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Record W2558955488 · doi:10.1109/lcomm.2016.2635146

Virtual Small Cells Formation in 5G Networks

2016· article· en· W2558955488 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 Communications Letters · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsWestern University
Fundersnot available
KeywordsMacrocellBase stationComputer scienceComputer networkSmall cellVirtual networkCellular networkMobile telephonyMobile radio

Abstract

fetched live from OpenAlex

A novel virtual small cells formation approach for the fifth-generation mobile network is proposed in which a subset of qualified users is selected to serve as the base stations of virtual small cells in connecting other users to the macrocell base stations. In the proposed approach, the optimum virtual small cell density is obtained, so that the number of required communication links to the macrocell base stations for connecting all users directly or through small cells is minimized. It is shown that by using the proposed virtual small cell technique, the number of connections to the macrocell base stations becomes proportional to the logarithm of the user density. Since this relationship is linear in conventional cellular networks, utilization of virtual small cells can significantly increase the overall network capacity. In addition, the optimum virtual small cell density is derived for cases where the communications costs for a user-to-macrocell direct link and a small cell-to-macrocell trunk link are not the same.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.963
Threshold uncertainty score0.382

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 head0.018
GPT teacher head0.215
Teacher spread0.198 · 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