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Record W2547767260 · doi:10.5539/cis.v9n4p52

Simulating the Performance Characteristic of Passband Modulation Techniques in MATLAB Environment

2016· article· en· W2547767260 on OpenAlexvenueno aff
Saed Thuneibat

Bibliographic record

VenueComputer and Information Science · 2016
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceMATLABPassbandPhase-shift keyingModulation (music)MultiplexingCoding (social sciences)Bandwidth (computing)Electronic engineeringAlgorithmTelecommunicationsBit error rateMathematicsBand-pass filter

Abstract

fetched live from OpenAlex

Now days, digital communication systems become complex and sophisticated. Not all vendors, if any, can understand the system and components of system that represent different modulation techniques, line and block coding, multiplexing and multiple access. They need the conclusion about which of modulation techniques is the suitable for transmission and in the same time can save the power and bandwidth. Engineers can study and analyze the modulation techniques and then compare between them to give such conclusion using modeling and simulation. MATLAB is a high level mathematical language for technical computing. In this paper we use MATLAB environment as simulation software to give on display a clear result that used to compare between two digital Passband modulation techniques BPSK and QPSK, and pinpoint the performance of the two techniques over selected parameters.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.006
GPT teacher head0.179
Teacher spread0.173 · 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
Published2016
Admission routes1
Has abstractyes

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