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Record W3023634272 · doi:10.1109/lcomm.2020.2993533

A Joint CoMP C-NOMA for Enhanced Cellular System Performance

2020· article· en· W3023634272 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Communications Letters · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNomaComputer scienceInterference (communication)Base stationEnhanced Data Rates for GSM EvolutionSingle antenna interference cancellationComputer networkCellular networkDecoding methodsTelecommunications linkExpression (computer science)Transmission (telecommunications)Ergodic theoryTelecommunicationsChannel (broadcasting)Mathematics

Abstract

fetched live from OpenAlex

The inter-cell interference (ICI) and the intra-user interference in non-orthogonal multiple access (NOMA) cellular networks have serious impacts on the performance of cell edge users. In this letter, we investigate the integration of coordinated multipoint (CoMP) transmission and cooperative NOMA (C-NOMA) aiming to improve the performance of cell edge users. Using this framework, we exploit the cooperation between base stations (BSs) to mitigate the ICI and the successive decoding of users that are near the BSs to further enhance the performance of cell edge users. In this setting, we derive a closed form expression for the outage probability of a cell edge user along with an analytical expression for its ergodic rate. We validate the derived expressions through various Monte-Carlo simulations, where we show the superiority of the proposed framework compared with other multiple access schemes proposed in the literature.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.717
Threshold uncertainty score0.873

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 science0.0020.000
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 head0.045
GPT teacher head0.225
Teacher spread0.180 · 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