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Record W2166045751 · doi:10.1109/glocom.2006.115

CTH14-5: Probability Density Functions of Logarithmic Likelihood Ratios in Phase Shift Keying BICM

2006· article· en· W2166045751 on OpenAlexaff
Leszek Szczeciński, Mustapha Benjillali

Bibliographic record

VenueGlobecom · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsPhase-shift keyingAlgorithmComputer scienceDemodulationKeyingBinary numberChannel (broadcasting)Modulation (music)Probabilistic logicEncoderBit error rateTransmission (telecommunications)Probability density functionLogarithmDecoding methodsAmplitude and phase-shift keyingElectronic engineeringMathematicsTelecommunicationsStatisticsArithmeticEngineeringPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

Bit-interleaved coded modulation (BICM) is a coded-modulation scheme where the output of the channel encoder and the input of the modulator are separated by a bit- level interleaver. From the decoder's point of view, the modulator, the transmission channel, and the demodulator (calculating bits' reliability metrics) become a memoryless BICM channel with binary inputs and real outputs. The BICM channel's outputs (reliability metrics) are known to be Gaussian for binary-or quaternary phase shift keying but no expression for their probability density function (PDF) is known for higher-order modulation. We fill this gap presenting closed-form expressions for PDF of reliability metrics in BICM based on M-PSK with gray mapping. Such probabilistic description of BICM channel is necessary to calculate the channel's information-theoretic parameters such as capacity, or cut-off rate but it is also a tool required to analyze the performance of coded transmission.

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)0.004
metaresearch head score (Gemma)0.022
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.011
GPT teacher head0.246
Teacher spread0.235 · 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

Citations7
Published2006
Admission routes1
Has abstractyes

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Same venueGlobecomSame topicAdvanced Wireless Communication TechniquesFrench-language works237,207