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Record W2564272990 · doi:10.1109/pimrc.2016.7794954

Two-tier cellular communication systems with enhanced vehicular-based primary nodes

2016· article· en· W2564272990 on OpenAlexaff
Samer Henry, Ahmed Alsohaily, E.S. Sousa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMIMOComputer scienceCellular networkAntenna (radio)Transmission (telecommunications)Computer networkDistributed antenna system3G MIMOSmart antennaBase stationElectronic engineeringOmnidirectional antennaTelecommunicationsEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

Two-tier cellular communication systems introduce a primary-secondary connectivity structure that improves the performance of cellular systems by offsetting the limitations of mobile User Equipment (UE) and providing superior high performing links to system Access Points (APs). This paper considers two-tier cellular systems employing vehicular-based primary nodes, with focus on enhancing the performance of primary links connecting vehicular nodes with system APs. Current vehicle mounted antenna designs do not take advantage of the relaxed energy and spacing constraints confining mobile UE antennas, thus limiting vehicle antenna gains to the elimination of vehicle penetration losses only. The employment of widely-spaced vehicular antenna arrays is proposed in this paper to enable the exploitation of high-order Multiple-Input Multiple-Output (MIMO) transmission schemes. Detailed system-level simulations are employed to compare the performance of various vehicular antenna array configurations for a wide range of deployment scenarios. When compared to narrowly-spaced antenna arrays with low-order MIMO transmission schemes, widely-spaced vehicular antenna arrays utilizing high-order MIMO transmission schemes are shown to provide substantial system coverage and capacity gains.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.951
Threshold uncertainty score0.432

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 head0.005
GPT teacher head0.186
Teacher spread0.181 · 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
GenreMethods

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

Citations3
Published2016
Admission routes1
Has abstractyes

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