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Record W2013303820 · doi:10.1109/icc.2014.6884141

Location-aware coordinated multipoint transmission in OFDMA networks

2014· article· en· W2013303820 on OpenAlexafffund
Ahmed Hamdi Sakr, Hesham ElSawy, Ekram Hossain

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceBase stationTelecommunications linkBeamformingTransmission (telecommunications)Orthogonal frequency-division multiple accessEfficient energy useInterference (communication)MIMOSignal-to-noise ratio (imaging)Computer networkElectronic engineeringOrthogonal frequency-division multiplexingReal-time computingTelecommunicationsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

We propose a novel Location-Aware multicell Cooperation (LAC) scheme for downlink transmission in OFDMA-based networks. Compared to the traditional multicell cooperation, the proposed scheme uses coordinated multipoint (CoMP) transmission to serve only users with poor signal-to-interference-plus-noise ratio (SINR). On the other hand, users with good SINR conditions are served via multiuser MIMO by a single base station (BS). The proposed scheme uses a joint zero-forcing beamforming with semi-orthogonal user selection (ZFBF-SUS) transmission along with optimized power allocation in a semi-distributed manner to maximize the overall system energy efficiency (i.e., the average data rate per unit power [bps/Watt], or equivalently, average number of successfully transmitted bits per energy unit [bit/Joule]). Numerical results show that the proposed scheme outperforms the scheme that uses cooperation to serve all users, in terms of energy efficiency as well as system capacity and fairness.

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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.004
GPT teacher head0.193
Teacher spread0.188 · 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

Citations12
Published2014
Admission routes2
Has abstractyes

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