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Record W1834923645 · doi:10.1109/glocom.1995.502689

Optimum digital modulation for nonlinear FDMA uplink and a regenerative satellite on-board processor

2002· article· en· W1834923645 on OpenAlexaff
Rehan Majeed, Weiguo Chen, P.J. McLane, K. Lazaris-Brunner

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPAPR reduction in OFDM
Canadian institutionsCOM DEV InternationalQueen's University
Fundersnot available
KeywordsTelecommunications linkPhase-shift keyingElectronic engineeringRepeater (horology)Communications satelliteComputer scienceDemodulationModulation (music)Bit error rateTelecommunicationsEngineeringSatelliteEncoding (memory)

Abstract

fetched live from OpenAlex

A number of linear, digital modulation techniques are evaluated for FDMA access to a regenerative, satellite repeater. The new aspect of the research is in considering a regenerative rather than an amplifying satellite transponder. QPSK, OQPSK, both with Nyquist pulse shaping, and MSK are the modulation techniques considered. A nonlinear, high power amplifier (HPA) is included in the user terminal for the FDMA uplink access to the regenerative satellite repeater. The multi-carrier demodulation algorithm on-board the satellite is based on the transmultiplexer algorithm as all uplink signals are assumed to have a fixed data rate. The performance criteria used in the study are power efficiency, bandwidth efficiency, and the complexity of the transmultiplexer algorithm for on-board processing, all of which vary relative to the modulation technique considered. If the HPA is linear we find that QPSK is the best modulation. When the HPA is nonlinear, however, it is the worst technique and OQPSK is best. The performance analysis is based on a full computer simulation of 8 uplink carriers being processed by the transmultiplexer algorithm. The downlink would be based on TDM access but is not considered in the paper.

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 score0.472
Threshold uncertainty score0.453

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 head0.021
GPT teacher head0.228
Teacher spread0.207 · 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
Published2002
Admission routes1
Has abstractyes

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