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Record W1587367539 · doi:10.1109/vetec.1992.245452

Performance of pi /4-QDPSK in a Rayleigh/lognormal/delay spread/AWGN-cochannel interference environment

2003· article· en· W1587367539 on OpenAlexafffund
Minh-Tuan Le, A.U.H. Sheikh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAdditive white Gaussian noiseRayleigh fadingInterference (communication)Channel (broadcasting)Bit error rateDelay spreadFadingComputer scienceAlgorithmSignal-to-interference ratioMathematicsTopology (electrical circuits)TelecommunicationsPhysicsCombinatorics

Abstract

fetched live from OpenAlex

The performance of pi /4-QDPSK system is presented in a fast Rayleigh fading/slow lognormal shadowing/delay spread/AWGN channel in the presence of cochannel interference. The performance of the system is measured in terms of symbol error rate (SER). The system was simulated using two-ray model for normalized value of Doppler shift (0.001), degree of shadowing ( sigma =6), and several values of time delays and carrier-to-interference ratios. It is seen that the system performance is limited by the presence of cochannel interference. When cochannel interference is negligible, the system performance is dominated by the ratio between the main path signal and the delayed path signal, and the differential time delay between them. It is observed that the shadowing degrades the system performance at low CNR but not at high CNR (i.e., CNR>50 dB). The irreducible symbol error rate (ISER) does not vary significantly as the differential time delay between the two path varies.>

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)0.002
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
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 head0.011
GPT teacher head0.207
Teacher spread0.196 · 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

Citations0
Published2003
Admission routes2
Has abstractyes

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