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Record W1658833353 · doi:10.1109/pacrim.1993.407271

Performance of 16-QPOM signals on a fading mobile satellite channel

2002· article· en· W1658833353 on OpenAlexaff
S.B. Slimane, Tho Le‐Ngoc

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsConcordia University
Fundersnot available
KeywordsQuadrature amplitude modulationQAMQuadrature (astronomy)FadingComputer scienceRician fadingMaximum likelihood sequence estimationPhase-shift keyingAlgorithmChannel (broadcasting)Electronic engineeringDecoding methodsTelecommunicationsEstimation theoryBit error rateEngineering

Abstract

fetched live from OpenAlex

The authors present a maximum likelihood sequence estimation (MLSE) receiver structure for both linear and hard-limited 16-quadrature pulse overlapping modulation (QPOM) schemes. Using this structure, the performance of these schemes over shadowed Rician fading channels is studied. The structure of linear 16-QPOM schemes allowed the use of independent decoding for in-phase and quadrature components of the transmitted signal, thus reducing considerably the complexity of the MLSE receiver. The hard-limited 16-QPOM scheme used two hard-limited QPOM schemes in parallel. It is shown that an optimum MLSE receiver for this type of signal needs to consider both in-phase and quadrature components at the same time. Using the MLSE receiver, both schemes outperform conventional coherent 16-quadrature amplitude modulation (QAM).>

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)0.003
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.022
GPT teacher head0.235
Teacher spread0.212 · 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

Citations1
Published2002
Admission routes1
Has abstractyes

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