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Record W2740056608 · doi:10.1109/mnet.2017.1600278

Non-Orthogonal Multiple Access Vehicular Small Cell Networks: Architecture and Solution

2017· article· en· W2740056608 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 Network · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceComputer networkArchitecture

Abstract

fetched live from OpenAlex

Recently, the concept of 5G vehicular smallcell networking (V-SCN) has been proposed to meet the growing demand of mobile data services in vehicular communications. However, it is a great challenge to explore spectrum and energy efficiency due to the fast vehicle mobility and varying communication environment. In this article, we focus on critical issues, such as interference management and handover, when employing 5G V-SCNs. In order to solve these issues, the network architecture embedded with NOMA is deigned. Furthermore, we propose a hierarchical power control solution to perform the joint optimization of cell association and power control to improve the spectrum and energy efficiency in NOMA-enabled 5G V-SCNs. Numerical comparison results provide some guidelines for developing NOMA-enabled 5G V-SCNs in an economical and highly energy-efficient manner.

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 score0.888
Threshold uncertainty score0.892

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 head0.012
GPT teacher head0.221
Teacher spread0.209 · 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