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Record W2243731809 · doi:10.1109/conecct.2015.7383866

Performance analysis of variable rate multicarrier transmission schemes over LMS channel

2015· article· en· W2243731809 on OpenAlexfundno aff
Akash Agarwal, Vijay Mukati, Preetam Kumar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSatellite Communication Systems
Canadian institutionsnot available
FundersMinistère de la Santé et des Services sociaux
KeywordsComputer scienceCode division multiple accessTransmission (telecommunications)Channel (broadcasting)Bit error rateComputer networkVariable (mathematics)Electronic engineeringMobile telephonyDiversity schemeFrequency-division multiple accessFadingTelecommunicationsOrthogonal frequency-division multiplexingMobile radioEngineeringMathematics

Abstract

fetched live from OpenAlex

With the increasing demand for increased coverage area, higher QoS, ubiquitous availability, flexibility and expandability, Land Mobile Satellite (LMS) multimedia communication is gaining popularity over existing Land Mobile Terrestrial (LMT) communication. This paper presents a comparative study of GO-OFDMA and VSL MC-CDMA variable rate transmission scheme over L and Ka-band LMS channel. For both the schemes, four variable rate classes employing 15 users are considered. It is shown that, for both the frequency bands, the BER performance of GO-OFDMA scheme is better than that of VSL MC-CDMA for all the different data rate class of users. Though, for Ka-band, the performance of both the schemes is relatively poor than L-band. Also, the performance of both the schemes for different elevation angles are illustrated and analyzed. Later, the composite signal PAPR performance of both the transmission schemes is shown and compared. It is observed that, the PAPR performance of GO-OFDMA scheme is better than VSL MC-CDMA. Hence GO-OFDMA scheme is a suitable candidate for variable rate communication over LMS channel.

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 score0.112
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.035
GPT teacher head0.250
Teacher spread0.215 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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