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Record W4290996627 · doi:10.1109/icc45855.2022.9838417

Millimeter Wave-based Fronthaul Network for Cell-free Massive MIMO

2022· article· en· W4290996627 on OpenAlexaff
Mohamed Ibrahim, Salah Elhoushy, Walaa Hamouda

Bibliographic record

VenueICC 2022 - IEEE International Conference on Communications · 2022
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsConcordia University
Fundersnot available
KeywordsTelecommunications linkMIMOComputer scienceBandwidth (computing)Computer networkExtremely high frequencyElectronic engineeringStochastic geometryEngineeringTelecommunicationsChannel (broadcasting)

Abstract

fetched live from OpenAlex

One of the major technological breakthroughs to support unprecedented demands of the future generations of wireless communication networks is cell-free (CF) massive multiple-input multiple-output (mMIMO). However, a fronthaul network with high and reliable capacity links is a prerequisite to realize the full potential of the CF mMIMO. Aiming at deploying a cost-efficient fronthaul network, this paper proposes a millimeter wave (mmWave)-based fronthaul network for the CF mMIMO system operation thanks to the broad bandwidth in the mmWave frequency band. Stochastic geometry tools have been exploited to reflect the impact of the considered fronthaul network on the uplink (UL) performance of CF mMIMO systems. Results reveal that increasing the blockage density deteriorates the average UL data rates, however, increasing the density of CPUs can limit the blockages effect on the system performance. Besides, while it is more preferable to deploy a large number of antennas per access points (AP)s under low blockage densities, having a large number of APs, provided with a small number of antennas leads to superior UL data rates at high blockage densities.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.954
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.110
GPT teacher head0.296
Teacher spread0.186 · 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.

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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

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