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Record W2160518835 · doi:10.1109/glocom.2003.1258531

Performance Analysis of a Jointly Optimal BPSK Receiver in Cochannel Interference

2005· article· en· W2160518835 on OpenAlexaff
T.V. Poon, Norman C. Beaulieu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicBlind Source Separation Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPhase-shift keyingInterference (communication)Additive white Gaussian noiseComputer scienceSIGNAL (programming language)Signal-to-noise ratio (imaging)AlgorithmBinary numberBit error ratePhase (matter)KeyingSingle antenna interference cancellationControl theory (sociology)Electronic engineeringWhite noiseMathematicsTelecommunicationsPhysicsDecoding methodsEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

A jointly optimal detection scheme for a binary phase shift keying signal in the presence of a cochannel interferer and additive white Gaussian noise has been recently reported. The average error rate performance of this detection scheme was determined by extensive simulation. This paper develops an analytical solution for the average error rate performance of this jointly optimal detection scheme. An interesting characteristic of the solution is that it takes different analytical forms depending on the region of signal-to-interference ratio. These regions depend, in turn, on the phase difference between the desired signal and the interference signal. The analytical solution is exact and has excellent agreement with simulation results. These results show that this jointly optimal detection scheme can have good performance at both small and large values of signal-to-interference ratio.

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)0.003
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 score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.264
Teacher spread0.246 · 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

Citations8
Published2005
Admission routes1
Has abstractyes

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