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Record W2104395501 · doi:10.1109/ccece.2004.1345333

Performance evaluation and total degradation of 16-QAM modulations over satellite channels

2004· article· en· W2104395501 on OpenAlexaff
H. AbdulHussein, Al-Asady, Mohamed Ibnkahla

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsQueen's University
Fundersnot available
KeywordsQAMQuadrature amplitude modulationConstellationCommunications satelliteNonlinear systemComputer scienceAmplifierTelecommunicationsStar (game theory)Constellation diagramSatellite constellationElectronic engineeringSatelliteTopology (electrical circuits)MathematicsControl theory (sociology)Bit error ratePhysicsEngineeringBandwidth (computing)Channel (broadcasting)Artificial intelligence

Abstract

fetched live from OpenAlex

The paper presents the total degradation (TD) performance of five 16-QAM constellations versus the input back off (IBO) of a high power amplifier (HPA) for a nonlinear satellite communication system. Results show that TD performance gives a set of convex curves and the star (4, 12) 16-QAM constellation has the lowest TD among the five 16-QAM constellations. The optimum IBO for each constellation is also calculated. Finally, the symbol error rate (SER) comparison of the five 16-QAM constellations is presented when they work in linear and nonlinear environments. For the nonlinear environment case, the HPA is forced to work with optimum IBO for each constellation. Results indicate that while star (5, 11) and rectangular 16-QAM are the best constellations in the linear case, star (4, 12) 16-QAM is the best one in the nonlinear case.

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)0.004
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.230
Teacher spread0.219 · 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

Citations13
Published2004
Admission routes1
Has abstractyes

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