverage in Local 6G Networks." pith.science (2026). https://pith.science/paper/UQX7F5KC

@misc{pith2026250509440,
  author       = {Pith},
  title        = {Pith review of: Dimensioning and Optimization of Reliability Coverage in Local 6G Networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UQX7F5KC}},
  note         = {Machine review of arXiv:2505.09440}
}

read the original abstract

Enabling vertical use cases for the sixth generation (6G) wireless networks, such as automated manufacturing, immersive extended reality (XR), and self-driving fleets, will require network designs that meet reliability and latency targets in well-defined service areas. In order to establish a quantifiable design objective, we introduce the novel concept of reliability coverage, defined as the percentage area covered by communication services operating under well-defined reliability and performance targets. Reliability coverage allows us to unify the different network design tasks occurring at different time scales, namely resource orchestration and allocation, resulting in a single framework for dimensioning and optimization in local 6G networks. The two time scales, when considered together, yield remarkably consistent results and allow us to observe how stringent reliability/latency requirements translate into the increased wireless network resource demands.

Figures

Figures reproduced from arXiv: 2505.09440 by the authors.

**Figure 2.** Framework overview: A vertical sets its targets ( [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Reliability coverage for different α ⋆-reliability levels as a function of the bandwidth for a network density of N = 20 APs. Note: η ∈ [0, 1] and α ⋆ ∈ [0, 1]. knowledge of SINR is critical for effective resource optimization, and for the reliability coverage estimation. This knowledge can be categorized as instantaneous SINR, vital for real-time adjustments, and statistical SINR, which offers insights into long-… view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Reliability coverage for different network densities as a function of [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 6.** Figure 6: Reliability coverage for various latency targets [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

15 extracted references · 15 canonical work pages

[13]

EVT-Enriched Radio Maps for Ultra-Reliable Communication,

P ´erez, Dian Echevarr ´ıa and L ´opez, Onel L Alcaraz and Alves, Hirley, “EVT-Enriched Radio Maps for Ultra-Reliable Communication,”IEEE Internet of Things Journal, 2025

work page 2025
[1]

Beginning of the Journey Toward 6G: Vision and Framework,

Liu, Ruiqi and Lin, Hui and Lee, Hyunjoong and Chaves, Fabiano and Lim, Hanna and Sk ¨old, Johan, “Beginning of the Journey Toward 6G: Vision and Framework,”IEEE Communications Magazine, vol. 61, no. 10, pp. 8–9, 2023

work page 2023
[2]

Statistical Tools and Methodologies for Ultrareliable Low-Latency Communication−A Tutorial,

O. L. A. L ´opez, N. H. Mahmood, M. Shehab, H. Alves, O. M. Rosabal, L. Marata, and M. Latva-Aho, “Statistical Tools and Methodologies for Ultrareliable Low-Latency Communication−A Tutorial,”Proceedings of the IEEE, vol. 111, no. 11, pp. 1502–1543, 2023

work page 2023
[3]

Extreme ultra-reliable and low-latency communication,

J. Park, S. Samarakoon, H. Shiri, M. K. Abdel-Aziz, T. Nishio, A. Elgabli, and M. Bennis, “Extreme ultra-reliable and low-latency communication,”Nature Electronics, vol. 5, pp. 133–141, 2022

work page 2022
[4]

Ultra-Reliable Low-Latency in 5G: A Close Reality or a Distant Goal?

A. Maghsoudnia, E. Vlad, A. Gong, D. M. Dumitriu, and H. Hassanieh, “Ultra-Reliable Low-Latency in 5G: A Close Reality or a Distant Goal?” inProceedings of the 23rd ACM Workshop on Hot Topics in Networks, 2024, pp. 111–120

work page 2024
[5]

Extreme communication in 6G: Vision and challenges for ‘in- X’ subnetworks,

G. Berardinelli, P. Baracca, R. O. Adeogun, S. R. Khosravirad, F. Schaich, K. Upadhya, D. Li, T. Tao, H. Viswanathan, and P. Mo- gensen, “Extreme communication in 6G: Vision and challenges for ‘in- X’ subnetworks,”IEEE Open Journal of the Communications Society, vol. 2, pp. 2516–2535, 2021

work page 2021
[6]

Minimum requirements related to technical performance for IMT-2020 radio interface(s),

ITU-R, “Minimum requirements related to technical performance for IMT-2020 radio interface(s),” International Telecommunication Union (ITU), Tech. Rep. ITU-R M.2410-0, Nov. 2017

work page 2020
[7]

Meta Distributions–Part 2: Properties and Interpretations,

M. Haenggi, “Meta Distributions–Part 2: Properties and Interpretations,” IEEE Communications Letters, vol. 25, no. 7, pp. 2094–2098, 2021

work page 2021

Show all 15 references

[8]

Achieving Ultra Reliable Communication in 5G Networks: A Dependability Perspective Avail- ability Analysis in the Space Domain,

Mendis, H. V . Kalpanie and Li, Frank Y ., “Achieving Ultra Reliable Communication in 5G Networks: A Dependability Perspective Avail- ability Analysis in the Space Domain,”IEEE Communications Letters, vol. 21, no. 9, pp. 2057–2060, 2017

2017
[9]

Deep Learning-Based Coverage and Rate Manifold Estimation in Cellular Networks,

Mondal, Washim Uddin and Mankar, Praful D. and Das, Goutam and Aggarwal, Vaneet and Ukkusuri, Satish V ., “Deep Learning-Based Coverage and Rate Manifold Estimation in Cellular Networks,”IEEE Transactions on Cognitive Communications and Networking, vol. 8, no. 4, pp. 1706–1715, 2022

2022
[10]

New trends in stochastic geometry for wireless networks: A tutorial and survey,

Hmamouche, Yassine and Benjillali, Mustapha and Saoudi, Samir and Yanikomeroglu, Halim and Di Renzo, Marco, “New trends in stochastic geometry for wireless networks: A tutorial and survey,”Proceedings of the IEEE, vol. 09, no. 7, pp. 1200–1252, 2021

2021
[11]

Assessing the spectrum needs for network-wide ultra-reliable communication with meta distributions,

A. Gomes, J. Kibiłda, and L. A. DaSilva, “Assessing the spectrum needs for network-wide ultra-reliable communication with meta distributions,” IEEE Communications Letters, vol. 27, no. 8, pp. 2242–2246, 2023. 7

2023
[12]

Dimensioning spectrum to support ultra-reliable low-latency communication,

A. Gomes, J. Kibiłda, N. Marchetti, and L. A. DaSilva, “Dimensioning spectrum to support ultra-reliable low-latency communication,”IEEE Communications Standards Magazine, vol. 7, no. 1, pp. 88–93, 2023

2023
[14]

Delivering ultra-reliable low-latency communications via statistical radio maps,

T. Kallehauge, A. E. Kalør, P. Ram ´ırez-Espinosa, M. Guillaud, and P. Popovski, “Delivering ultra-reliable low-latency communications via statistical radio maps,”IEEE Wireless Communications, vol. 30, no. 2, pp. 14–20, 2023

2023
[15]

Technical specification group radio access network; study on 3D channel model for LTE,

3GPP, “Technical specification group radio access network; study on 3D channel model for LTE,” 3rd Generation Partnership Project (3GPP), Tech. Rep. 36.873, Dec. 2017, version 12.7.0. Jacek Kibiłdais a faculty member with CCI and the Department of Electrical and Computer Engin...

2013

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [13]

EVT-Enriched Radio Maps for Ultra-Reliable Communication,

P ´erez, Dian Echevarr ´ıa and L ´opez, Onel L Alcaraz and Alves, Hirley, “EVT-Enriched Radio Maps for Ultra-Reliable Communication,”IEEE Internet of Things Journal, 2025

work page 2025

[2] [1]

Beginning of the Journey Toward 6G: Vision and Framework,

Liu, Ruiqi and Lin, Hui and Lee, Hyunjoong and Chaves, Fabiano and Lim, Hanna and Sk ¨old, Johan, “Beginning of the Journey Toward 6G: Vision and Framework,”IEEE Communications Magazine, vol. 61, no. 10, pp. 8–9, 2023

work page 2023

[3] [2]

Statistical Tools and Methodologies for Ultrareliable Low-Latency Communication−A Tutorial,

O. L. A. L ´opez, N. H. Mahmood, M. Shehab, H. Alves, O. M. Rosabal, L. Marata, and M. Latva-Aho, “Statistical Tools and Methodologies for Ultrareliable Low-Latency Communication−A Tutorial,”Proceedings of the IEEE, vol. 111, no. 11, pp. 1502–1543, 2023

work page 2023

[4] [3]

Extreme ultra-reliable and low-latency communication,

J. Park, S. Samarakoon, H. Shiri, M. K. Abdel-Aziz, T. Nishio, A. Elgabli, and M. Bennis, “Extreme ultra-reliable and low-latency communication,”Nature Electronics, vol. 5, pp. 133–141, 2022

work page 2022

[5] [4]

Ultra-Reliable Low-Latency in 5G: A Close Reality or a Distant Goal?

A. Maghsoudnia, E. Vlad, A. Gong, D. M. Dumitriu, and H. Hassanieh, “Ultra-Reliable Low-Latency in 5G: A Close Reality or a Distant Goal?” inProceedings of the 23rd ACM Workshop on Hot Topics in Networks, 2024, pp. 111–120

work page 2024

[6] [5]

Extreme communication in 6G: Vision and challenges for ‘in- X’ subnetworks,

G. Berardinelli, P. Baracca, R. O. Adeogun, S. R. Khosravirad, F. Schaich, K. Upadhya, D. Li, T. Tao, H. Viswanathan, and P. Mo- gensen, “Extreme communication in 6G: Vision and challenges for ‘in- X’ subnetworks,”IEEE Open Journal of the Communications Society, vol. 2, pp. 2516–2535, 2021

work page 2021

[7] [6]

Minimum requirements related to technical performance for IMT-2020 radio interface(s),

ITU-R, “Minimum requirements related to technical performance for IMT-2020 radio interface(s),” International Telecommunication Union (ITU), Tech. Rep. ITU-R M.2410-0, Nov. 2017

work page 2020

[8] [7]

Meta Distributions–Part 2: Properties and Interpretations,

M. Haenggi, “Meta Distributions–Part 2: Properties and Interpretations,” IEEE Communications Letters, vol. 25, no. 7, pp. 2094–2098, 2021

work page 2021