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Record W2025485250 · doi:10.1109/msp.2014.2335236

Three-Dimensional Beamforming: A new enabling technology for 5G wireless networks

2014· article· en· W2025485250 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 Signal Processing Magazine · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsQueen's University
Fundersnot available
KeywordsMobile broadbandGigabitComputer scienceComputer networkWireless networkCellular networkTelecommunicationsBeamformingWirelessIMT AdvancedMobile telephonyNext-generation networkSpectral efficiencyBroadbandWireless broadbandMobile computingMobile radioMobile technologyMobile WebThe InternetWorld Wide Web

Abstract

fetched live from OpenAlex

It is anticipated that the mobile data traffic will grow 1,000 times higher from 2010 to 2020 with a rate of roughly a factor of two per year. This increasing demand for data in next-generation mobile broadband networks will lead to many challenges for system engineers and service providers. To address these issues and meet the stringent demands in coming years, innovative and practical solutions should be identified that are able to provide higher spectral efficiency, better performance, and broader coverage. Next generations of wireless cellular networks, which are known as fifth generation (5G) or beyond fourth generation (B4G) wireless networks, are expected to produce higher data rates for mobile subscribers in the order of tens of gigabits per second (Gbit/s) and support a wide range of services. Despite the absence of official standards for the 5G, the data rate of 1 Gbit/s per user anywhere for 5G mobile networks is expected to be deployed beyond 2020.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.924
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.224
Teacher spread0.213 · 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