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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.295

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 head0.006
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

Quick stats

Citations9
Published2006
Admission routes1
Has abstractyes

Explore more

Same topicPAPR reduction in OFDMFrench-language works237,207