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Record W2535468383 · doi:10.1109/pimrc.2014.7136164

A novel dual-slope mm-Wave channel model based on 3D ray-tracing in urban environments

2014· article· en· W2535468383 on OpenAlexaboutno aff
Y.B. Chang, Sangkyu Baek, Sooyoung Hur, Youngjoong Mok, YoungJu Lee

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsPath lossRay tracing (physics)Extremely high frequencyDelay spreadComputer scienceChannel (broadcasting)Log-distance path loss modelBandwidth (computing)FadingAcousticsTelecommunicationsElectronic engineeringOpticsPhysicsWirelessEngineering

Abstract

fetched live from OpenAlex

To solve mobile traffic crunch, the usage of enormous bandwidth in millimeter wave (mm-Wave) is under discussion. In this paper, we investigate radio channel characteristics of mm-Wave frequency in the downtown area of Ottawa using 3D ray-tracing technique. In the results, important parameters of the radio channel model, such as path loss exponent, shadow fading, delay spread and angle spread, are provided. Especially, in case of path loss model in non-line of sight, a novel dual-slope approach is proposed for two conventional deployment scenarios. Comparing to traditional single-slope path loss model, the proposed method has smaller RMS errors in terms of local mean of path loss observations. We believe that the proposed method is appropriate to evaluate performance of the mm-Wave system in dense urban environments.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.202
Teacher spread0.177 · 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 source (direct Gemma or distilled Codex), 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

Citations35
Published2014
Admission routes1
Has abstractyes

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