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Record W3155775693

Two-Tier Networks: Beamforming at Secondary Node, applied to LTE

2018· dissertation· en· W3155775693 on OpenAlexfundaboutno aff
Jun Qian

Bibliographic record

VenueTSpace (University of Toronto) · 2018
Typedissertation
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsBeamformingBase stationCellular networkAntenna (radio)Computer networkComputer scienceNode (physics)Smart antennaEngineeringAntenna arrayElectronic engineeringReal-time computingTelecommunicationsDirectional antenna
DOInot available

Abstract

fetched live from OpenAlex

This thesis explores the gains and limitations associated with Two-Tier cellular communication systems comprising classical base stations, User Equipment (UE) and Secondary Nodes facilitating the connection of UE to base stations using beamforming antenna arrays. To investigate the merit of the proposed connectivity structure, data was collected from a live Long-Term Evolution (LTE) cellular network around the University of Toronto downtown campus. To facilitate data collection, a detailed framework was developed, implemented and refined using commercial hardware and software for collecting live data from cellular communication systems. By determining the Direction of Arrival (DoA) of received signals, the utilized antenna arrays were shown to provide substantial performance gains. Specifically, utilization of a 16-element antenna array was shown to increase the spectral efficiency range by 2.48 to 15.8 times or improving the coverage radius range by 1.11 to 1.85 times.

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)0.001
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: Other · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.005
GPT teacher head0.208
Teacher spread0.203 · 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
GenreOther

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

Citations0
Published2018
Admission routes2
Has abstractyes

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