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Record W2330545593 · doi:10.1109/lawp.2015.2487381

60-GHz Statistical Channel Characterization for Wireless Data Centers

2015· article· en· W2330545593 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Antennas and Wireless Propagation Letters · 2015
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsUniversité du Québec en Abitibi-TémiscamingueUniversity of OttawaUniversité du Québec en Outaouais
Fundersnot available
KeywordsPath lossExtremely high frequencyChannel (broadcasting)Interference (communication)Radio propagation modelLog-distance path loss modelLink budgetComputer scienceWirelessShadow mappingDelay spreadSoftware deploymentElectronic engineeringRadio propagationComputer networkFadingTelecommunicationsEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This letter presents 60-GHz radio channel measurements and characterization in a productivity data center. Path loss and delay spread are statistically modeled for possible use-cases corresponding to potential deployment scenarios in a wireless data center (WDC). Models behaviors are then used to highlight the propagation differences across use-cases. It was found that most use-cases are characterized by a rich scattering channel and sub-free-space path loss exponent values. Compared to common indoor environments, delay spread and path loss values suggest a better link budget at higher distances and possibly higher interference in dense deployment scenarios. The reported models and key propagation behaviors are useful for practical system design and evaluation of WDC millimeter-wave links.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.974
Threshold uncertainty score0.933

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.057
GPT teacher head0.257
Teacher spread0.200 · 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