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Record W2160193335 · doi:10.1109/pacrim.1997.620369

NCFSK error performance on slow fading channels in the presence of unsynchronized interferers

2002· article· en· W2160193335 on OpenAlexaff
Peter Han Joo Chong, Cyril Leung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBit error rateRayleigh fadingInterference (communication)SIGNAL (programming language)Channel (broadcasting)Modulation (music)Phase-shift keyingAdjacent-channel interferenceRayleigh scatteringPhysicsAlgorithmDoppler effectElectronic engineeringComputer scienceMathematicsFadingTelecommunicationsOpticsAcousticsEngineering

Abstract

fetched live from OpenAlex

An expression for the bit error rate (BER) of noncoherent frequency shift keying (NCFSK) modulation with a non-faded desired signal and a single Rayleigh-faded unsynchronized co-channel interferer (UCCI) is derived. This is used to obtain the BER, P/sub f/, for a Rayleigh-faded desired signal. It is found that P/sub f/ is not sensitive to the number, n/sub I/ of UCCI's. It is shown that the BER with UCCI is smaller than that with synchronized co-channel interference (SCCI). For a Rayleigh-faded desired signal, the difference is about 0.8 dB for large signal-to-interference ratios (SIR). Simulation is used to obtain the block error rate (BKER) with UCCI as a function of n/sub I/, number of errors corrected and Doppler frequency. It is found that the BKER with no error correction increases with n/sub I/. The BKER with UCCI is generally smaller than that with SCCI. With error correction, the improvement in BKER tends to be larger as n/sub I/ increases.

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)0.009
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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.032
GPT teacher head0.252
Teacher spread0.220 · 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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