MétaCan
Menu
Back to cohort
Record W4238795354 · doi:10.1109/icce.2003.1218933

A channel characterization technique using frequency-domain pilot time-domain correlation method for DVB-T systems

2003· article· en· W4238795354 on OpenAlexaff
Xianbin Wang, Yiyan Wu, B. Caron, B. Ledoux, S. Lafleche

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTelecommunications and Broadcasting Technologies
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsFrequency domainTime domainChannel (broadcasting)Computer sciencePilot signalImpulse responseSIGNAL (programming language)AlgorithmImpulse (physics)CorrelationElectronic engineeringTelecommunicationsMathematicsEngineeringPhysics

Abstract

fetched live from OpenAlex

A new channel characterization technique using a frequency-domain pilot time-domain correlation (FPTC) method is proposed for DVB-T systems. This new technique is based on the time-domain correlation between the received signal and the pilot sequence embedded in the DVB-T signal, which is derived from the frequency domain pilots and known to the channel characterization equipment. Compared with other channel estimation techniques, the major advantage of this proposal is its simple implementation. Channel impulse response can be estimated without timing recovery. The impact of the non-perfect carrier recovery is very small since the correlation is only computed on a short period of the received signal. The proposed method has been verified through numerical simulations and field tests. The possible improvement of the estimation accuracy is indicated.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.257
Teacher spread0.229 · 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 designBench or experimental
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

Citations3
Published2003
Admission routes1
Has abstractyes

Explore more

Same topicTelecommunications and Broadcasting TechnologiesFrench-language works237,207