MétaCan
Menu
Back to cohort
Record W1673032502 · doi:10.1109/vetecs.2006.1683199

Peak to Average Power Ratio Properties of MC-CDMA and SM-CDMA

2006· article· en· W1673032502 on OpenAlexaff
Maryam Sabbaghian, D.D. Falconer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPAPR reduction in OFDM
Canadian institutionsCarleton University
Fundersnot available
KeywordsCode division multiple accessOrthogonal frequency-division multiplexingModulation (music)Electronic engineeringCDMA spectral efficiencyAmplifierComputer scienceNear-far problemTelecommunicationsPhysicsEngineeringChannel (broadcasting)AcousticsBandwidth (computing)

Abstract

fetched live from OpenAlex

This paper compares peak to average power ratio (PAPR) of multi carrier code division multiple access (MC-CDMA) and serial modulation-CDMA (SM-CDMA) with each other and with corresponding non-spread spectrum techniques which are orthogonal frequency division multiplexing (OFDM) and serial modulation (SM). In this comparison we consider the effect of two spreading types- binary and complex spreading, and equal-level and multi-level constellation on the amplitude distribution and out-of-band radiation. While MC-CDMA and OFDM have similar amplitude distribution, we show that the PAPR of SM-CDMA can be larger than that of SM. If the spreading is binary and the constellation is multi-level, this effect is so powerful that the PAPR of SM-CDMA becomes similar to that of MC-CDMA. Finally we use a modified selected mapping (SLM) algorithm to decrease the PAPR of SM-CDMA so that it would be equal to that of SM. Therefore SM-CDMA has lower PAPR if we use it together with modified SLM, and the system will need amplifiers with lower power back-off values

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)0.017
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.171
Teacher spread0.165 · 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

Citations9
Published2006
Admission routes1
Has abstractyes

Explore more

Same topicPAPR reduction in OFDMFrench-language works237,207