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Record W2541090731 · doi:10.1109/ants.2013.6802833

Estimation of coverage areas in microcells

2013· article· en· W2541090731 on OpenAlexaff
Diego Castro-Hernandez, Raman Paranjape

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsTransmitterMicrocellPath lossComputer scienceAlgorithmRadio propagationPath (computing)Shadow mappingThresholdingHeuristicLog-distance path loss modelStatisticsMathematicsTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

The estimation of the coverage area of a single transmitter in a microcell environment with irregular distribution and elevation of buildings is investigated in this paper. Two methods were applied to compute the coverage area of five locations of a test transmitter. The first method corresponded to a simple thresholding of the estimated path loss values provided by a deterministic propagation prediction model based on physical optics and the Geometrical Theory of Diffraction (GTD). We propose a second method to compute the coverage area based on a set of heuristic rules that combines the estimated values of the propagation path loss, information from the environment as well as data from physical measurements. We performed extensive signal strength measurements in order to evaluate the accuracy of the estimation of the coverage area. According to our results, computing the coverage area by applying a threshold to the predicted path loss values leads to significant errors, in average 20% of the map was erroneously included in the coverage area of each test location of the transmitter. We were able to reduce this error to 4% with our proposed method based on heuristic rules.

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 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 score0.438
Threshold uncertainty score0.441

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 communication0.0000.000
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 head0.009
GPT teacher head0.195
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.

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

Citations4
Published2013
Admission routes1
Has abstractyes

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