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Record W2133050515 · doi:10.1109/ccece.2014.6900914

Efficient M-PSK lock detectors and SNR estimators

2014· article· en· W2133050515 on OpenAlexaff
Yair Linn

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsTRIUMFUniversity of British Columbia
Fundersnot available
KeywordsDetectorPhase-shift keyingLock (firearm)Computer sciencePhase detectorEstimatorKeyingElectronic engineeringDetection theorySignal-to-noise ratio (imaging)Channel (broadcasting)AlgorithmBit error rateElectrical engineeringMathematicsEngineeringTelecommunicationsStatistics

Abstract

fetched live from OpenAlex

We present new structures for carrier lock detection for M-PSK (M-ary Phase Shift Keying) receivers. The lock detectors are NDA (Non Data Aided) and are shown to have the following advantages: (1) they have a compact fixed-point hardware implementation suitable for implementation in ASICs (Application Specific Integrated Circuits); (2) they require only 1 sample/symbol; (3) relatively few symbol intervals are needed in order to achieve good detection and false alarm probabilities; and (4) the decision thresholds for the lock detectors are simple to calculate and are resilient to imperfections in the AGC (Automatic Gain Control) circuit. It is shown that the lock detection metric can be used to estimate the channel SNR (Signal to Noise Ratio). Theoretical, simulation, and laboratory results are presented.

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 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.520

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 head0.004
GPT teacher head0.167
Teacher spread0.163 · 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 teacher head, 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

Citations3
Published2014
Admission routes1
Has abstractyes

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