MétaCan
Menu
Back to cohort
Record W2889125649 · doi:10.1109/ccece.2018.8447629

A Performance Evaluation of Millimeter-Wave Cellular Networks with User Mobility

2018· article· en· W2889125649 on OpenAlexaff
Shamma Nikhat, Mustafa Mehmet-Ali

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer scienceBlocking (statistics)HandoverBase stationComputer networkCellular networkExtremely high frequencyOverhead (engineering)Mobility modelStochastic geometryMobile telephonyPath lossUser equipmentElectronic engineeringReal-time computingTelecommunicationsMobile radioEngineeringWirelessMathematics

Abstract

fetched live from OpenAlex

Millimeter-Wave (mmWave) communications may be the key technology for the realization of 5G networks. MmWave communications have significantly different propagation characteristics than microwave (μWave) frequencies. The recent studies have determined the performance of cellular mmWave networks using stochastic geometry technique assuming a stationary user. The stationary user model does not capture correlation in the blocking of the links as the user moves on. In this work, we have determined the performance seen by a mobile user traveling over a path at constant and varying speeds. We have obtained the cumulative information received by the user as a function of its path length for different blocking intensities and cell sizes. The results show that while the received information rate does not vary significantly with mobility, the average path length that the mobile user is associated with a base station without interruption drops down sharply with increasing blocking intensity. This will cause in high handover rate, which will result in high overhead. This work demonstrates the significance of the user mobility on the performance of cellular mmWave networks.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.409
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.227
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2018
Admission routes1
Has abstractyes

Explore more

Same topicMillimeter-Wave Propagation and ModelingFrench-language works237,207