Evidence map›Paper›PMID 35336267›Full record

ArticleSensors (Basel, Switzerland)2022

3D Poisson-Based Neighborhood Capacity Analysis for Millimeter Wave Communications.

Massimiliano Comisso, Giulia Buttazzoni, Stefano Pastore, Francesca Vatta, Fulvio Babich

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 98% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Massimiliano ComissoDepartment of Engineering and Architecture, University of Trieste, Via A. Valerio 10, 34127 Trieste, Italy.ORCID 0000-0001-7261-1247
Giulia ButtazzoniDepartment of Engineering and Architecture, University of Trieste, Via A. Valerio 10, 34127 Trieste, Italy.ORCID 0000-0001-9105-6066
Stefano PastoreDepartment of Engineering and Architecture, University of Trieste, Via A. Valerio 10, 34127 Trieste, Italy.ORCID 0000-0001-9188-7855
Francesca VattaDepartment of Engineering and Architecture, University of Trieste, Via A. Valerio 10, 34127 Trieste, Italy.ORCID 0000-0003-4105-8084
Fulvio BabichDepartment of Engineering and Architecture, University of Trieste, Via A. Valerio 10, 34127 Trieste, Italy.ORCID 0000-0003-0121-5524
University of Trieste · IT

Funding

University of Trieste Interference modeling in ultra-dense 5G/6G networks
6 · The paper itself

Abstract

This paper proposes a theoretical model for evaluating the capacity of a millimeter wave (mmWave) source destination link when the nodes are distributed according to a three-dimensional (3D) homogeneous Poisson point process. In the presented analysis, different from the existing approaches, the destination lies in an arbitrary location with respect to the source; thus, the link performance can be evaluated for a neighbor of any order. Moreover, the developed model relies on a realistic propagation environment, characterized by path loss attenuation and shadowing in line of sight (LoS), non-LoS, and outage link state conditions. The derived formulas, which are calculated in closed-form and validated by independent Monte Carlo simulations, are used to investigate the influence of the intensity parameter, of the antenna gain, and of the mmWave frequency band on the link capacity for any possible neighbor in a practical 3D scenario.

Indexed as

3D analysislink capacitymillimeter wavesneighbor orderPoisson point process

Identifiers

PMID35336267
PMCPMC8949683
OpenAlexW4220891555

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.