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Record W2142872918 · doi:10.1109/cwit.2011.5872110

Information rates of cyclostationary faster-than-nyquist signaling

2011· article· en· W2142872918 on OpenAlexaff
Yong Jin Daniel Kim, Jan Bajcsy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPAPR reduction in OFDM
Canadian institutionsMcGill University
Fundersnot available
KeywordsCyclostationary processNyquist–Shannon sampling theoremNyquist rateBandwidth (computing)Computer scienceAdditive white Gaussian noiseElectronic engineeringTelecommunicationsTransmission (telecommunications)Channel (broadcasting)Computer networkEngineeringSampling (signal processing)

Abstract

fetched live from OpenAlex

Faster than Nyquist (FTN) signaling has been studied as an alternative transmission technology in communication systems when information carrying symbols are sent faster than the Nyquist rate determined by the physical channel bandwidth. This paper presents analysis of the information rates of cyclostationary FTN signaling on AWGN and selected ISI channels. It also presents related numerical capacity results and discusses potential merits to the FTN signaling.

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)0.002
metaresearch head score (Gemma)0.012
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.222
Teacher spread0.201 · 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

Citations33
Published2011
Admission routes1
Has abstractyes

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