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Record W2159660095 · doi:10.1109/glocom.2010.5683272

Discrete Time Faster-Than-Nyquist Signalling

2010· article· en· W2159660095 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPAPR reduction in OFDM
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsAdditive white Gaussian noisePhase-shift keyingComputer scienceNyquist–Shannon sampling theoremNyquist rateBit error rateElectronic engineeringData transmissionCommunications systemChannel (broadcasting)Modulation (music)Transmission (telecommunications)AlgorithmTelecommunicationsComputer networkSampling (signal processing)EngineeringDetector

Abstract

fetched live from OpenAlex

Increasing the symbol rate up to 25% faster than the Nyquist criteria for a digital communications system with QPSK modulation with an AWGN channel does not significantly increase the bit error rate or required transmission bandwidth. This so-called Faster-than-Nyquist (FTN) signalling has not been used in commercially deployed communications systems since the previously proposed implementation schemes required large receiver complexity. This paper introduces a reformulation of FTN signalling in terms of a non-square matrix multiplied by a sample vector of modulated QPSK symbols. It is shown that with this formulation the receiver complexity to detect the transmitted data for an AWGN channel is well within the complexity bounds for standard digital communication systems. This formulation enables an analysis of FTN signalling directly comparing it to standard higher order modulation and data coding techniques.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.005
GPT teacher head0.203
Teacher spread0.198 · 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

Quick stats

Citations33
Published2010
Admission routes1
Has abstractyes

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