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Record W2143021806 · doi:10.1109/glocom.2005.1577887

Precise bit error probability analysis of DCT OFDM in the presence of carrier frequency offset on AWGN channels

2005· article· en· W2143021806 on OpenAlexaff
Peng Hui Tan, Norman C. Beaulieu

Bibliographic record

VenueGLOBECOM '05. IEEE Global Telecommunications Conference, 2005. · 2005
Typearticle
Languageen
FieldEngineering
TopicPAPR reduction in OFDM
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOrthogonal frequency-division multiplexingAdditive white Gaussian noisePhase-shift keyingDiscrete cosine transformCarrier frequency offsetQuadrature amplitude modulationComputer scienceBit error rateAlgorithmElectronic engineeringFrequency offsetQAMDiscrete Fourier transform (general)KeyingMathematicsTelecommunicationsWhite noiseFourier transformFractional Fourier transformDecoding methodsEngineeringChannel (broadcasting)Fourier analysis

Abstract

fetched live from OpenAlex

A precise method for calculating the bit error probability of a discrete cosine transform (DCT)-based orthogonal frequency-division multiplexing (OFDM) system on AWGN channels in the presence of frequency offset is derived. These accurate results are used to examine and compare the bit error probability performances of a DCT-OFDM system and the conventional discrete Fourier transform (DFT)-based OFDM system. Several signaling formats, such as binary phase shift keying (BPSK), quaternary phase shift keying (QPSK), and 16-ary quadrature amplitude modulation (16-QAM) are considered. Analysis and simulation results show that the DCT-OFDM system outperforms the DFT-OFDM system in the presence of carrier frequency offset.

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)0.010
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 score0.001
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.042
GPT teacher head0.296
Teacher spread0.254 · 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

Citations15
Published2005
Admission routes1
Has abstractyes

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Same venueGLOBECOM '05. IEEE Global Telecommunications Conference, 2005.Same topicPAPR reduction in OFDMFrench-language works237,207