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Record W2064854919 · doi:10.1109/cce.2014.6916729

Hardware modelling of frequency recovery in an upstream demodulator for DOCSIS 3.0

2014· article· en· W2064854919 on OpenAlexafffund
Suresh Kalle, Francis M. Bui

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDigital Filter Design and Implementation
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Saskatchewan
KeywordsDemodulationFrequency offsetComputer scienceDigital signal processingMATLABUpstream (networking)Intersymbol interferenceOffset (computer science)Context (archaeology)Clock recoveryComputer hardwareElectronic engineeringOrthogonal frequency-division multiplexingJitterComputer networkTelecommunicationsEngineeringDecoding methodsChannel (broadcasting)

Abstract

fetched live from OpenAlex

For many communication systems, frequency offset is inevitable, since even minute differences between different oscillators are typically sufficient to cause this problem. The challenge is exacerbated by other non-ideal effects, including intersymbol interference (ISI). In this paper, the hardware modelling issues are considered for a frequency recovery scheme that has been previously shown to approach the Cramer-Rao Lower Bound (CRLB) and is immune to ISI-induced biasing. The associated design and implementation are pursued in the context of a DOCSIS 3.0 upstream demodulator. To investigate the correspondences, with issues and implications, between theoretical and practical hardware-level performances, the frequency recovery scheme is implemented in MATLAB and Altera DSP Builder, which offers hardware-oriented modelling. The obtained results and analysis, which are performed by comparing corresponding outputs from MATLAB and Altera DSP Builder, show good match and support the practical validity of the frequency recovery scheme for DOCSIS 3.0.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.820
Threshold uncertainty score0.241

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 communication0.0000.001
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 head0.060
GPT teacher head0.274
Teacher spread0.214 · 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
GenreMethods

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

Citations1
Published2014
Admission routes2
Has abstractyes

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