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Record W2186667778 · doi:10.1109/pimrc.2015.7343374

Dummy users; Network-aware cooperative terminals in wireless access networks

2015· article· en· W2186667778 on OpenAlexaff
Hadi Baligh, Keyvan Zarifi, Jianglei Ma, Peiying Zhu, Amine Maaref, Mohamed Salem

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsHuawei Technologies (Canada)
Fundersnot available
KeywordsComputer scienceComputer networkHotspot (geology)ScalabilityTerminal (telecommunication)Wireless networkLeverage (statistics)Cellular networkSoftware deploymentVirtualizationWirelessComputer securityTelecommunicationsCloud computingDatabaseOperating system

Abstract

fetched live from OpenAlex

Cooperation among network nodes at one side and terminal nodes on the other side are the two main enablers of radio access virtualization to establish scalable and dynamically adjustable data pipes to the users in the 5G mobile networks. User cooperation requires careful design to address cooperation incentive, battery life, user discovery, security and privacy. Dummy user, a network-aware terminal, can facilitate the implementation of user cooperation and leverage its benefits. Dummy users can offer a low cost alternative for the enhancement of the network performance to address persistent or temporary demand such as a hotspot created by an outdoor festival. Proper deployment of dummy users provides fairness and ubiquity of the network while enabling many advanced transmission and reception schemes that require more accurate channel state knowledge at the network.

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 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.274
Teacher spread0.246 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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