MétaCan
Menu
Back to cohort
Record W2157394318 · doi:10.1109/ccnc.2007.99

Applications of Level Crossing Theory to Clipping Noise Characterization in Filtered OFDM Signals

2007· article· en· W2157394318 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 institutionsDalhousie University
Fundersnot available
KeywordsOrthogonal frequency-division multiplexingBasebandClipping (morphology)Gaussian noiseAdditive white Gaussian noiseElectronic engineeringRaised-cosine filterComputer scienceMathematicsTelecommunicationsAlgorithmWhite noiseBandwidth (computing)Channel (broadcasting)EngineeringLow-pass filterPrototype filter

Abstract

fetched live from OpenAlex

This paper applies the level crossing rate (LCR) and average fade duration (AFD) analysis to characterize clipping noise in filtered OFDM signals. Because orthogonal frequency division multiplexing (OFDM) signals exhibit complex Gaussian process behavior, well established results for the Rayleigh enve- lope of the correlated Gaussian process are used to derive the LCR and AFD statistics of the OFDM signal at the output of a square-root raised cosine shaping filter. The LCR and AFD information is then used to determine the statistics of the OFDM signal that passes through a soft-limiter, approximating at the baseband high power amplifier (HPA) memory-less nonlinearity. Simulation results corroborate theoretical derivations for the LCR and AFD statistics in real OFDM signals. The results obtained are important in predicting the clipping noise impact on the in-band and adjacent channel interference.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.683
Threshold uncertainty score0.386

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)0.0000.000

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 head0.026
GPT teacher head0.280
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

Quick stats

Citations1
Published2007
Admission routes1
Has abstractyes

Explore more

Same topicPAPR reduction in OFDMFrench-language works237,207