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Record W2240342643

Opportunistic CoMP for 5G massive MIMO Multilayer Networks

2015· article· en· W2240342643 on OpenAlexaboutno aff
Wolfgang Zirwas

Bibliographic record

VenueInternational ITG Workshop on Smart Antennas · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsMacroComputer scienceMIMOSpectral efficiencySmall cellBooster (rocketry)Radio spectrumRadio frequencyBase stationComputer networkElectronic engineeringTelecommunicationsEngineeringChannel (broadcasting)
DOInot available

Abstract

fetched live from OpenAlex

In the FP7 project METIS we investigate suitable combinations of massive MIMO, joint transmission coordinated multipoint and small cells for future 5G systems. Challenging is a tight integration over small and macro layers. Straight forward is to ensure orthogonality between small and macro cell layers by allocation to different RF frequency bands like e.g. 2.6 and 3.5GHz respectively. Here within the macro layer frequency band an opportunistic tight cooperation between macro and small cell radio stations is being proposed. This requires dual band small cell radio stations operating in a traditional local area frequency band like 3.5GHz for data off loading and simultaneously - and on a need basis - at e.g. 2.6GHz as performance booster for macro UEs. First high level evaluations indicate significant spectral efficiency, capacity and coverage gains for macro cell users benefiting from higher Rx power and rank enhancement.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.052
GPT teacher head0.291
Teacher spread0.238 · 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
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

Citations12
Published2015
Admission routes1
Has abstractyes

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