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Record W2522493423 · doi:10.1109/csndsp.2016.7573990

A time scheduling scheme in MIMO Y channels with reduced antenna configurations

2016· article· en· W2522493423 on OpenAlexaff
Zichao Zhou, Xueying Yuan, Jacek Ilow

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsDalhousie University
Fundersnot available
KeywordsRelayMIMOScheduling (production processes)Computer sciencePrecodingComputer networkMulti-user MIMOChannel (broadcasting)Scheme (mathematics)Topology (electrical circuits)EngineeringMathematicsElectrical engineeringMathematical optimization

Abstract

fetched live from OpenAlex

This paper develops a time scheduling scheme for generalized K-user multiple-input multiple-output (MIMO) Y channels where multiple terminals communicate with each other in a bi-directional manner via the relay. The objective is to decrease the system configuration requirements in terms of antenna numbers at the relay station and user nodes. Specifically, the scheme splits the user nodes into multiple groups and spreads the transmissions over a larger number of time slots (TSs) compared with the original MIMO Y channel scheme. While the latter scheme uses only one medium access control (MAC) and one broadcast (BC) phase, the proposed time scheduling arranges bi-directional data exchanges using specialized precoding in two or more MAC and BC phases between smaller number of users. As a result, the required numbers of antennas at the relay and the user nodes are decreased at the expense of reduced effective throughput. The lower complexity antenna configurations in MIMO Y channels will make the whole system more amenable to practical implementations.

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)0.000
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.035
GPT teacher head0.267
Teacher spread0.232 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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