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Record W2728954613 · doi:10.1109/lcomm.2017.2720583

Closed-Form BER Expressions of QPSK Constellation for Uplink Non-Orthogonal Multiple Access

2017· article· en· W2728954613 on OpenAlexaff
Xiaolu Wang, Fabrice Labeau, Lin Mei

Bibliographic record

VenueIEEE Communications Letters · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsNomaTelecommunications linkBit error ratePhase-shift keyingComputer scienceConstellationAdditive white Gaussian noiseExpression (computer science)Channel capacityChannel (broadcasting)Closed-form expressionAlgorithmMathematicsTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

Non-orthogonal multiple access (NOMA) is an attractive multiple access technique to achieve the optimal system capacity region. A great deal of recent attention has been devoted to the study of the NOMA system capacity performance. However, the exact bit error rate (BER) expressions of NOMA systems have not been derived yet. We provide the exact closed-form BER expressions of the QPSK constellation for an uplink NOMA system over an additive white Gaussian noise channel. Finally, the validity of our derived BER expressions is verified through simulations.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.004

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 head0.064
GPT teacher head0.333
Teacher spread0.269 · 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

Citations102
Published2017
Admission routes1
Has abstractyes

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