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Record W2555593130 · doi:10.1109/access.2016.2631222

Position-Aided mm-Wave Beam Training Under NLOS Conditions

2016· article· en· W2555593130 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 Access · 2016
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsPosition (finance)Computer scienceRay tracing (physics)Antenna (radio)Non-line-of-sight propagationPower (physics)Beam (structure)AlgorithmDatabaseData miningSimulationArtificial intelligenceTelecommunicationsOpticsWirelessPhysics

Abstract

fetched live from OpenAlex

Ray tracing simulation results indicate that a high-resolution database is not needed to exploit user position knowledge in the 28-GHz band, even in the case of inexact information. A proposed antenna alignment algorithm (using maximum position errors and database resolutions of 10 and 4 m, respectively) that takes advantage of the propagation characteristics knowledge of database points located around the reported location is applied. The results show that the distance between the points can be increased up to 2 m with no considerable negative impact on performance. Simulations also indicate that this outcome is sustained when the maximum power level received at the user equipment varies. The algorithm provides the benefit of a higher initial power delivery and fewer steps, as long as the exact geographical position of the user is within the circular area containing the considered database points. The performance is similar to or better than that of a modified classical hierarchical procedure.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.559
Threshold uncertainty score0.589

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)0.0010.000

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 head0.092
GPT teacher head0.293
Teacher spread0.201 · 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