MétaCan
Menu
Back to cohort
Record W2125259057 · doi:10.1109/icc.2009.5199173

Mean Time to Loss of Lock and Average Switching Rate of an Automatic Frequency Control Loop with an Interferer and Noise in a Fading Channel

2009· article· en· W2125259057 on OpenAlexaff
Amin Emad, Norman C. Beaulieu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFadingRician fadingRayleigh fadingFading distributionNarrowbandChannel (broadcasting)Noise (video)Computer scienceControl theory (sociology)TelecommunicationsElectronic engineeringMathematicsEngineering

Abstract

fetched live from OpenAlex

The performance of an automatic frequency control (AFC) loop in a noisy fading channel when an interference signal is present at the input of the AFC is studied. Independent non-identically distributed (i.n.d.) channels with Rayleigh and Rician fading are considered. The received signals are assumed to be narrowband and linearly modulated while the analysis is applicable to the unmodulated scenario as well. Closed-form expressions and integral form formulas are derived for the mean time to loss of lock (MTLL) and the average switching rate (ASR) of an AFC. Numerical examples are provided to illustrate the effects of noise and slow fading on the performance of an AFC in the presence of an interferer. It is shown that in some scenarios, an AFC has a better performance if the desired signal is corrupted by more noise.

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)0.009
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
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 science0.0000.000
Research integrity0.0010.001
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 head0.013
GPT teacher head0.259
Teacher spread0.246 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

Explore more

Same topicWireless Communication Networks ResearchFrench-language works237,207