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Record W4243909654 · doi:10.1002/9781119333142.ch0

Introduction

2018· book-chapter· en· W4243909654 on OpenAlexaff
Anwer Al‐Dulaimi, Xianbin Wang, I Chih‐Lin

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsWestern UniversityExfo Electro-Optical Engineering (Canada)
Fundersnot available
KeywordsComputer scienceRadio access networkStandardizationComputer networkCore networkMIMOWireless networkCloud computingTelecommunicationsAccess technologyWirelessAccess networkBeamformingBase stationMobile stationOperating system

Abstract

fetched live from OpenAlex

The fifth generation (5G) mobile network is a new generation of wireless systems that is intended to connect users faster and more reliable than any previous generation. Mobile operators are adopting new radio access technologies that can provide service anywhere and through any interface of connectivity. The 5G networks will employ massive cloud storage to facilitate their computational operations. The 5G requirements reflect the end-user requirements and the upper band availability in both spectrum and technology. The continuous developments in the massive MIMO and beamforming techniques allow accessing the millimeter wave spectrum to support ultradense high-capacity scenarios. The chapter also presents an overview of this book. The book divides the 5G challenges into five parts to investigate: physical layer for 5G radio interface technologies, radio access technology for 5G networks, 5G network interworking and core network advancements, vertical 5G applications, and R&D and 5G standardization.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.598
Threshold uncertainty score0.852

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0060.004
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.4020.277

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 head0.007
GPT teacher head0.188
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
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

Citations3
Published2018
Admission routes1
Has abstractyes

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